The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually. In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages. In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff. In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts. In stage 3, Cooccurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification. Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features. To prove the efficiency of the proposed method, it is tested on different stone texture image databases. The proposed method resulted in high classification rate when compared with the other existing methods.
COMPOSITE TEXTURE SHAPE CLASSIFICATION BASED ON MORPHOLOGICAL SKELETON AND RE...sipij
After several decades of research, the development of an effective feature extraction method for texture
classification is still an ongoing effort. Therefore , several techniques have been proposed to resolve such
problems. In this paper a novel composite texture classification method based on innovative pre-processing
techniques, skeletonization and Regional moments (RM) is proposed. This proposed texture classification
approach, takes into account the ambiguity brought in by noise and the different caption and digitization
processes. To offer better classification rate, innovative pre-processing methods are applied on various
texture images first. Pre-processing mechanisms describe various methods of converting a grey level image
into binary image with minimal consideration of the noise model. Then shape features are evaluated using
RM on the proposed Morphological Skeleton (MS) method by suitable numerical characterization
measures for a precise classification. This texture classification study using MS and RM has given a good
performance. Good classification result is achieved from a single region moment RM10 while others failed
in classification.
This document discusses various techniques for analyzing the texture of images. It begins by classifying texture analysis into three categories: pixel-based, local feature-based, and region-based. Pixel-based techniques use grey level co-occurrence matrices and histograms. Local feature-based techniques use edges and generalized co-occurrence matrices. Region-based techniques use region growing and topographic models. The document then provides an overview of statistical, model-based, geometrical, and signal processing texture analysis methods. It notes that the grey level co-occurrence matrix introduced by Haralick et al. became a standard due to natural textures being difficult to analyze with autocorrelation alone.
Presented by Adrien Depeursinge, PhD, at MICCAI 2015 Tutorial on Biomedical Texture Analysis (BTA), Munich, Oct 5 2015.
Texture-based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively, and repeatedly across multiple time points. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics (imagenomics and radiogenomics). Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; localization uncertainty of texture operators; validation; and, translation to routine clinical applications.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
SEGMENTATION USING ‘NEW’ TEXTURE FEATUREacijjournal
This document summarizes a research paper that proposes a new texture feature descriptor called "NEW" for image segmentation. The NEW descriptor labels neighboring pixels and forms eight-component binary vectors to represent texture. Fuzzy c-means clustering is then used to segment images into regions based on texture. Experimental results on texture images from the Brodatz dataset show the NEW descriptor can successfully segment images into the correct number of texture regions. Accuracy, precision, and recall metrics are used to evaluate the segmentation performance.
Segmentation of medical images using metric topology – a region growing approachIjrdt Journal
A metric topological approach to the region growing based segmentation is presented in this article. Region based growing techniques has gained a significant importance in the medical image processing field for finest of segregation of tumor detected part in the image. Conventional algorithms were concentrated on segmentation at the coarser level which failed to produce enough evidence for the validity of the algorithm. In this article a novel technique is proposed based on metric topological neighbourhood also with the introduction of new objective measure entropy, apart from the traditional validity measures of Accuracy, PSNR and MSE. This measure is introduced to prove the amount of information lost after segmentation is reduced to greater extent which elucidates the effectiveness of the algorithm. This algorithm is tested on the well known benchmarking of testing in ground truth images in par with the proposed region based growing segmented images. The results validated show the validation of effectiveness of the algorithm.
Change Detection of Water-Body in Synthetic Aperture Radar ImagesCSCJournals
Change detection is the art of quantifying the changes in the Synthetic Aperture Radar (SAR) images that have happened over a period of time. Remote sensing has been the parental technique to perform change detection analysis. This paper empirically investigates the impact of applying the combination of texture features for different classification techniques to separate water body from non-water body. At first, the images are classified using unsupervised Principle Component Analysis (PCA) based K-means clustering for dimension reduction. Then the texture features like Energy, Entropy, Contrast , Inverse Differential Moment , Directional Moment and the Median are extracted using Gray Level Co-occurrence Matrix (GLCM) and these features are utilized in Linear Vector Quantization (LVQ) and Support Vector Machine (SVM) classifiers. This paper aims to apply a combination of the texture features in order to significantly improve the accuracy of detection. The utility of detection analysis, influences management and policy decision making for long-term construction projects by predicting the preventable losses.
Texture Unit based Approach to Discriminate Manmade Scenes from Natural Scenesidescitation
This document summarizes a research paper that proposes a method to discriminate between natural and manmade scenes using texture analysis. It analyzes local texture information in images using a "texture unit matrix" approach. Texture units characterize the texture of a pixel and its neighbors. Texture unit matrices are generated from images and used to form feature vectors. A self-organizing map (SOM) classifier is then used to classify images as natural or manmade based on these feature vectors. The researchers tested their method on databases of "near" scenes within 10 meters and "far" scenes about 500 meters away. Their results found that analyzing the minimum texture unit matrix in a base-5 approach provided the most accurate classifications between natural and manmade scenes
COMPOSITE TEXTURE SHAPE CLASSIFICATION BASED ON MORPHOLOGICAL SKELETON AND RE...sipij
After several decades of research, the development of an effective feature extraction method for texture
classification is still an ongoing effort. Therefore , several techniques have been proposed to resolve such
problems. In this paper a novel composite texture classification method based on innovative pre-processing
techniques, skeletonization and Regional moments (RM) is proposed. This proposed texture classification
approach, takes into account the ambiguity brought in by noise and the different caption and digitization
processes. To offer better classification rate, innovative pre-processing methods are applied on various
texture images first. Pre-processing mechanisms describe various methods of converting a grey level image
into binary image with minimal consideration of the noise model. Then shape features are evaluated using
RM on the proposed Morphological Skeleton (MS) method by suitable numerical characterization
measures for a precise classification. This texture classification study using MS and RM has given a good
performance. Good classification result is achieved from a single region moment RM10 while others failed
in classification.
This document discusses various techniques for analyzing the texture of images. It begins by classifying texture analysis into three categories: pixel-based, local feature-based, and region-based. Pixel-based techniques use grey level co-occurrence matrices and histograms. Local feature-based techniques use edges and generalized co-occurrence matrices. Region-based techniques use region growing and topographic models. The document then provides an overview of statistical, model-based, geometrical, and signal processing texture analysis methods. It notes that the grey level co-occurrence matrix introduced by Haralick et al. became a standard due to natural textures being difficult to analyze with autocorrelation alone.
Presented by Adrien Depeursinge, PhD, at MICCAI 2015 Tutorial on Biomedical Texture Analysis (BTA), Munich, Oct 5 2015.
