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
PDE BASED FEATURES FOR TEXTURE ANALYSIS USING WAVELET TRANSFORMIJCI JOURNAL
This document summarizes a research paper that proposes a novel method for texture analysis using wavelet transforms and partial differential equations (PDEs). The method involves applying wavelet transforms to images to obtain directional information. Anisotropic diffusion, a PDE technique, is then used on the directional information to compute a texture approximation. Various statistical features are extracted from the texture approximation. Linear discriminant analysis enhances class separability of the features before classification using k-nearest neighbors. The method is evaluated on the Brodatz texture dataset and results show it achieves better classification accuracy than other methods while having lower computational cost.
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
06 9237 it texture classification based edit putriIAESIJEECS
Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.
Spectral Density Oriented Feature Coding For Pattern Recognition ApplicationIJERDJOURNAL
ABSTRACT:- The significant of multi spectral band resolution is explored towards selection of feature coefficients based on its energy density. Toward the feature representiaon in transformed domain, multi wavelet transformations were used for finer spectral representation. However, due to a large feature count these features are not optimal under low resource computing system. In the recognition units, running with low resources a new coding approach of feature selection, considering the band spectral density is developed. The effective selection of feature element, based on its spectral density achieve two objective of pattern recognition, the feature coefficient representiaon is minimized, hence leading to lower resource requirement, and dominant feature representation, resulting in higher retrieval performance.
The mean shift procedure is a general nonparametric technique for analyzing complex multimodal feature spaces and delineating arbitrarily shaped clusters. It works by recursively finding the nearest stationary point of the underlying density function, which corresponds to the mode of the density. The mean shift procedure relates to kernel density estimation and robust M-estimators of location. It provides a versatile tool for feature space analysis that can solve many low-level computer vision tasks with few parameters.
Density Driven Image Coding for Tumor Detection in mri ImageIOSRjournaljce
The significant of multi spectral band resolution is explored towards selection of feature coefficients based on its energy density. Toward the feature representiaon in transformed domain, multi wavelet transformations were used for finer spectral representation. However, due to a large feature count these features are not optimal under low resource computing system. In the recognition units, running with low resources a new coding approach of feature selection, considering the band spectral density is developed. The effective selection of feature element, based on its spectral density achieve two objective of pattern recognition, the feature coefficient representiaon is minimized, hence leading to lower resource requirement, and dominant feature representation, resulting in higher retrieval performance.
A Comparative Study of Wavelet and Curvelet Transform for Image DenoisingIOSR Journals
Abstract : This paper describes a comparison of the discriminating power of the various multiresolution based thresholding techniques i.e., Wavelet, curve let for image denoising.Curvelet transform offer exact reconstruction, stability against perturbation, ease of implementation and low computational complexity. We propose to employ curve let for facial feature extraction and perform a thorough comparison against wavelet transform; especially, the orientation of curve let is analysed. Experiments show that for expression changes, the small scale coefficients of curve let transform are robust, though the large scale coefficients of both transform are likely influenced. The reason behind the advantages of curvelet lies in its abilities of sparse representation that are critical for compression, estimation of images which are denoised and its inverse problems, thus the experiments and theoretical analysis coincide . Keywords: Curvelet transform, Face recognition, Feature extraction, Sparse representation Thresholding rules,Wavelet transform..
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
PDE BASED FEATURES FOR TEXTURE ANALYSIS USING WAVELET TRANSFORMIJCI JOURNAL
This document summarizes a research paper that proposes a novel method for texture analysis using wavelet transforms and partial differential equations (PDEs). The method involves applying wavelet transforms to images to obtain directional information. Anisotropic diffusion, a PDE technique, is then used on the directional information to compute a texture approximation. Various statistical features are extracted from the texture approximation. Linear discriminant analysis enhances class separability of the features before classification using k-nearest neighbors. The method is evaluated on the Brodatz texture dataset and results show it achieves better classification accuracy than other methods while having lower computational cost.
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
06 9237 it texture classification based edit putriIAESIJEECS
Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.
Spectral Density Oriented Feature Coding For Pattern Recognition ApplicationIJERDJOURNAL
ABSTRACT:- The significant of multi spectral band resolution is explored towards selection of feature coefficients based on its energy density. Toward the feature representiaon in transformed domain, multi wavelet transformations were used for finer spectral representation. However, due to a large feature count these features are not optimal under low resource computing system. In the recognition units, running with low resources a new coding approach of feature selection, considering the band spectral density is developed. The effective selection of feature element, based on its spectral density achieve two objective of pattern recognition, the feature coefficient representiaon is minimized, hence leading to lower resource requirement, and dominant feature representation, resulting in higher retrieval performance.
The mean shift procedure is a general nonparametric technique for analyzing complex multimodal feature spaces and delineating arbitrarily shaped clusters. It works by recursively finding the nearest stationary point of the underlying density function, which corresponds to the mode of the density. The mean shift procedure relates to kernel density estimation and robust M-estimators of location. It provides a versatile tool for feature space analysis that can solve many low-level computer vision tasks with few parameters.
Density Driven Image Coding for Tumor Detection in mri ImageIOSRjournaljce
The significant of multi spectral band resolution is explored towards selection of feature coefficients based on its energy density. Toward the feature representiaon in transformed domain, multi wavelet transformations were used for finer spectral representation. However, due to a large feature count these features are not optimal under low resource computing system. In the recognition units, running with low resources a new coding approach of feature selection, considering the band spectral density is developed. The effective selection of feature element, based on its spectral density achieve two objective of pattern recognition, the feature coefficient representiaon is minimized, hence leading to lower resource requirement, and dominant feature representation, resulting in higher retrieval performance.
