This document summarizes several commonly used features for cross-spectral image matching between visible light and thermal images. It discusses how features represent information from different spectrum images for matching. The document reviews features such as local binary pattern (LBP), histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), Gabor wavelets, discrete cosine transform (DCT) and binary statistical image features (BSIF) that have been used in cross-spectral face and iris recognition with good results. It provides an overview of how these features extract unique characteristics from visible and thermal images to effectively represent the images and enable successful cross-spectral matching.
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.
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 paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Development of stereo matching algorithm based on sum of absolute RGB color d...IJECEIAES
This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving filters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving filters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the first stage to get the preliminary corresponding result, then the BF works as an edge-preserving filter to remove the noise from the first stage. The second BF is used at the last stage to improve final disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.
This document provides a review of various texture classification approaches and texture datasets. It begins with an introduction to texture classification and its general framework. Key steps in texture classification are preprocessing, feature extraction, and classification. The document then discusses several common feature extraction methods used in texture classification, including local binary pattern (LBP), scale invariant feature transform (SIFT), speeded up robust features (SURF), Fourier transformation, texture spectrum, and gray level co-occurrence matrix (GLCM). It also reviews three popular classifiers for texture classification: K-nearest neighbors (K-NN), artificial neural network (ANN), and support vector machine (SVM). Finally, it mentions several popular texture datasets that are commonly used for training and testing texture
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.
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 paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Development of stereo matching algorithm based on sum of absolute RGB color d...IJECEIAES
This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving filters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving filters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the first stage to get the preliminary corresponding result, then the BF works as an edge-preserving filter to remove the noise from the first stage. The second BF is used at the last stage to improve final disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.
This document provides a review of various texture classification approaches and texture datasets. It begins with an introduction to texture classification and its general framework. Key steps in texture classification are preprocessing, feature extraction, and classification. The document then discusses several common feature extraction methods used in texture classification, including local binary pattern (LBP), scale invariant feature transform (SIFT), speeded up robust features (SURF), Fourier transformation, texture spectrum, and gray level co-occurrence matrix (GLCM). It also reviews three popular classifiers for texture classification: K-nearest neighbors (K-NN), artificial neural network (ANN), and support vector machine (SVM). Finally, it mentions several popular texture datasets that are commonly used for training and testing texture
Spectral approach to image projection with cubic b spline interpolationiaemedu
This document summarizes a research paper that proposes a new method for image projection using spectral interpolation with cubic B-spline interpolation. The key points are:
1) Existing super resolution methods based on Fourier transforms have limitations in providing high visual quality when scaling images.
2) The proposed method first transforms image frames into the frequency domain using FFT. It then interpolates in the spectral domain using cubic B-spline interpolation to project the images onto a higher resolution grid.
3) Experimental results show the proposed method provides higher visual quality projections compared to conventional Fourier-based approaches, while also having faster computation times.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
Feature integration for image information retrieval using image mining techni...iaemedu
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...IJCSEIT Journal
A face recognition system using different local features with different distance measures is proposed in this
paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values,
Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local
features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector
and diagonal vectors are computed for these matrices. Global feature vector is generated for face
recognition. Experiments are performed on benchmark face YALE database. Results indicate that the
proposed method gives better recognition performance in terms of average recognized rate and retrieval
time compared to the existing methods.