Texture-based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively, and repeatedly across multiple time points. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics (imagenomics and radiogenomics). Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; localization uncertainty of texture operators; validation; and, translation to routine clinical applications.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
SEGMENTATION USING ‘NEW’ TEXTURE FEATUREacijjournal
This document summarizes a research paper that proposes a new texture feature descriptor called "NEW" for image segmentation. The NEW descriptor labels neighboring pixels and forms eight-component binary vectors to represent texture. Fuzzy c-means clustering is then used to segment images into regions based on texture. Experimental results on texture images from the Brodatz dataset show the NEW descriptor can successfully segment images into the correct number of texture regions. Accuracy, precision, and recall metrics are used to evaluate the segmentation performance.
Segmentation of medical images using metric topology – a region growing approachIjrdt Journal
A metric topological approach to the region growing based segmentation is presented in this article. Region based growing techniques has gained a significant importance in the medical image processing field for finest of segregation of tumor detected part in the image. Conventional algorithms were concentrated on segmentation at the coarser level which failed to produce enough evidence for the validity of the algorithm. In this article a novel technique is proposed based on metric topological neighbourhood also with the introduction of new objective measure entropy, apart from the traditional validity measures of Accuracy, PSNR and MSE. This measure is introduced to prove the amount of information lost after segmentation is reduced to greater extent which elucidates the effectiveness of the algorithm. This algorithm is tested on the well known benchmarking of testing in ground truth images in par with the proposed region based growing segmented images. The results validated show the validation of effectiveness of the algorithm.
Change Detection of Water-Body in Synthetic Aperture Radar ImagesCSCJournals
Change detection is the art of quantifying the changes in the Synthetic Aperture Radar (SAR) images that have happened over a period of time. Remote sensing has been the parental technique to perform change detection analysis. This paper empirically investigates the impact of applying the combination of texture features for different classification techniques to separate water body from non-water body. At first, the images are classified using unsupervised Principle Component Analysis (PCA) based K-means clustering for dimension reduction. Then the texture features like Energy, Entropy, Contrast , Inverse Differential Moment , Directional Moment and the Median are extracted using Gray Level Co-occurrence Matrix (GLCM) and these features are utilized in Linear Vector Quantization (LVQ) and Support Vector Machine (SVM) classifiers. This paper aims to apply a combination of the texture features in order to significantly improve the accuracy of detection. The utility of detection analysis, influences management and policy decision making for long-term construction projects by predicting the preventable losses.
Texture Unit based Approach to Discriminate Manmade Scenes from Natural Scenesidescitation
This document summarizes a research paper that proposes a method to discriminate between natural and manmade scenes using texture analysis. It analyzes local texture information in images using a "texture unit matrix" approach. Texture units characterize the texture of a pixel and its neighbors. Texture unit matrices are generated from images and used to form feature vectors. A self-organizing map (SOM) classifier is then used to classify images as natural or manmade based on these feature vectors. The researchers tested their method on databases of "near" scenes within 10 meters and "far" scenes about 500 meters away. Their results found that analyzing the minimum texture unit matrix in a base-5 approach provided the most accurate classifications between natural and manmade scenes
This document proposes a new method for segmenting outdoor images called Color Cluster Elimination (CCE) which utilizes color clustering and texture analysis. CCE performs color clustering in a multi-resolution pyramid to gradually eliminate larger color clusters, preventing them from dominating segmentation and allowing smaller clusters to emerge more clearly. It then examines regions for adjacent homochromatic objects with different textures, introducing Texture Sewn Response (TSR) to indicate texture strength across resolutions/directions. The method is evaluated on the BSDS500 dataset against other metrics, demonstrating satisfactory performance for outdoor scene segmentation.
This document discusses texture analysis in image processing. It defines texture as the spatial arrangement of color or intensities in an image that can help with image segmentation and classification. There are two main approaches to texture analysis: structural, which looks at regular patterns of texels, and statistical, which analyzes relationships between pixel intensities using methods like edge detection, co-occurrence matrices, and histograms. Statistical texture analysis captures the degrees of randomness and regularity in textures through metrics calculated from pixel intensity distributions and relationships.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
Local Phase Oriented Structure Tensor To Segment Texture Images With Intensit...CSCJournals
This paper proposed the active contour based texture image segmentation scheme using the linear structure tensor and tensor oriented steerable Quadrature filter. Linear Structure tensor (LST) is a popular method for the unsupervised texture image segmentation where LST contains only horizontal and vertical orientation information but lake in other orientation information and also in the image intensity information on which active contour is dependent. Therefore in this paper, LST is modified by adding intensity information from tensor oriented structure tensor to enhance the orientation information. In the proposed model, these phases oriented features are utilized as an external force in the region based active contour model (ACM) to segment the texture images having intensity inhomogeneity and noisy images. To validate the results of the proposed model, quantitative analysis is also shown in terms of accuracy using a Berkeley image database.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
A Novel Method for Detection of Architectural Distortion in MammogramIDES Editor
Among various breast abnormalities architectural
distortion is the most difficult type of tumor to detect. When
area of interest is medical image data, the major concern is to
develop methodologies which are faster in computation and
relatively noise free in processing. This paper is an extension
of our own work where we propose a hybrid methodology that
combines a Gabor filtration with directional filters over the
directional spectrum for digitized mammogram processing.
The most commendable thing in comparison to other
approaches is that complexity has been lowered as well as the
computation time has also been reduced to a large extent. On
the MIAS database we achieved a sensitivity of 89 %.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A combined method of fractal and glcm features for mri and ct scan images cla...sipij
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale
complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of
irregularity of the medical images. This descriptor property does not give ownership of the local image
structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM
Features. This powerful combination has proved good results especially in classification of medical texture
from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical
diagnostics tests for osteoporosis pathologies.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
1) The document discusses image segmentation in satellite images using optimal texture measures. It evaluates four texture measures from the gray-level co-occurrence matrix (GLCM) with six different window sizes.
2) Principal Component Analysis (PCA) is applied to reduce the texture measures to a manageable size while retaining discrimination information.
3) The methodology consists of selecting an optimal window size and optimal texture measure. A 7x7 window size provided superior performance for classification. PCA is used to analyze correlations between texture measures and window sizes.
The document summarizes an investigation comparing experimental and computational modeling results of grain deformation in a cast nickel superalloy. Digital image correlation was used to experimentally measure strain distributions across a tensile sample, finding heterogeneous strain localized in certain grains. A crystal plasticity finite element model was developed to simulate the deformation using orientations from EBSD maps. The model showed improved agreement with experimental strain distributions at lower values of the strain rate sensitivity parameter m, but still some disagreement on strain localization. Including subsurface grains in the model further improved agreement, showing the importance of full 3D microstructure representation.