A Comparative Study of Wavelet and Curvelet Transform for Image DenoisingIOSR Journals
Abstract : This paper describes a comparison of the discriminating power of the various multiresolution based thresholding techniques i.e., Wavelet, curve let for image denoising.Curvelet transform offer exact reconstruction, stability against perturbation, ease of implementation and low computational complexity. We propose to employ curve let for facial feature extraction and perform a thorough comparison against wavelet transform; especially, the orientation of curve let is analysed. Experiments show that for expression changes, the small scale coefficients of curve let transform are robust, though the large scale coefficients of both transform are likely influenced. The reason behind the advantages of curvelet lies in its abilities of sparse representation that are critical for compression, estimation of images which are denoised and its inverse problems, thus the experiments and theoretical analysis coincide . Keywords: Curvelet transform, Face recognition, Feature extraction, Sparse representation Thresholding rules,Wavelet transform..
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
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
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.
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 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.
Multi Wavelet for Image Retrival Based On Using Texture and Color QuerysIOSR Journals
This document summarizes a research paper on using multi-wavelet transforms for content-based image retrieval. The paper proposes extracting multi-wavelet features from images in a database and query images to measure similarity. It calculates energy levels from multi-wavelet sub-bands and uses Canberra distance between feature vectors to retrieve similar images. The method achieves 98.5% accuracy and is faster than using Gabor wavelets. In conclusion, multi-wavelet transforms provide good performance for content-based image retrieval applications.
Sub-windowed laser speckle image velocimetry by fast fourier transform technique
Abstract
In this work, laser speckle velocimetry, a unique optical method for velocity measurement of fluid flow has been described. A laser sheet is developed and is illuminated on microscopic seeded particles to produce the speckle pattern at the recording plane. Double frame- single-exposure speckle images are captured in such a way that the second speckle image is shifted exactly in a known direction. The auto-correlation method has the ambiguity of direction of flow. To rectify this, spatial shift of the second image has been premeditated. Cross-correlation of sub interrogation areas is obtained by Fast Fourier Transform technique. Four sub-windows processed to obtain the velocity information with vector map analysis precisely.
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.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
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 summarizes a research paper on using discrete wavelet transform for medical image retrieval. It discusses extracting texture features like energy, entropy, contrast and correlation from images using DWT. Haar wavelet is used to analyze texture features. The texture features of images in a database are calculated and compared to an input image to retrieve similar images from the database. Local binary patterns are also extracted as features for classification and retrieval of medical images.
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.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
A Text-Independent Speaker Identification System based on The Zak TransformCSCJournals
This paper presents a novel text-independent speaker identification system based on the discrete Zak transform. The system uses the Zak transform coefficients as features to model 23 speakers from the ELSDSR database. During identification, the Euclidean distance between the Zak transform of the test speech and each speaker model is calculated. The speaker with the minimum distance is identified. The system achieves an identification efficiency of 91.3% using a single test file and 100% using two test files. The Zak-based method is also faster and has comparable accuracy to MFCC-based speaker identification. The paper also explores dividing signals into segments and averaging the Zak transforms, which improves efficiency while only slightly increasing modeling time.
Mammographic Feature Enhancement using Singularities of Contourlet Transformidescitation
Early detection of breast cancer reduces the
mortality rate among women. Computer aided enhancement
techniques in mammograms assist radiologists in providing a
second opinion. In this work, selected modulus maxima of
Contourlet Transform and selected zero-crossings of the
Contourlet Transform were utilized for enhancing
microcalcification features in mammograms while reducing
noise. Relevant and strong edge information at various levels
was retained based on a parent-child relationship among
selected Contourlet coefficients. Experimental results of the
proposed techniques based on contourlet transform
demonstrate the superiority of the modulus maxima method
compared to the zero crossing method in mammographic
image enhancement. To validate the methods the mini – MIAS
database was employed. Various quality measures considered
for performance evaluation are Contrast improvement index,
Peak Signal to Noise Ratio, Target to Background Contrast
ratio and Tenengrad Criterion.
This document discusses image fusion techniques for enhancing images. It begins with an introduction to image fusion, which combines relevant information from multiple images of the same scene into a single enhanced image. It then discusses discrete wavelet transform (DWT) based image fusion in more detail. Several image fusion rules for combining coefficient data during the DWT process are described, including maximum selection, weighted average, and window-based verification schemes. The importance of image fusion for applications like object identification, classification, and change detection is highlighted. Finally, the document reviews related work on different image fusion methods and algorithms proposed by other researchers.
The document describes the design of a custom cryptographic processor for implementing symmetric key operations. The processor is implemented on an FPGA using Verilog. It includes instruction units to perform logical operations, arithmetic operations, and finite field arithmetic needed for symmetric key algorithms like AES, Blowfish, RC5, RC6, IDEA. The processor is pipelined for high speed and includes modules for an ALU, control unit, registers, and multiplexers. Experimental results showed the processor operates at high speed with low area and delay compared to a general purpose processor.
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
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
The document analyzes water quality parameters of the Vaigai River in Tamil Nadu, India. Samples were collected from three locations along the river on a monthly basis and tested for total dissolved solids, total suspended solids, electrical conductivity, dissolved oxygen, and magnesium. The results found that two of the sampling locations had water quality issues like high dissolved solids and magnesium that require treatment for drinking. Water quality was generally better during monsoon seasons with more rainfall. The study aims to evaluate the river's water resources and classify them for different uses.
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
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.
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 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.
Multi Wavelet for Image Retrival Based On Using Texture and Color QuerysIOSR Journals
This document summarizes a research paper on using multi-wavelet transforms for content-based image retrieval. The paper proposes extracting multi-wavelet features from images in a database and query images to measure similarity. It calculates energy levels from multi-wavelet sub-bands and uses Canberra distance between feature vectors to retrieve similar images. The method achieves 98.5% accuracy and is faster than using Gabor wavelets. In conclusion, multi-wavelet transforms provide good performance for content-based image retrieval applications.
Sub-windowed laser speckle image velocimetry by fast fourier transform technique
Abstract
In this work, laser speckle velocimetry, a unique optical method for velocity measurement of fluid flow has been described. A laser sheet is developed and is illuminated on microscopic seeded particles to produce the speckle pattern at the recording plane. Double frame- single-exposure speckle images are captured in such a way that the second speckle image is shifted exactly in a known direction. The auto-correlation method has the ambiguity of direction of flow. To rectify this, spatial shift of the second image has been premeditated. Cross-correlation of sub interrogation areas is obtained by Fast Fourier Transform technique. Four sub-windows processed to obtain the velocity information with vector map analysis precisely.