A Combined Method with automatic parameter optimization for Multi-class Image...AM Publications
Multi-class image semantic segmentation deals with many applications in consumer electronics
fields such as image editing and image retrieval. Segmentation is done by combining the top down and bottomup
segmentation. Top-Down Process can be done by Semantic Texton Forest and bottom up- process using
JSEG. These two segmentation process can be executed in a combined manner. But this cannot choose the
optimal value of JSEG parameter for each interested semantic category. Hence an automatic parameter selection
algorithm has been proposed. An automatic parameter selection technique called an automatic multilevel
thresholding algorithm using stratified sampling and PSO is used to remedy the limitations.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
A novel approach for georeferenced data analysis using hard clustering algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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This document provides a survey of various image segmentation techniques used in image processing. It begins with an introduction to image segmentation and its importance in fields like pattern recognition and medical imaging. It then categorizes and describes different segmentation approaches like edge-based, threshold-based, region-based, etc. The literature survey section summarizes several papers on specific segmentation algorithms or applications. It concludes with a table comparing the advantages and disadvantages of different segmentation techniques. The overall document aims to provide an overview of segmentation methods and their uses in computer vision.
Wavelet based histogram method for classification of textuIAEME Publication
This document summarizes a research paper that proposes a new method called Wavelet based Histogram on Texton Patterns (WHTP) for classifying textures. The method applies a discrete wavelet transform to texture images and extracts texton frequencies from the approximation and detail subbands at different scales. It calculates texton frequencies for original images and wavelet-transformed images. Combining these texton frequencies improves classification success rates when distinguishing between various types of stone textures. The paper aims to improve on other texture classification methods by incorporating spatial information using textons in the wavelet domain. An experimental evaluation finds the proposed WHTP method achieves more accurate classification of stone textures compared to other approaches.
Stone texture classification and discrimination by edge direction movementeSAT Journals
Abstract
Texture discrimination is the rich field in the area pattern recognition and pattern analysis. The texture classification is the one of
the major field in texture discrimination. In this paper derive an approach for texture group classification based on the direction
movement. The edge movements are identified in each 3×3 window of the texture image. Based on the edge direction movements
the texture images are categorized. Two texture groups used in this paper. Texture group 1 consists of Bark, Sand, Raffia and
Pigskin images and Straw, Bsand, Wgrain and Grass image are treated as texture group2. In this paper, Horizontal, Vertical
direction and also Right, Left Diagonal Edge direction movements are identified.
Key Words: Edge Direction movements, texture classification, pattern recognition, texture group
A Review of Recent Texture Classification: MethodsIOSR Journals
This document reviews recent trends in texture classification methods. It summarizes that signal processing feature extraction methods like Gabor filters and wavelets have become popular due to providing higher accuracy, though older methods like gray level co-occurrence matrices are still used. For classification, nearest neighbor algorithms remain common due to simplicity, while support vector machines have increased in usage. Brodatz texture datasets are most frequently employed despite limitations, while other datasets see less use. In general, research emphasizes accuracy over speed, utilizing more complex feature extraction and classification algorithms.
Texture Segmentation Based on Multifractal Dimension ijsc
This document presents a new texture segmentation algorithm based on multifractal dimension. The algorithm divides an image into blocks and extracts feature vectors for each block using box counting method on multiple thresholds of the image. A supervised learning phase is used to classify blocks based on these feature vectors by extracting mean and standard deviation values for sample windows labeled by an expert. The algorithm was tested on multi-texture images by extracting feature vectors for each small block and classifying them based on the trained classifier.
This document proposes a new method for segmenting outdoor images called Color Cluster Elimination (CCE) which utilizes color clustering and texture analysis. CCE performs color clustering in a multi-resolution pyramid to gradually eliminate larger color clusters, preventing them from dominating segmentation and allowing smaller clusters to emerge more clearly. It then examines regions for adjacent homochromatic objects with different textures, introducing Texture Sewn Response (TSR) to indicate texture strength across resolutions/directions. The method is evaluated on the BSDS500 dataset against other metrics, demonstrating satisfactory performance for outdoor scene segmentation.
This document discusses texture analysis in image processing. It defines texture as the spatial arrangement of color or intensities in an image that can help with image segmentation and classification. There are two main approaches to texture analysis: structural, which looks at regular patterns of texels, and statistical, which analyzes relationships between pixel intensities using methods like edge detection, co-occurrence matrices, and histograms. Statistical texture analysis captures the degrees of randomness and regularity in textures through metrics calculated from pixel intensity distributions and relationships.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
Local Phase Oriented Structure Tensor To Segment Texture Images With Intensit...CSCJournals
This paper proposed the active contour based texture image segmentation scheme using the linear structure tensor and tensor oriented steerable Quadrature filter. Linear Structure tensor (LST) is a popular method for the unsupervised texture image segmentation where LST contains only horizontal and vertical orientation information but lake in other orientation information and also in the image intensity information on which active contour is dependent. Therefore in this paper, LST is modified by adding intensity information from tensor oriented structure tensor to enhance the orientation information. In the proposed model, these phases oriented features are utilized as an external force in the region based active contour model (ACM) to segment the texture images having intensity inhomogeneity and noisy images. To validate the results of the proposed model, quantitative analysis is also shown in terms of accuracy using a Berkeley image database.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
A Novel Method for Detection of Architectural Distortion in MammogramIDES Editor
Among various breast abnormalities architectural
distortion is the most difficult type of tumor to detect. When
area of interest is medical image data, the major concern is to
develop methodologies which are faster in computation and
relatively noise free in processing. This paper is an extension
of our own work where we propose a hybrid methodology that
combines a Gabor filtration with directional filters over the
directional spectrum for digitized mammogram processing.
The most commendable thing in comparison to other
approaches is that complexity has been lowered as well as the
computation time has also been reduced to a large extent. On
the MIAS database we achieved a sensitivity of 89 %.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A combined method of fractal and glcm features for mri and ct scan images cla...sipij
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale
complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of
irregularity of the medical images. This descriptor property does not give ownership of the local image
structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM
Features. This powerful combination has proved good results especially in classification of medical texture
from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical
diagnostics tests for osteoporosis pathologies.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
1) The document discusses image segmentation in satellite images using optimal texture measures. It evaluates four texture measures from the gray-level co-occurrence matrix (GLCM) with six different window sizes.
2) Principal Component Analysis (PCA) is applied to reduce the texture measures to a manageable size while retaining discrimination information.
3) The methodology consists of selecting an optimal window size and optimal texture measure. A 7x7 window size provided superior performance for classification. PCA is used to analyze correlations between texture measures and window sizes.