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.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
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 summarizes a research paper on using discrete wavelet transform for medical image retrieval. It discusses extracting texture features like energy, entropy, contrast and correlation from images using DWT. Haar wavelet is used to analyze texture features. The texture features of images in a database are calculated and compared to an input image to retrieve similar images from the database. Local binary patterns are also extracted as features for classification and retrieval of medical images.
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.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
A Text-Independent Speaker Identification System based on The Zak TransformCSCJournals
This paper presents a novel text-independent speaker identification system based on the discrete Zak transform. The system uses the Zak transform coefficients as features to model 23 speakers from the ELSDSR database. During identification, the Euclidean distance between the Zak transform of the test speech and each speaker model is calculated. The speaker with the minimum distance is identified. The system achieves an identification efficiency of 91.3% using a single test file and 100% using two test files. The Zak-based method is also faster and has comparable accuracy to MFCC-based speaker identification. The paper also explores dividing signals into segments and averaging the Zak transforms, which improves efficiency while only slightly increasing modeling time.
Mammographic Feature Enhancement using Singularities of Contourlet Transformidescitation
Early detection of breast cancer reduces the
mortality rate among women. Computer aided enhancement
techniques in mammograms assist radiologists in providing a
second opinion. In this work, selected modulus maxima of
Contourlet Transform and selected zero-crossings of the
Contourlet Transform were utilized for enhancing
microcalcification features in mammograms while reducing
noise. Relevant and strong edge information at various levels
was retained based on a parent-child relationship among
selected Contourlet coefficients. Experimental results of the
proposed techniques based on contourlet transform
demonstrate the superiority of the modulus maxima method
compared to the zero crossing method in mammographic
image enhancement. To validate the methods the mini – MIAS
database was employed. Various quality measures considered
for performance evaluation are Contrast improvement index,
Peak Signal to Noise Ratio, Target to Background Contrast
ratio and Tenengrad Criterion.
This document discusses image fusion techniques for enhancing images. It begins with an introduction to image fusion, which combines relevant information from multiple images of the same scene into a single enhanced image. It then discusses discrete wavelet transform (DWT) based image fusion in more detail. Several image fusion rules for combining coefficient data during the DWT process are described, including maximum selection, weighted average, and window-based verification schemes. The importance of image fusion for applications like object identification, classification, and change detection is highlighted. Finally, the document reviews related work on different image fusion methods and algorithms proposed by other researchers.
The document describes the design of a custom cryptographic processor for implementing symmetric key operations. The processor is implemented on an FPGA using Verilog. It includes instruction units to perform logical operations, arithmetic operations, and finite field arithmetic needed for symmetric key algorithms like AES, Blowfish, RC5, RC6, IDEA. The processor is pipelined for high speed and includes modules for an ALU, control unit, registers, and multiplexers. Experimental results showed the processor operates at high speed with low area and delay compared to a general purpose processor.
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
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
The document analyzes water quality parameters of the Vaigai River in Tamil Nadu, India. Samples were collected from three locations along the river on a monthly basis and tested for total dissolved solids, total suspended solids, electrical conductivity, dissolved oxygen, and magnesium. The results found that two of the sampling locations had water quality issues like high dissolved solids and magnesium that require treatment for drinking. Water quality was generally better during monsoon seasons with more rainfall. The study aims to evaluate the river's water resources and classify them for different uses.
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
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
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
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
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
The document describes a genetic algorithm approach to optimizing the design of steel-concrete composite plane frames to minimize cost. Key points:
- The algorithm uses genetic algorithms to determine the optimal sizes of composite beams and columns that minimize total frame cost while satisfying strength, serviceability, and other design constraints.
- Design variables are the cross-sectional properties of beams and columns. The algorithm evaluates candidate designs, analyzes the frame, and checks if designs satisfy constraints.
- The best designs are kept in subsequent generations while weaker designs are removed, simulating natural selection. This process iterates to find the optimal composite frame design.
- The approach is demonstrated on 2x3 and 2x5 frame examples,
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
This document summarizes two GPU programming models - Accelerator and CUDA. It describes the basic steps in Accelerator programming including creating data arrays, loading them into data-parallel array objects, processing the arrays using Accelerator operations, creating a result object, and evaluating the result on a target processor. It also provides an example code showing the use of ParallelArrays and FloatParallelArray objects. The document then briefly introduces CUDA as a parallel computing platform and programming model for GPUs that provides lower and higher-level APIs.
This document presents a method for interactive image segmentation using constrained active contours. It begins with an overview of existing interactive segmentation techniques, including boundary-based methods like active contours/snakes and region-based methods like random walks and graph cuts. The proposed method initializes a contour using region-based segmentation then refines it using a convex active contour model that incorporates both regional information from seed pixels and boundary smoothness. This allows the contour to globally evolve to object boundaries while handling topology changes.
The document analyzes friction stir welding tools with various threaded pin profiles. Three tool pin profiles are modeled in CATIA and analyzed using ANSYS: cylindrical, frustum, and conical pins with threads. The stress distributions and displacement vectors in the pins are obtained for different rotational speeds and temperatures. The results show that the cylindrical pin profile experiences the lowest stress levels and displacement compared to the other profiles. Increasing rotational speed does not significantly affect the stress, while higher temperatures generally increase stress across all pin profiles. The cylindrical pin profile is determined to be preferable for withstanding loads during friction stir welding.
The document summarizes research on synthesizing and characterizing fluorinated yttrium barium copper oxide (Y3Ba5Cu8Oy) superconducting compounds. X-ray diffraction analysis showed the compounds had an orthorhombic structure similar to YBa2Cu3O7, with a larger 'c' axis. Scanning electron microscopy revealed the grain size increased with higher fluorine content. Transition temperature measurements found the superconducting transition onset temperature increased with fluorine addition up to 0.6, likely due to optimization of oxygen stoichiometry, but saturated beyond that point due to disturbance of the optimum oxygen level. Overall, fluorine doping improved the superconducting properties of the samples.
This document analyzes the performance of different directional antennas for various routing protocols in mobile ad hoc networks. It compares omni-directional, steerable, and switched beam antennas using the DSR, OLSR, and ZRP routing protocols in terms of average jitter, end-to-end delay, throughput, and power consumption. The document outlines the characteristics and operation of each antenna type and routing protocol. It then describes the simulation setup using the QualNet simulator to evaluate and compare the performance of the antennas with the different routing protocols under various mobility scenarios.
Paulo Câmara propõe dia de defesa dos conselhos tutelaresPaulo Veras
O documento propõe um projeto de lei para instituir o dia 6 de fevereiro como o Dia Estadual de Mobilização e Fortalecimento dos Conselhos Tutelares de Pernambuco. A data homenagearia três conselheiros tutelares assassinados e reconheceria a importância do trabalho dos Conselhos Tutelares na defesa dos direitos da criança e adolescente. A procuradoria opinou favoravelmente ao envio do projeto à Assembleia Legislativa.
Conferencia Evernote herramienta que te ayuda a organizarteSoluciona Facil
Este documento describe cómo Evernote puede ayudar a las personas a organizarse. Explica qué es Evernote, los pasos para registrarse y descargar las aplicaciones, y cómo puede usarse para mejorar la productividad personal y empresarial al digitalizar notas, documentos y más. También cubre las ventajas de la suscripción Premium y cómo Evernote se integra con otras aplicaciones.
Este documento describe la amistad como una de las relaciones humanas más hermosas que se puede formar a cualquier edad y entre personas de diferentes orígenes. Define la amistad como una relación de mutua benevolencia y confianza que requiere compromiso y deberes entre los amigos. Finalmente, ofrece algunas definiciones y frases sobre lo que significa un verdadero amigo.
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...csandit
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...cscpconf
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
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
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.
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
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet-based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image compared to previous methods.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image.
Ijri ece-01-02 image enhancement aided denoising using dual tree complex wave...Ijripublishers Ijri
This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is performed through the dual-tree complex wavelet transform , lanczos interpolator and edge preserving smoothing filters. Unlike the discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six orientations of the DTWCT, then it is normalized.
Keywords: dual-tree complex wavelet transform (DTWCT), lanczos interpolator, edge preserving smoothing filters.
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.
Ijri ece-01-02 image enhancement aided denoising using dual tree complex wave...Ijripublishers Ijri
This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular
algorithms based on the random spray sampling technique, but not only. According to the nature of sprays,
output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate
assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is
considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity
of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the
non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is
performed through the dual-tree complex wavelet transform , lanczos interpolator and edge preserving smoothing filters.
Unlike the discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space.
For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the
six orientations of the DTWCT, then it is normalized.
Keywords: dual-tree complex wavelet transform (DTWCT), lanczos interpolator, edge preserving smoothing filters.
This document summarizes research on using the discrete curvelet transform for content-based image retrieval.
The researchers propose extracting texture features from images using discrete curvelet transforms. Low-level statistics like mean and standard deviation are computed from the curvelet coefficients to represent images. Experiments on the Brodatz texture database show the curvelet-based features significantly outperform Gabor filters and wavelets. Curvelets better capture edge information and directionality compared to other transforms. With a 5-level decomposition, average retrieval precision was 79.54% versus 70.3% for wavelets. In conclusion, curvelet transforms are a promising technique for texture-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
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.
An Adaptive Moving Mesh Method For Two-Dimensional Relativistic Magnetohydrod...Scott Faria
This document describes an adaptive moving mesh method for solving two-dimensional thin film flow equations that include surface tension. The method uses moving mesh partial differential equations (MMPDEs) and mesh density functions to adaptively move the computational mesh based on solution characteristics like curvature. Numerical results show this r-adaptive moving mesh technique accurately captures the moving contact line and associated fingering instability with reduced computational effort compared to a fixed uniform mesh.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
This document proposes an Adaptive Resolution Enhancement Algorithm (AREA) using an Edge Targeted Filter (ETF) and Dual Tree Complex Wavelet Transform (DTCWT) to enhance the resolution and image quality of low-quality, low-resolution images. The ETF first detects edges in an input image and generates a mask. It then focuses on improving edge reconstruction. The output is input to DTCWT, which analyzes subband coefficients to detect flaws and interpolates coefficients to enhance spectral resolution. The inverse DTCWT produces a spatially enhanced output image with improved resolution up to 17% compared to existing techniques. Simulation results demonstrate the effectiveness of the proposed algorithm.
An efficient image segmentation approach through enhanced watershed algorithmAlexander Decker
This document proposes an efficient image segmentation approach combining an enhanced watershed algorithm and color histogram analysis. The watershed algorithm is applied to preprocessed images after merging the results with an enhanced edge detection. Over-segmentation issues are addressed through a post-processing step applying color histogram analysis to each segmented region, improving overall performance. The document provides background on image segmentation techniques, reviews related work applying watershed algorithms, and discusses challenges like over-segmentation that watershed approaches can face.