The document summarizes an investigation comparing experimental and computational modeling results of grain deformation in a cast nickel superalloy. Digital image correlation was used to experimentally measure strain distributions across a tensile sample, finding heterogeneous strain localized in certain grains. A crystal plasticity finite element model was developed to simulate the deformation using orientations from EBSD maps. The model showed improved agreement with experimental strain distributions at lower values of the strain rate sensitivity parameter m, but still some disagreement on strain localization. Including subsurface grains in the model further improved agreement, showing the importance of full 3D microstructure representation.
A Combined Method with automatic parameter optimization for Multi-class Image...AM Publications
Multi-class image semantic segmentation deals with many applications in consumer electronics
fields such as image editing and image retrieval. Segmentation is done by combining the top down and bottomup
segmentation. Top-Down Process can be done by Semantic Texton Forest and bottom up- process using
JSEG. These two segmentation process can be executed in a combined manner. But this cannot choose the
optimal value of JSEG parameter for each interested semantic category. Hence an automatic parameter selection
algorithm has been proposed. An automatic parameter selection technique called an automatic multilevel
thresholding algorithm using stratified sampling and PSO is used to remedy the limitations.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
A novel approach for georeferenced data analysis using hard clustering algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
This document provides a survey of various image segmentation techniques used in image processing. It begins with an introduction to image segmentation and its importance in fields like pattern recognition and medical imaging. It then categorizes and describes different segmentation approaches like edge-based, threshold-based, region-based, etc. The literature survey section summarizes several papers on specific segmentation algorithms or applications. It concludes with a table comparing the advantages and disadvantages of different segmentation techniques. The overall document aims to provide an overview of segmentation methods and their uses in computer vision.
Wavelet based histogram method for classification of textuIAEME Publication
This document summarizes a research paper that proposes a new method called Wavelet based Histogram on Texton Patterns (WHTP) for classifying textures. The method applies a discrete wavelet transform to texture images and extracts texton frequencies from the approximation and detail subbands at different scales. It calculates texton frequencies for original images and wavelet-transformed images. Combining these texton frequencies improves classification success rates when distinguishing between various types of stone textures. The paper aims to improve on other texture classification methods by incorporating spatial information using textons in the wavelet domain. An experimental evaluation finds the proposed WHTP method achieves more accurate classification of stone textures compared to other approaches.
Stone texture classification and discrimination by edge direction movementeSAT Journals
Abstract
Texture discrimination is the rich field in the area pattern recognition and pattern analysis. The texture classification is the one of
the major field in texture discrimination. In this paper derive an approach for texture group classification based on the direction
movement. The edge movements are identified in each 3×3 window of the texture image. Based on the edge direction movements
the texture images are categorized. Two texture groups used in this paper. Texture group 1 consists of Bark, Sand, Raffia and
Pigskin images and Straw, Bsand, Wgrain and Grass image are treated as texture group2. In this paper, Horizontal, Vertical
direction and also Right, Left Diagonal Edge direction movements are identified.
Key Words: Edge Direction movements, texture classification, pattern recognition, texture group
A Review of Recent Texture Classification: MethodsIOSR Journals
This document reviews recent trends in texture classification methods. It summarizes that signal processing feature extraction methods like Gabor filters and wavelets have become popular due to providing higher accuracy, though older methods like gray level co-occurrence matrices are still used. For classification, nearest neighbor algorithms remain common due to simplicity, while support vector machines have increased in usage. Brodatz texture datasets are most frequently employed despite limitations, while other datasets see less use. In general, research emphasizes accuracy over speed, utilizing more complex feature extraction and classification algorithms.
Texture Segmentation Based on Multifractal Dimension ijsc
This document presents a new texture segmentation algorithm based on multifractal dimension. The algorithm divides an image into blocks and extracts feature vectors for each block using box counting method on multiple thresholds of the image. A supervised learning phase is used to classify blocks based on these feature vectors by extracting mean and standard deviation values for sample windows labeled by an expert. The algorithm was tested on multi-texture images by extracting feature vectors for each small block and classifying them based on the trained classifier.
Feature integration for image information retrieval using image mining techni...iaemedu
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
Massive Regional Texture Extraction for Aerial and Natural ImagesIOSR Journals
The document presents a proposed method called Massive Regional Texture Extraction (MRTE) for segmenting natural and aerial images. The MRTE method uses local thresholding and seeded region growing to extract textured regions from images. It maintains a lookup table to control pixel homogeneity during region growth. The algorithm provides less user interaction while achieving sharp demarcation of edges and intensity levels. Experimental results on natural and aerial image datasets show MRTE increases segmented homogeneous regions by 40-50% and pixels in segmented images by 50-60% compared to existing seeded growing methods. The proposed method effectively segments images into precise homogeneous regions for applications like content-based image retrieval.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A novel predicate for active region merging in automatic image segmentationeSAT Journals
Abstract Image segmentation is an elementary task in computer vision and image processing. This paper deals with the automatic image segmentation in a region merging method. Two essential problems in a region merging algorithm: order of merging and the stopping criterion. These two problems are solved by a novel predicate which is described by the sequential probability ratio test and the minimal cost criterion. In this paper we propose an Active Region merging algorithm which utilizes the information acquired from perceiving edges in color images in L*a*b* color space. By means of color gradient recognition method, pixels with no edges are clustered and considered alone to recognize some preliminary portion of the input image. The color information along with a region growth map consisting of completely grown regions are used to perform an Active region merging method to combine regions with similar characteristics. Experiments on real natural images are performed to demonstrate the performance of the proposed Active region merging method. Index Terms: Adaptive threshold generation, CIE L*a*b* color gradient, region merging, Sequential Probability Ratio Test (SPRT).
Importance of Mean Shift in Remote Sensing SegmentationIOSR Journals
1) Mean shift is a non-parametric clustering technique that can segment remote sensing images into homogeneous regions without prior knowledge of the number of clusters or constraints on cluster shape.
2) The document presents a case study demonstrating mean shift can segment an image containing oil storage tanks into distinct regions faster than level set segmentation.
3) Mean shift is shown to be well-suited for remote sensing image segmentation tasks like forest mapping and land cover classification due to its ability to handle noise, gradients, and texture variations common in real-world images.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a new framework for color image segmentation using a combination of watershed and seed region growing algorithms. It begins with an introduction to image segmentation and discusses challenges with traditional gray-scale methods when applied to color images. The document then proposes a method using automatic seed region growing integrated with the watershed algorithm. Experimental results on an input image are shown to demonstrate the segmentation process and output images. The framework is concluded to improve upon traditional gray-scale methods for segmenting the richer information in color images.