Performance analysis of contourlet based hyperspectral image fusion methodijitjournal
Recently, contourlet transform has been widely used in hyperspectral image fusion due to its advantages,
such as high directionality and anisotropy; and studies show that the contourlet-based fusion methods
perform better than the existing conventional methods including wavelet-based fusion methods. Few studies
have been done to comparatively analyze the performance of contourlet-based fusion methods;
furthermore, no research has been done to analyze the contourlet-based fusion methods by focusing on
their unique transform mechanisms. In addition, no research has focused on the original contourlet
transform and its upgraded versions. In this paper, we investigate three different kinds of contourlet
transform: i) original contourlet transform, ii) nonsubsampled contourlet transform, iii) contourlet
transform with sharp frequency localization. The latter two transforms were developed to overcome the
major drawbacks of the original contourlet transform; so it is necessary and beneficial to see how they
perform in the context of hyperspectral image fusion. The results of our comparative analysis show that the
latter two transforms perform better than the original contourlet transform in terms of increasing spatial
resolution and preserving spectral information.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
GraphRAG for Life Science to increase LLM accuracy
Li2519631970
1. Manoj Kumar, Prof. Sagun Kumar sudhansu, Dr. Kasabegoudar V. G / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1963-1970
Wavelet Based Texture Analysis And Classification With Linear
Regration Model
Manoj Kumar*, Prof. Sagun Kumar sudhansu**, Dr. Kasabegoudar V. G.
***
*(PG Department, Collage of Engineering Ambejogai)
Abstract
The Wavelet Transform is a analysis of spatial relations between neighbourhood
multiresolution analysis tool commonly applied to pixels in a small image region. As a consequence,
texture analysis and classification and also their performance is the best for the class of so
Wavelet based pre-processing is a very successful called micro-textures [9]. With a study on human
method providing proper Image Enhancement vision system, researchers begin to develop the
and remove noise without considerable change in multi-resolution texture analysis models, such as the
overall intensity level. The Wavelet Transform wavelet transform and the Gabor transform.
mostly used for contrast enhancement in noisy Extensive research has demonstrated that these
environments. In this paper we propose a texture approaches based on the multi-resolution analysis
analysis with the linear regression model based on could achieve reasonably good performance, so they
the wavelet transform. This method is motivated have already been widely applied to texture analysis
by the observation that there exists a distinctive and classification. The most common multi-
correlation between the samples images, resolution analysis approach is to transform a
belonging to the same kind of texture, at different texture image into local spatial/frequency
frequency regions obtained by 2-D wavelet representation by wrapping this image with a bank
transform. Experimentally, it was observed that of filters with some tuned parameters.
this correlation varies from texture to texture. This property coincides with the study that
The linear regression model is employed to the human visual system can be modelled as a set of
analyze this correlation and extract texture channels. This clearly motivates researchers to study
features that characterize the samples. Our how to extract more discriminable texture feature
method considers not only the frequency regions based on the multi-resolution techniques. Currently
but also the correlation between these regions. In the wavelet transform and the Gabor transform are
contrast the tree structured wavelet transform the most popular multi-resolution methods.
(TSWT) do not consider the correlation between Compared to the wavelet transform [10]-[13], the
different frequency regions. Experiments show Gabor transforms needs to select the filter
that our method significantly improves the parameters according to different texture. There is a
texture classification rate in comparison with the, trade-off between redundancy and completeness in
TSWT, Gabor Transform and GLCM with the design of the Gabor filters because of none
Gabor and some recently proposed methods Orthogonality. The Gabor transform is also limited
derived from these. to its filtering area. Consequently, we choose the
wavelet transform to obtain the spectral information
Index Terms—Linear regression, texture of the texture image. Texture image at different
analysis, texture classification, wavelet scales and use the statistics of the spectral
transforms. information as the texture descriptor, they ignore the
texture structural information. In this paper, it is
I. INTRODUCTION found that there exists a distinctive correlation
Worldwide networking allows us to between some sample textures images, belonging to
communicate, share, and learn information in the the same kind of texture, at different frequency
global manner. Digital library and multimedia regions obtained by 2-D wavelet packet transform.
databases are rapidly increasing; so efficient search Experimentally it is demonstrated that this
algorithms need to be developed. correlation varies from texture to texture. A new
Texture provides essential information for texture classification method, in which the simple
many image classification tasks. Much research has linear regression model is employed into analyzing
been done on texture classification during the last this correlation, is presented. Experiments show that
three decades [1]-[4]. In the 1980s, most traditional this method significantly improves the texture
approaches included gray level co-occurrence classification rate in comparison with the
matrices (GLCM) [5], second-order statistic method multiresolution methods, including TSWT, the
[6], Gauss–Markov random field [7], and local Gabor transform [14]-[16], and some recently
linear transform [8], which are restricted to the proposed methods. The most common
1963 | P a g e
2. Manoj Kumar, Prof. Sagun Kumar sudhansu, Dr. Kasabegoudar V. G / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1963-1970
multiresolution analysis approach is to transform a LL regions, whereas the 2-D wavelet packet
texture image into local spatial/frequency transform decomposes all frequency regions to
representation by wrapping this image with a bank achieve a full decomposition, as shown in Fig. 1 and
of filters with some tuned parameters. This property 2, Therefore, the 2-D PSWT depicts the
coincides with the study that the human visual characteristics of the image in the LL regions and the
system can be modelled as a set of channels. This 2-D wavelet packet trans- form describes the
clearly motivates researchers to study how to extract properties of the image in all regions. Most of the
more discriminable texture feature based on the research in the multiresolution analysis based on the
multiresolution techniques [17-[18]. Currently, the wavelet domain focuses on directly extracting the
wavelet transform and the Gabor transform are the energy values from the sub images and uses them to
most popular multiresolution methods. characterize the texture image. In this paper, the
In this paper, it is found that there exists a mean of the magnitude of the sub-image coefficients
distinctive correlation between some sample texture is used as its energy.
images, belonging to the same kind of texture, at In this paper the mean of the magnitude of
different frequency regions obtained by 2-D wavelet the sub image coefficients is used as its energy. That
Packet transforms. Experimentally it is demonstrated is, if the sub image is x(m,n),with 1≤m≤M
that this correlation varies from texture to texture. A and1≤n≤N its energy can be represented
𝑀 𝑁
new texture classification method, in which the E=(1/𝑀𝑁) 𝑖=1 𝑗 =1 𝑋(𝑖, 𝑗) 1.0
simple linear regression model is employed into Where x(i,j) is the pixel value of the sub image.
analyzing this rate in comparison with the
multiresolution methods including PSWT, TSWT,
the Gabor transform, and some recently proposed
methods.