Content Based Image Retrieval Using Dominant Color and Texture FeaturesIJMTST Journal
The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Content Based Image Retrieval (CBIR) is the retrieval of images based on features such as color and texture. Image retrieval using color feature cannot provide good solution for accuracy and efficiency. The most important features are Color and texture. In this paper technique used for retrieving the images based on their content namely dominant color, texture and combination of both color and texture. The technique verifies the superiority of image retrieval using multi feature than the single feature.
A Survey on Image Segmentation and its Applications in Image Processing IJEEE
As technology grows day by day computer vision becomes a vital field of understanding the behavior of an image. Image segmentation is a sub field of computer vision that deals with the partition of objects into number of segments. Image segmentation found a huge application in pattern reorganization, texture analysis as well as in medial image processing. This paper focus on distinct sort of image segmentation techniques that are utilized in computer vision. Thus a survey has been created for various image segmentation techniques that describe the importance of the same. Comparison and conclusion has been created within the finish of this paper.
This document summarizes and analyzes image segmentation and edge detection techniques for medical images. It discusses several current segmentation methods like histogram-based, edge detection, region growing, level set, and graph partitioning methods. The document then proposes a new active contour model for image segmentation that uses both edge and region information to segment images with undefined boundaries. It also discusses solving computational difficulties of models using level set theory. In conclusion, the proposed segmentation algorithms are shown to outperform some well-known methods in accuracy and processing speed.
Validation Study of Dimensionality Reduction Impact on Breast Cancer Classifi...ijcsit
A fundamental problem in machine learning is identifying the most representative subset of features from
which we can construct a predictive model for a classification task. This paper aims to present a validation
study of dimensionality reduction effect on the classification accuracy of mammographic images. The
studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear
embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high
rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the
classification rate is about 95%. It was also found that the classification rate increases with the size of the
reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained
results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dun
index. The measurement of these indices confirms that the optimal value of reduced space dimension is
d=60.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
International Journal of Computational Engineering Research(IJCER) ijceronline
This document presents a hybrid methodology for classifying segmented images using both unsupervised and supervised classification techniques. The proposed methodology involves first segmenting the image into spectrally homogeneous regions using region growing segmentation. Then, a clustering algorithm is applied to the segmented regions for initial classification. Selected regions are used as training data for a supervised classification algorithm to further categorize the image. The hybrid approach combines the benefits of unsupervised clustering and supervised classification. The methodology is evaluated on natural and aerial images to compare its performance to existing seeded region growing and texture extraction segmentation methods.
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
Texture Images Classification using Secant Lines Segments Histogramijtsrd
Texture classification is the process to classify different textures from the given images. The aim of texture classification is to classify the category of a texture image. To design an effective algorithm for texture classification, it is essential to find a set of texture features with good discriminating power. This paper presents a texture classification system using secant lines segments histogram and Euclidean Distance. Secant lines segments histogram is used to generate the features from texture images as a histogram. These features offer a better discriminating strategy for texture classification. These features are first used for training and later on for classifying the texture images. Euclidean Distance is used for distinguishing each of the known categories for classification. Ei Phyu Win | Mie Mie Tin | Pyae Phyo Thu "Texture Images Classification using Secant Lines Segments Histogram" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27984.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/27984/texture-images-classification-using-secant-lines-segments-histogram/ei-phyu-win
Similar to A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
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image, co-occurrence matrix is widely used. From the co-event lattice set of textural elements separated and
these components are broadly used to remove textural data from advanced pictures [4][5]. In basic approach,
surface is considered as reiteration of a few primitives. For surface grouping and portrayal, these strategies
have been connected by a few creators and made progress to a specific degree [6].
So many approaches are available in the literature for texture classification. The first and top most
approach is Local Binary Pattern (LBP) approach [7][8]. But LBP approach has some disadvantages. If the
central pixel value changes by 1, the LBP value drastically changes. Other existing approaches are based on
wavelet transform [9][10], statistical learning from morphological image processing [11], long linear patterns
[12][13], edge direction movements [14], excluding Complex Patterns [15] and preprocessed images [16].
Texture pictures are characterized by utilizing different wavelet transforms using statistical parameters [17]
and primitive parameters.
Recently, Juan Wang et.al [18] proposed a method for texture classification using Scattering
Statistical and Co-occurrence Features. Wang developed new approach for texture features extraction. This
approach used scattering transform for scattering statistical features and scattering co-occurrence features
extraction which are derived from sub-bands of the scattering decomposition and original images and these
features are used for classification. This approach got reasonable percentage rate of classification but the time
complexity is more.
Siva Kumar et.al [19] proposed a method for stone texture classification based on edge direction
movement. In this approach, edge movements are identified on each 3×3 sub-image and based on the edge
direction movements, the texture images are classified. This approach mainly classifies the texture image into
two groups only and each group consists of 4 different types of texture images. Ratna Bhargavi et al [20]
proposed an approach for detection of Lesion using texture features and Xiaorong Xue et.al [21] proposed an
approach for Classification of Fully Polari metric SAR Images based on Polari metric Features and
Spatial Features.
Vijay Kumar et.al [22] proposed a method for classifying the stone textures into four categories
based on occurrence of T-pattern count which are overlapped 5 bit T-patterns on each 5×5 sub-image. The
classification rate of this approach is about 96.16%. In Vijay Kumar’s work, standard classification
algorithms are not used for classifying the stone texture group. Standard classification systems consume more
time for extraction of the features from stone image and also for classification.
The existing standard classification approaches, both classification of stone textures and extraction
of the features from stone image consume more time. Other existing approaches in literature, even proposed
algorithms for classifying the stone texture group. Their classification results are not compared with standard
classification algorithms to verify the accuracy. If correct features are extracted then they fit for both standard
classification and also for user defined algorithm. So, the present work concentrates on developing a method
called DDRGRM for classifying the stone textures into four groups.
Till now majority of the existing techniques extract features from the entire image. The proposed
DDRGRM strategy is to decrease the stone image dimensionality into (2N/5×2M/5) and applies fuzzy
concept for lessening the dim level range for viable and proficient stone surface grouping. Another
fundamental issue in classification of texture and recognition is texture characterization from derived
features. Many of the existing approaches have the drawback of computational complexity as they include
processing of entire image with large range of gray levels for texture classification and recognition.
To address this, the present paper proposes an approach in which the image dimension and dim level range
are decreased with no loss of surface component data.
The main objective of the proposed method is to be compatible with both the approaches i.e. for
user defined algorithm and also for standard classification algorithms. The proposed method does not use any
standard classification algorithms for classifying the stone texture group. The rest of the paper is organized as
follows. Section 2 describes the proposed method. Derived user defined algorithm and Results are explained
in section 3. Finally, conclusions are given in section 4.
2. PROPOSED METHOD
For portraying the attributes of the neighborhood example of the surface by utilizing surface
descriptor strategies, for example, Local Binary Pattern (LBP),Texture Unit (TU) and Textons. The surface
descriptors are valuable for surface examination and critical grouping and it gives both factual and auxiliary
qualities of a surface. These descriptors are totally nearby and generally characterized on a 3×3
neighborhood. The proposed technique display takes a 5×5 neighborhood, and reduces it into a 2×2
neighborhood without loss of any surface data and further it diminishes the dim level range utilizing
fluffy rationale.
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The proposed DDRGRM model mainly consists of 6 steps. In step 1, convert the RGB stone texture
image into Gray level image using Weighted RGB conversion method. Formation of nine overlapped sub
3×3 sub images from a 5×5 sub image is performed in step 2. In step 3 Derivation of “Local Difference
Matrix (LDM)” on the nine overlapped 3×3 sub images and generate the reduced matrix. Further reduce the
3×3 sub image into 2×2 sub image without losing the texture image information in step 4. Step 5, reduce the
gray level range in each 2×2 sub image using fuzzy concept and generate the Fuzzy reduced co-occurrence
matrix, in step 6, extract the CM features for classification. The block diagram of the proposed model is
shown in Figure 1.
Use Thresholding
Figure 1. Block diagram of DDRGRM Model
2.1. Convert RGB to Gray level image:
To extract the features the RGB image will be transformed to Gray image using Weighted RGB
conversion. As the RGB image is formed by 3 commanded hues i.e. Red, Green and Blue, in Weighted RGB
conversion diverse weights are assigned to each shading segment and these three segments are used for
converting the RGB image to gray image. The transformation procedure is specified in equation 1.
𝐺𝑟𝑎𝑦(𝑥, 𝑦) = 0.3 ∗ 𝑅(𝑥, 𝑦) + 0.59 ∗ 𝐺(𝑥, 𝑦) + 0.11 ∗ 𝐵(𝑥, 𝑦) (1)
Where R, G, B are the Red, Green, Blue color component values, (x,y) are the pixel positions and
Gray(x,y) represents the gray value at the given pixel position (x,y). The RGB image and resultant gray
image after conversion are shown in Figure 2.
(a) (b)
Figure 2. Marble stone image (a) Color image (b) Resultant Gray level image
2.2. Formation of 9 overlapped 3×3 sub images from a 5×5 sub image:
The 5×5 sub image consists of 25 pixels represented by {V1, V2, …., V13, ...V25}, where V13
represents the gray value of the innermost center pixel and remaining are the neighboring pixel intensity
values as shown in Figure 3. Figure 4 represents overlapped 3×3 sub windows referred as {w1, w2, w3,…
w9} extracted from the 5X5 sub image represented in Figure 3.
Convert RGB to
Gray level image
Formation of 9 3×3
sub images
Extract 3 CM
Features
Derive the User defined
Algorithm
Estimate Stone
texture Group
Generate
LDM
Decrease the 3×3 sub
image into 2×2 sub
image
Reduce the gray
level range
Input Stone
image
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A Novel Approach Based on Decreased Dimension and Reduced Gray… (G S N Murthy)
2505
V1 V2 V3 V4 V5
V6 V7 V8 V9 V10
V11 V12 V13 V14 V15
V16 V17 V18 V19 V20
V21 V22 V23 V24 V25
Figure 3. Representation of a 5×5 sub image
V1 V2 V3 V2 V3 V4 V3 V4 V5
V6 V7 V8 V7 V8 V9 V8 V9 V10
V11 V12 V13 V12 V13 V14 V13 V14 V15
w1 w2 w3
V6 V7 V8 V7 V8 V9 V8 V9 V10
V11 V12 V13 V12 V13 V14 V13 V14 V15
V16 V17 V18 V17 V18 V19 V18 V19 V20
w4 w5 w6
V11 V12 V13 V12 V13 V14 V13 V14 V15
V16 V17 V18 V17 V18 V19 V18 V19 V20
V21 V22 V23 V22 V23 V24 V23 V24 V25
w7 w8 w9
Figure 4. Formation of overlapped 3×3 neighborhoods {w1, w2, w3,…, w9} from Figure 3
2.3. Derivation of LDM on each 3×3 overlapped window of 5×5 sub image:
In this step, LDM is figured for every one of the nine 3×3 covered windows {w1, w2, w3,… , w9}
of 5×5 sub picture. The LDM gives a productive portrayal of surface picture. The LDM on each wi is the
outright contrast between the neighboring pixel and the dark estimation of the focal pixel which is evaluated
using equation 2 and represented in Figure 5. This results in nine new 3×3 LDMs represented as {LDM1,
LDM2, LDM3,…, LDM9} for each overlapped window {w1, w2…. w9}.
LDMi = abs (vi - vc) for i = 1,2,...9 (2)
Where vc is the centre pixel and vi represent the neighboring pixel values of the overlapped 3×3
neighborhood. Basing on equation 2 the resultant value of each LDM in which the central pixel value is
always zero.
│V1-V7│ │V2-V7│ │V3-V7│
│V6-V7│ │V7-V7│ │V8-V7│
│V11-V7│ │V12-V7│ │V13-V7│
Figure 5. Generation of LDM1 from w1.
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2.4. Generation of Decrease Dimension Matrix (DDM) of 5×5 into 3×3 window:
In this each value of DDM is evaluated from each of the nine LDM’s generated in the previous step
in two stages: generation of Mean LDM in the first step and then generate DDM. In stage one, the mean of
the 9 windows which are generated in previous step by using the equation 3 are found. The generated values
forms a matrix is called Mean LDM (MLDM). The MLDM is a 3×3 window with nine elements (MLDP1 to
MLDP9). The MLDM preserves the local region possessions including edge information.
MLDPi = meanof (LDMi ) for i = 1,2,…9 (3)
Further, generate the DDM by calculating the local difference between the neighboring pixel values
and central pixel value of the MLDP matrix and is represented by equation 4.
DDMPi = abs (MLDPi – MLDPc ) for MLDPi = 1,2,…9 (4)
The Equation 4 reveals that continuously dominant pixel value of the 3×3 DDM is zero.
2.5. Generation of Reduced Dimension Matrix (RDM) of 2×2 window from DDM:
The generation process of RDM marix is shown in figure 6. The DDM window comprises of nine
qualities which is created in previous step as shown in figure 6(a). In this progression, the DDM of a 3×3
neighborhood is lessened into a 2×2 RDM by utilizing Triangular Shape Primitives (TSP). The proposed TSP
is an associated neighborhood of three pixels on a 3×3 DDM, without focal pixel. The TSP's on DDM
doesn’t consider focal pixel as its dark level is constantly zero. The normal of these TSP's creates pixel
estimations of Reduced Dimension Matrix (RDM) of measure 2×2 as appeared in Figure 6(b) based on
equations 5 to 8. By this the proposed technique decreases the texture image of size N×M into the size
(2N/5) × (2M/5).