This paper is organized as follows. The
brief review about 2-D wavelet transform and an
application of the correlation between different
frequency regions to texture analysis are described in
Section II.
Several multiresolution techniques, such as, TSWT,
and the Gabor and GLCM transform, and our
method are compared. Finally, the conclusions are
summarized in Section III.
II. TEXTURE ANALYSIS WITH LINEA
REGRESSION MODEL Fig.1:Flow Chart Level using Decomposition.
A. Two-Dimensional Wavelet Packet Transform:
The wavelet transform provides a precise
and unifying framework for the analysis and
characterization of a signal at different scales. It is
described as a multiresolution analysis tool for the
finite energy function. It can be implemented
efficiently with the pyramid-structured wavelet
transform and the wavelet packet transform. The
pyramid-structured wavelet Performs further
decomposition of a signal only in the low frequency
regions. Adversely, the wavelet packet transform
decomposes a signal in all low and high frequency
regions. As the extension of the 1-D wavelet
transform, the 2-D wavelet transform can be carried
out by the tensor product of two 1-D wavelet base
functions along the horizontal and vertical directions,
and the corresponding filters can be expressed as h L
L(k,l) = h(k)h(l), h L H(k,l) = h(k)h(l), h H L(k,l) =
g(k)h(l) and h H H(k,l) = g(k)g(l). An image can be
decomposed into four sub images by convolving the
image with these filters. These four sub images Fig.2 decomposition of image in HL, LL, HH, LH
characterize the frequency information of the image region
in the LL, LH, HL, and HH frequency regions
respectively. The 2-D PSWT can be constructed by
the whole process of repeating decomposition in the
1964 | P a g e
3. Manoj Kumar, Prof. Sagun Kumar sudhansu, Dr. Kasabegoudar V. G / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1963-1970
B. Analysis of Correlation between Frequency C) Linear Regression Model:
channels. In the above subsection, the correlation
Given that there are some sample texture between different frequency regions has been
images from the same kind of texture, these images validated as a sort of effective texture characteristic.
should have the same spatial relation between In this section, we employ the simple linear
neighbourhood pixels as this texture. These images regression model to analyze the correlation. Suppose
are all decomposed to obtain the same frequency that we have a set of the random data
regions by 2-D wavelet transform. The most 𝑋1 𝑌1 T…… (X_n Y_n )T, for two numerical
common approach is to calculate all frequency variables X and Y and suppose that we regard X as a
regions’ energy values of every image with the cause of Y. From the simple linear regression
energy function and to characterize this texture by analysis, the distribution of the random data
the statistics of these energy values. This approach approximately appears a straight line in X, Y space
ignores the spatial relation of these sample texture when X and Y are perfectly related linearly. There is
images. In this study, we capture this inherent texture a linear function that captures the systematic
property by learning a number of sample texture relationship between two variables. This line
images. From a statistical perspective, a frequency function (also called the simple linear regression
region of a sample texture image can be viewed as a equation) can be given as follows:
random variable and the energy values of this yˆ= a * x + b
frequency region can be treated as the random values Where yˆ is called a fitted value of y.
of this variable Experimentally it is found that there We exploit the simple linear regression
exists a distinctive correlation between different model to extract the texture features from the
variables (frequency regions). correlation in the frequency channel pairs. The
As a powerful multiresolution analysis tool, channel-pair list includes all channel pairs with T.
the2-D wavelet transform has proved useful in For two frequency channels of one channel pair in
texture analysis, classification, and segmentation. the list, we take out their energy values from the
The 2-D PSWT performs further decomposition of a channel-energy matrix M and then consider these
texture image only in the low frequency regions. energy values as the random data 𝑋1 𝑌1 T,... , (X_n
Consequently it is not suitable for images whose Y_n )T, for two variables X and Y.The distribution
dominant frequency information is located in the of these energy values should also represent a
middle or high frequency regions. Although the 2-D straight line in X,Y space. In general, the wavelet
wavelet packet transform characterizes the packet transform generates a multiresolution texture
information in all frequency regions, this representation including all frequency channels of a
representation is redundant. On the other hand, in texture image with the complete and orthogonal
order to generate a sparse representation, the 2-D wavelet basis functions which have a “reasonably
TSWT decomposes the image dynamically in the well controlled” spatial/frequency localization
low and high frequency regions whose energies are property. Our method absorbs this advantage of the
higher than a predetermined threshold. However, it wavelet packet transform. The difference between
ignores the correlation between different frequency our method and other multiresolution based texture
regions. We use 2-D wavelet packet transform to analysis methods are PSWT loses the middle and
obtain all frequency information of a texture image high frequency information. TSWT does not take
in this paper The detail is described as follow. First, into the account the correlation between different
the original image is decomposed into four sub- frequency channels. The Gabor transform uses a
images, which can be viewed as the parent node and fixed n umber of filter masks with predetermined
the four children nodes in a tree and named as O, A, frequency and bandwidth parameters. In contrast, our
B, C, and D respectively, as shown in Fig. 3. method not only thinks about all frequency channels
but also analyzes the correlation between them with
the simple linear regression model. So, it can be
viewed as a very useful multiresolution method.