RDMP1 = (DDMP1+ DDMP2+DDMP4) / 3 (5)
RDMP2 = (DDMP2+ DDMP3+DDMP6) / 3 (6)
RDMP3 = (DDMP4+ DDMP7+DDMP8) / 3 (7)
RDMP4 = (DDMP6+ DDMP8+DDMP9) / 3 (8)
DDMP1 DDMP2 DDMP 3
DDMP 4 DDMP 5 DDMP6 RDMP1 RDMP2
DDMP 7 DDMP 8 DDMP 9 RDMP3 RDMP4
(a) (b)
Figure 6. Generation process of a RDM of size 2×2 from a 3×3 DDM neighborhood.
a) The DDM neighborhood b) RDM.
2.6. Reduction of gray level range in RDM using fuzzy logic:
Fuzzy rationale has certain real focal points over conventional Boolean rationale with regards to
certifiable applications, for example, surface portrayal of genuine pictures. To deal precisely with the areas of
regular pictures even within the sight of clamor and the diverse procedures of subtitle and digitization fluffy
rationale is presented on DDM. The proposed fluffy rationale converts DDM dark levels into 5 levels ranging
from 0 to 4. The resultant framework is called Decrease Dimension Reducing Gray level Range Matrix
(DDRGRM). In LBP double examples are assessed by contrasting the neighboring pixels and focal pixel.
The proposed DDRGRM model is determined by looking at the every pixel of the 2×2 DDM with the normal
pixel estimations of the DDM. The DDRGRM portrayal is appeared in Figure 7. The accompanying
Equations 9 is utilized to decide the components of DDRGRM model.
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Figure 7. Fuzzy representation of DDRGRM model of the image
0 ifRDMPi< V0 and RDMPi< x
1 ifRDMPi< V0 and RDMPi≥ x
DDRGRMPi= 2 ifRDMPi= V0 for i = 1, 2, 3, 4 (9)
3 ifRDMPi> V0 and RDMPi >y
4 ifRDMPi> V0 and RDMPi ≤ y
Where x, y are the user-specified values and V0 =
(∑ TSPi
𝟒
𝐢=𝟏 )
𝟒
(10)
For example, the process of evaluating DDRGRM model from a sub RDM image of 2×2 is shown in
Figure 8. The Figure 8 (a) represents RDM and figure 8(b) represents the rsultent fuzzy matrix from RDM.
In this study, x and y values are chosen as V0/2 and 3V0 /2 respectively.
28 39 1 2
61 9 4 0
(a) (b)
Figure 8. The process of evaluating DDRGRM model from sub RDM (a) RDM (b) DDRGRM model
2.7. Computation of CM features on the derived DDRGRM model:
The present approach determined Gray Level Co-occurrence Matrix (GLCM) on the DDRGRM
model of the stone texture image. GLCM is proposed by Haralick to characterize the image based on how
certain dark levels happen in comparison with other dim levels. GLCM can gauge the surface of the picture
since co-event frameworks are generally vast and scanty. GLCM is considered to be a benchmark for
extracting Haralick features like angular second moment, contrast, correlation, variance, inverse difference
moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy,
information measures of correlation and maximal correlation coefficient, etc.. These elements have been
broadly utilized as a part of the investigation, grouping and elucidation of picture information. Its point is to
portray the stochastic properties of the spatial conveyance of dark levels in an image. Out of these proposed
Haralick features the proposed approach used three Haralick highlights i.e. Correlation (CR), Cluster
Prominence (CP) and Information measure of correlation1 (IMC1) for classification of stone texturesinto 4
different groups. For characterization of stone textures into 4 unique groups equations (11) to (13) are used.
The DDRGRM method with GLCM consolidates the benefits of both statistical and structural information of
the stone texture image.
𝑐𝑜𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 = ∑ ∑
(𝑖∗𝑗)∗𝐶𝑂𝑀(𝑖,𝑗)− (𝜇 𝑥∗𝜇 𝑦)
𝜎 𝑥∗𝜎 𝑦
𝑛
𝑗=1
𝑚
𝑖=1 (11)
Where 𝜇 𝑥, 𝜇 𝑦 and 𝜎𝑥, 𝜎 𝑦 are the mean and standard deviations of probability matrix GLCM along row wise x
and column wise y
𝐶𝑃 = ∑ ∑ (𝑖 + 𝑗 − 𝜇 𝑥 − 𝜇 𝑦 )4𝑛
𝑗=1
𝑚
𝑖=1 ∗ 𝐶𝑂𝑀(𝑖, 𝑗) (12)
𝐼𝑀𝐶1 = ∑ ∑
𝑙𝑜𝑔(𝑖∗𝑗)∗𝐶𝑂𝑀((𝑖,𝑗)
𝜇 𝑥∗𝜇 𝑦
𝑛
𝑗=1
𝑚
𝑖=1 (13)
Where Pij is the pixel value of the image at position (i, j)
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3. RESULTS AND DISCUSSION
The proposed DDRGRM model with CM features is implemented using a data set of 612 stone
images collected from Mayang database, 678 stone images collected from VisTex database, 832 images
collected from Paul Bourke database, 400 stone texture images collected from Google database i.e., a total of
2522 stone texture images. Sample images of each group are shown in Figure 9.
Figure 9. Sample stone texture images from various databases, 16 from each class
The three CM features i.e Correlation (CR), Cluster Prominence (CP) and Information measure of
correlation1 (IMC1) are extracted on to the DDRGRM model of different stone texture groups of images and
the results are stored in the feature vector. Feature set leads to representation of training images. The three
CM features of stone images of four groups i.e. Marble, Granite, Bricks and Mosaic are shown in Tables 1, 2,
3, and 4 respectively. Based on these feature set values the tested image is classified by using one of the two
approaches and classified the stone images into one of the four pre-defined groups i.e., Marble, Granite,
Bricks and Mosaic. The first approach uses the standard classification algorithms and second approach uses a
user defined algorithm.
Table 1. Feature set values of the granite textures
Sno
IMAGE
NAME
CM features on DDRGRM model
Correlation Cluster Prominence Information measure of correlation1
1 Granite001 0.0213 593 0.7563
2 Granite002 0.0356 613 2.36
3 Granite003 0.1021 601 13.91
4 Granite004 0.0564 487 12.36
5 Granite005 0.0967 476 10.35
6 Granite006 0.1063 513 9.36
7 Granite007 0.1007 576 8.64
8 Granite008 0.0965 519 7.49
9 Granite009 0.0640 412 6.76
10 Granite010 0.0402 472 2.42
11 Granite011 0.0508 396 4.36
12 Granite012 0.0197 386 0.66
13 Granite013 0.0231 393 0.99
14 Granite014 0.0937 592 11.78
15 Granite015 0.0941 553 12.37
16 Granite016 0.0452 412 2.35
17 Granite017 0.0733 402 8.97
18 Granite018 0.0828 497 10.16
19 Granite019 0.1070 778 13.08
20 Granite020 0.0354 712 1.3500
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3.1. By using Standard classification Algorithms
The proposed method is tested using k-Nearest Neighbor Classifier (K-NNC) and support vector
machines (SVM) are used for classification purpose. All experiments are carried out on a PC machine with i5
processor 2.6 GHz CPU and 4 GB RAM memory under MATLAB 10.1a platform. 40 % of the each database
is used for training and remaining 60 % images are used for testing purpose i.e. 1008 images are used for
training purpose and 1514 images are used for testing purpose. The percentage of classification of the
proposed method with K-NNC applied and generated values are listed out in Table 5. The percentage of
classification of the proposed method with Support Vector Machine (SVM) applied and generated values are
listed out in Table 6.
Table 5. Percentage of classification when k-NNC algorithm is applied
Texture Group
Classification Rate of considered Stone texture Databases when k-NN classifier applied
VisTex Mayang Google Paul Bourke Overall %
Bricks 95.9 95.76 95.98 96.35 96
Marble 95.94 96.34 96.06 96.04 96.1
Granite 95.99 95.76 96.28 96.35 96.1
Mosaic 95.88 96.02 96.52 95.3 95.93
Table 6. Percentage of classification when SVM algorithm is applied
Texture Group
Classification Rate of considered Stone texture Databases when SVM classifier applied
VisTex Mayang Google Paul Bourke Overall %
Bricks 95.93 96.13 96.14 96.05 96.06
Marble 96.51 96.21 96.24 96.09 96.26
Granite 95.93 96.43 96.24 96.14 96.19
Mosaic 96.19 96.67 96.07 96.03 96.24
From above two tables, it is observed that when the K-NN classifier applied to the proposed method
obtained classification percentage as 96.03% and the classification percentage when SVM is applied is
96.19%. Almost two classification algorithms gave same classification percentage and it is high. So the
proposed DDRGRM model is well suited for extraction of features from stone images and to classify the
stone textures into 4 groups.
3.2. By using Standard classification Algorithms
Based on the features extracted on the training data set, the proposed user defined approach derives
a classification approach as shown in algorithm 1 to classify the stone textures into one of the four predefined
groups. So as to test the efficiency of the user defined classification approach the test data set is collected
randomly from different stone texture databases.
Algorithm 1: Algorithm for Classification of Stone textures into 4 pre-defined groups using CM feature on
DDRGRM model of stone images.
Begin
if CP > 800 && CR >= 0.219 then
Print (stone image age is classified as 'Bricks Class’);
Else if CP > 800 && CR < 0.219 then
Print (stone image age is classified as 'Mosaic Class');
Else if CP < 800 && IMC1 > 19 then
Print (stone image age is classified as 'Marble Class’);
Else if CP < 800 && IMC1 < 19 then
Print (stone image age is classified as 'Granite Class');
Else
Print (stone image age is classified as 'Unkonown Class');
End
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Table 7. Classification rates of stone images into 4 groups using CM feature on DDRGRM model of texture
images based on Algorithm 1.
Texture
Group
Classification Rate of considered Stone texture Databases when User define Classification Algorithm is used
VisTex Mayang Google Paul Bourke Overall %
Bricks 95.97 96 96.11 96.25 96.08
Marble 96.28 96.33 96.2 96.12 96.23
Granite 96.01 96.15 96.31 96.3 96.19
Mosaic 96.09 96.4 96.35 95.72 96.14
From the two sections, observe that the extracted features are well suited for classifiation of stone
textures when standard and user defined classification algorithms. For analysing the results the confsion
matrix is generated when user defined algorithm is applied on test database. The confusion matrix is shown
in Table 8. The confusion matrix shows the classified class for each input texture in test database.
Table 8. Confusion matrix of the proposed method
Texture Group Marble Mosaic Bricks Granite
Marble 632 2 1 1
Mosaic 0 624 1 1
Bricks 2 1 624 2
Granite 1 2 0 626
4. COMPARISON WITH OTHER EXISTING METHODS:
The proposed approach based on GLCM highlight on DDRGRM for stone texture classification has
shown better classification rate in comparison with other existing approaches. The results of other existing
approaches that are considered for comparison include: classification approach proposed by Vijay et al [22]
which used Overlapped 5-bit T-Patterns Occurrence on 5-by-5 sub images, Wavelet based Histogram on
Texton Patterns (WHTP) [23] proposed by Sasi Kiran et al, texture classification based on Texton Features
[24] by Ravi babu et al and approach based on Syntactic Pattern on 3D technique [25]. It is quite evident that,
the proposed strategy resulted in high characterization rate than the existing techniques. The classification
rate for the proposed and other existing strategies are shown in Table 9 and the same was portrayed using
graphical representation in Figure 10.
Table 9. Percentage mean classification rates for proposed DDRGRM model and other existing methods in
the literature
Image Database 5-bit 'T' Pattern
Approach
Syntactic Pattern
on 3D method
Texton Feature
Detection
Wavelet based Histogram
on Texton Patterns
Proposed
DDRGRM Method
VisTex 95.95 93.15 95.46 92.87 95.93
Texture Images Taken
by Camera
96.35 92.87 95.12 91.7 96.85
Google 96.76 93.32 94.86 93.56 96.96
Mayang 95.85 92.83 94.39 92.95 96.15
Paul Bourke 95.93 93.05 95.23 93.05 95.98
Figure 10. Comparison graph showing the classification rate of proposed and other existing approaches
For different data sets
89
90
91
92
93
94
95
96
97
VisTex
Texture Images
Taken by Camera
Google
Mayang
Paul Bourke
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5. CONCLUSION
The proposed DDRGRM strategy utilizing CM characterized stone textures into four groups by
means of dimensionality reduction and reduced gray level of the texture images. Still the proposed approach
achieved high classification rate, by retaining all critical nearby components including edge highlights and
using three important Haralick parameters for powerful exact stone surface grouping. The proposed
technique definitely lessened the computational time due to reduced dimensionality and gray level. Further
the proposed approach extracted the features which are suitable to apply both existing standard classification
approaches like k-NN and SVM approaches and also the user defined approach. This helped in verifying the
efficiency of the proposed DDRGRM approach. It is evident from above results that proposed approach has
resulted in high classification accuracy of 96.37% in comparison with 96.03 % and 96.19% by k-NN
classification and SVM approaches.
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