III. EXPERIMENTAL RESULT
In this section, we will verify the
performance. we used 40 textures .Every original
image is of size 640X640 pixels with 256 gray
levels. 81 sample images of size 128 with an
overlap32 pixels between vertically and horizontally
adjacent images are extracted from each original
Fig. 3 Tree Representation colour image and used in the experiments, and the
mean of every image is removed before the
They symbolize the original image, LL, LH, HL, and processing. These 3240 texture images are separated
HH frequency regions. to two sets being used as the training set with 1600
1965 | P a g e
4. Manoj Kumar, Prof. Sagun Kumar sudhansu, Dr. Kasabegoudar V. G / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1963-1970
images and the test set with 1640 images, and the Gabor transform, and the statistic-based
respectively. We compare our method with other methods, like GLCM, can achieve the super multi-
traditional multi-resolutionion methods, such as . resolution methods because they take the texture
The tree-structured wavelet transforms and Gabor primitives into account. In particular, the
and Gabor plus GLCM. combination of the wavelet transform and GLCM
In the classification algorithm of TSWT, its excels our method in many textures though its
feature is constructed by the energy of all frequency average correct rate is still slightly lower than mine.
regions in the third scale and its controllable This dramatic trait can be illuminated that the
parameters like decomposition constant and wavelet transform captures the frequency
comparison constant, are empirically set to 0.15 and information of the texture image at different scales
10, respectively. We implement the Gabor transform and GLCM characterizes the local spatial properties
obtained form by convolving an image with a set of of the texture. However this approach leads to space
functions by appropriate dilations and rotations of increase the feature dimension and brings in the
the Gabor mother function. The average retrieval rate computation complexity in classification scheme,
defined in our method is different from that of other even the curse of dimensionality. Feature reduction
methods .Because we use threshold based like principle component analysis (PCA) alleviates
comparison in one dimension. All query samples are the curse to some extent at the cost of affecting the
processed by our method and respectively assigned performance [19] our method is to take advantage of
to the corresponding texture. We count the samples the texture inherent in the learning phase. Thus
of this texture, which are assigned to the right relation characteristic, our classification algorithm
texture, and get the average percentage number as simply takes the threshold comparison in one
the average retrieval rate of this texture. In view of dimension space in order to avoid the difficulty in
our methods, the distance, where is the query sample choosing a distance function that is suitable for the
and is a texture from the database, is firstly distributions of the multidimensional texture features
computed with the feature vectors discussed above. Furthermore, it is obvious that the simple threshold
The distances are then sorted in increasing order and comparison in one dimension space greatly
the closet set of patterns is then retrieved. In the ideal diminishes the great computation
case all the top 41 retrievals are from the texture. The Complexity in comparison with the distance measure
sample retrieval rate same is defined as the average similarities in high-dimensional space images,
percentage number of samples belonging to the same respectively.
texture as the query sample in the top 41 matches.
The average retrieval rate of a texture is the average IV. CONCLUSION
number of all sample retrieval rates of this texture. In this paper, a new approach to texture
Several different distances similarity measures, like analysis and classification with the simple linear
Euclidean, Bayesian, Mahalanobis, can be explored regression model based on the wavelet transform is
to obtain different results for other The different presented and its good performance on the
distances are applied to different methods in our classification accuracy is demonstrated in the
experiment on behalf of their reasonably good experiments. Although the traditional multi-
performances Table 4 shows the retrieval accuracy of resolution methods, like TSWT, the Gabor
these different multiresolution methods for 40 transform, are suitable for some textures, our
textures. Fig. 4 gives a graph illustrating the retrieval method is natural and effective for much more
performance as a function of texture here Gabor textures. While the fuse of the Gabor transform and
based performance is very poor. However, the GLCM is confined to the Gabor filter, the way of
average accuracy of our method is still first class, combining the wavelet transform with GLCM
highly 96.86%, and much greater than other methods obtains the excellent performance very close to our
94.16% (TSWT), 84.45% GLCK and Gabor), method. However, our method surpasses them in the
76.429% (the Gabor transform), respectively. In classification scheme because it adopts the simple
summery TSWT keeps much more information of threshold comparison and the leave-one-out
the spectral resolution in high frequency regions, so algorithm. It is worthwhile to point out several
it can get better performance than the Gabor,GLCM distinctive characteristics of this new method. To
transform. Gabor with GLCM Excellent performance begin with, our method provides all frequent
compared to TSWT, GLCM, and Gabor. A very channels of the quasi-periodicity texture in
broad class of filters whose parameters are much comparison with PSWT, Gabor, So, it is able to
more suitable for textures in our Database. However, characterize much more spectral information of the
our method considers not only all the frequency texture at different multi-resolution. Next, the
information of the Texture but also the correlation correlation of different frequent channels can be
between them. Therefore, it can achieve the best applied in this method through the simple linear
result in Comparison with TSWT, GLCM and regression model. In contrast, TSWT does not
Gabor, Transform The way of combining the consider the correlation between different frequent
multiresolution methods, like the wavelet transform regions though it also keeps more frequent
1966 | P a g e
5. Manoj Kumar, Prof. Sagun Kumar sudhansu, Dr. Kasabegoudar V. G / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1963-1970
information and employs the energy criterion to get to characterize the texture image at the
over the over complete decomposition. multidimensional space, and our method employs the
Therefore, this texture intrinsic correlation between different frequency regions to
characteristic helps our method go beyond TSWT. the construct the texture feature. So, it employs
Furthermore, most of the research in the multi- threshold comparison in one dimension space rather
resolution analysis directly computes the energy than some distance measures in the multidimensional
values from the sub images and extracts the features space. Therefore, it is very easy and fast to examine
to characterize the texture image at the multi- the change of different frequent channels for texture
dimension space, and, yet, our method employs the image.
correlation between different frequency regions to Our current work has focused so far on the
construct the texture feature. So, it employs the simple linear regression model which is used to
threshold comparison in one dimension space rather employ the linear correlation. More thorough
than some distance measures in the multi-dimension theoretical analysis of the linear correlation is
space. Therefore, it is very easy and fast to examine expected in the future. In addition, the application of
the change of different frequent channels for texture this method to texture segmentation is under our
image. Our current work has focused so far on the current investigation. Due to our method being quite
simple linear regression model which is used to sensitive to noise, it is the urgent requirement of
employ the linear correlation. More thorough achieving its noisy in- variance.
theoretical analysis of the linear correlation is
expected in the future. In addition, the application of
1.2
this method to texture segmentation is under our
current investigation. Due to our method being quite
sensitive to noise shown from Section, it is the 1
urgent requirement of achieving its noisy invariance.
In this paper, a new approach to texture analysis with
OUR
the simple linear regression model based on the 0.8
wavelet transform is presented. Although the METHOD
traditional multiresolution methods, like PSWT, Wavelet
TSWT, the Gabor transform are suitable for some 0.6
textures, our method is natural and effective for
much more textures. GLCM and
Next, the correlation of different frequent 0.4 Gabor
channels can be applied in this method through the Gabor
simple linear regression model. In contrast, TSWT
does not consider the correlation between different 0.2
frequent regions though it also keeps more frequent
information and employs the energy criterion to get
over the decomposition Therefore; this texture 0
intrinsic characteristic helps our method go beyond 1 5 9 13172125293337
TSWT. Furthermore, most of the research in the
multiresolution analysis directly computes the energy
values from the sub images and extracts the features Fig.4. Comparison of classification rate using
to characterize the texture image. In this paper, a new different methods.
approach to texture analysis with the simple linear
regression model based on the wavelet transform is
presented. Although the traditional multiresolution
methods, like PSWT, TSWT, the Gabor transform
are suitable for some textures, our method is natural
and effective for much more textures.
Next, the correlation of different frequent
channels can be applied in this method through the
simple linear regression model. In contrast, TSWT
does not consider the correlation between different
frequent regions though it also keeps more frequent
information and employs the energy criterion to get
over the decomposition Therefore; this texture
intrinsic characteristic helps our method go beyond
TSWT. Furthermore, most of the research in the
multiresolution analysis directly computes the energy
values from the sub images and extracts the features
1967 | P a g e
7. Manoj Kumar, Prof. Sagun Kumar sudhansu, Dr. Kasabegoudar V. G / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1963-1970
REFERENCES
[1] Y. Rui, T. S. Huang, and S. F. Chang,
“Image retrieval: Current techniques,
promising directions, and open issues,” J.
Vis. Common. Image Represent. vol. 10,
pp. 39–62 1999.
[2] R. Randen and J. H. Husøy, “Filtering for
texture classification: A comparative
study,” IEEE Trans. Pattern Anal. Mach.
Intell., vol. 21, no. 4, pp. 291–310, Apr.
[3] J. Zhang and T. Tan, “Brief review of
invariant texture analysis methods,” Pattern
Recognition. vol. 35, pp. 735–747, 2002.
[4] C.-C. Chen and C.-C. Chen, “Filtering
methods for texture discrimination,”
Pattern Recognit. Lett., vol. 20, pp. 783–
790, 1999.
[5] R.M.Haralick, K.Shanmugan, and I.
Dinstein,“Textural features for image
classification,” IEEE Trans. Syst. Man
Cybern., vol. SMC-6, no. 6, pp. 610–621,
Nov. 1973.
[6] P. C. Chen and T. Pavlidis, “Segmentation
by texture using correlation,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 5, no. 1,
pp. 64–69, Jan. 1983.
[7] 7R. L. Kashyap and R. Chellappa,
“Estimation and choice of neighbors in
spatial- interaction models of images,”
IEEE Trans. Inf. Theory, vol. 29, no. 1, pp.
60–72, Jan. 1983.
[8] M.Unser,“Local linear transforms for
texture measurements, “Signal Process.
vol.11,pp.61–79,1986.-400,
[9] M. Unser, “Texture classification and
segmentation using wavel Frames,
M. Unser, “Texture classification and
segmentation using wavel Frames,
[10] T.ChangandC.-C.J.Kuo,“A wavelet
transform approach to texture analysis,” in
Proc. IEEE ICASSP, Mar. 1992, vol. 4, no.
23–26, pp.661–664.
[11] ChangandC.-C.J.Kuo,“Tree-structured
wavelet transform for textured image
Segmentation,”Proc.SPIE,vol.1770,pp.394
–405,1992.
[12] T.Chang andC.-C.J.Kuo, “Texture analysis
Fig.6. Forty Texture used in this experiment. and classification with tree-structured
wavelet transform,”IEEE Trans.Image
ACKNOWLEDGMENT Process., vol. 2,no.4,pp.429–441,Oct.1993.
The authors would like to thank the editor and the [13] G.H.Wu, Y.J.Zhang, andX.G.Lin,“Wavelet
anonymous reviewers for their contributing transform-based texture classification
comments, as well as Prof. Sagun kumar sudhansu Feature
and Dr. Kasabegoudar V.G., PG Department collage weighting,”Proc.IEEEInt.Conf.Image
of engineering Ambejogai) and S. K. Dass. For Processing,vol.4,no.10,pp.435 439,1999.
providing the software. [14] W.Y.Maand B.S.Manjunath,“A
comparison of wavelet transform features
for texture Image annotation,” Proc. IEEE
Int. Conf. Image Processing, vol.2, no.23–
26,pp.256259,Oct.1995.
1969 | P a g e
8. Manoj Kumar, Prof. Sagun Kumar sudhansu, Dr. Kasabegoudar V. G / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1963-1970
[15] W.Y.MaandB.S.Manjunath,“Texture
features and learning similarity,”
Proc.IEEECVPR, .18–20,pp.425–
430,Jun.1996.
[16] B. S. Manjunath and W. Y. Ma, “Texture
feature for browsing and retrieval of
image Data, IEEE rans. Pattern Anal
.Mach. Intel vol.18,no.8,pp.837–
842,Aug.1996.
[17] D.A.ClausiandH.Deng,“Desing-based
texture feature fusion using gabor filters
and co- occurrence probabilities,” IEEE
Trans. Image.
process.,vol.14,no.7,pp.925–
936,Jul.2005.
[18] G. Van de Wouwer, P. Scheunders, and
D. Van Dyck, “Statistical texture
characterization from discrete wavelet
representation,” IEEE Trans.Image,Vol
8,no.4,,pp,592- 598,Apr.1999.
[19] Timothy K. Shih, Lawrence Y. Deng,
Ching-Sheng Wang, and Sheng-En Yeh,
“Conten- base Image Retrieval with
Intensive Signature via Affine Invariant
Transformation”, Dept. of Computer
Science and Information Engineering
Tamkang University.
1970 | P a g e