This document discusses techniques for enhancing thermal images, including converting to grayscale, histogram equalization, linear and adaptive filtering, and morphological operations. Histogram equalization spreads pixel intensities over the full range to improve contrast. Linear filtering is used for smoothing, sharpening and edge enhancement, while adaptive filtering better preserves edges. Morphological operations use a structuring element to quantify how well an element fits in an image. Fourier transforms change the image domain and can restore images through inverse transforms. The techniques can improve image quality for applications like quality control and diagnostics.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
OBJECT DETECTION, EXTRACTION AND CLASSIFICATION USING IMAGE PROCESSING TECHNIQUEJournal For Research
Domestic refrigerators are widely used household appliances and a large extent of energy is consumed by this system. A phase change material is a substances that can store or release significant amount of heat energy by changing the phase liquid to vapour or vice versa. So, reduction of temperature fluctuation and improvement of system performances is that main reason of using PCM enhances the heat transfer rate thus improves the COP of refrigeration as well as the quality frozen food. The release and storage rate of a refrigerator is depends upon the characteristics of refrigerators and its properties using phase change material for a certain thermal load it is found that COP of conventional refrigerator is increased . The phase change material used in chamber built manually and which surrounds the evaporator chamber of a conventional refrigerator the whole heat transfer for load given to refrigerator cabin (to evaporator) evaporator to phase change material by conduction. This system hence improves the performances of household refrigerator by increasing its compressor cut-off time and thereby minimizing electrical energy usage. The main objective is to improve the performance, cooling time period, storage capacity and to maintain the constant cooling effect for more time during power cut off hours using phase change material.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
This document describes an image preprocessing scheme for line detection using the Hough transform in a mobile robot vision system. The preprocessing includes resizing images to 128x96 pixels, converting to grayscale, performing edge detection using Sobel filters, and edge thinning. A newly developed edge thinning method is found to produce images better suited for the Hough transform than other thinning methods. The preprocessed images are then used as input for line detection and the robot's self-navigation system.
PERFORMANCE ANALYSIS OF CLUSTERING BASED IMAGE SEGMENTATION AND OPTIMIZATION ...cscpconf
Partitioning of an image into several constituent components is called image segmentation.
Myriad algorithms using different methods have been proposed for image segmentation. Many
clustering algorithms and optimization techniques are also being used for segmentation of
images. A major challenge in segmentation evaluation comes from the fundamental conflict
between generality and objectivity. As there is a glut of image segmentation techniques
available today, customer who is the real user of these techniques may get obfuscated. In this
paper to address the above described problem some image segmentation techniques are evaluated based on their consistency in different applications. Based on the parameters used quantification of different clustering algorithms is done.
FINGERPRINT MATCHING USING HYBRID SHAPE AND ORIENTATION DESCRIPTOR -AN IMPROV...IJCI JOURNAL
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process.
Fingerprints are broadly used for personal identification due to its feasibility, distinctiveness, permanence,
accuracy and acceptability. This paper proposes a way to improve the Equal Error Rate (EER) in
fingerprint matching techniques in the domain of hybrid shape and orientation descriptor. This type of
fingerprint matching domain is popular due to capability of filtering false and strange minutiae pairings.
EER is calculated by using FMR and FNMR to check the performance of proposed technique.
This document discusses using Fourier transforms to measure image similarity. It proposes a metric that uses both the real and complex components of the Fourier transform to compute a similarity ranking between two images. The metric calculates the intersection of the covariance matrix of the Fourier transform magnitude and phase spectra of the two images. The approach is shown to be advantageous for images with varying degrees of lighting. It summarizes existing methods for image comparison and feature extraction, and discusses implementing the proposed similarity metric using OpenCV. Sample results are given comparing the new Fourier-based method to existing histogram comparison techniques.
A novel embedded hybrid thinning algorithm forprjpublications
The document proposes a hybrid thinning algorithm that combines the Stentiford and Zhang-Suen thinning algorithms. It compares the hybrid algorithm to the original Stentiford and Zhang-Suen algorithms on an input image. The hybrid algorithm more accurately thins the image to a single pixel width but does not improve time complexity compared to the original algorithms. The hybrid approach uses four templates across two sub-iterations to identify and remove pixels based on connectivity values until no more can be removed. Experimental results show the hybrid algorithm more effectively increases image contrast than the original thinning algorithms.
Comparison of various Image Registration Techniques with the Proposed Hybrid ...idescitation
Image Registration is termed as the method to
transform different forms of image data into one coordinate
system. Registration is a important part in image processing
which is used for matching the pictures which are obtained at
different time intervals or from various sensors. A broad range
of registration techniques have been developed for the various
types of image data. These techniques are independently
studied for many applications resulting in the large body of
result. Vision is the most advanced of human sensors, so
naturally images play one of the most important roles in
human perception. Image registration is one of the branches
encompassed by the diverse field of digital image processing.
Due to its importance in many application areas as well as
since its nature is complicated; image registration is now the
topic of much recent research. Registration algorithms tend
to compute transformations to set correspondence betweenthe two images. In this paper the survey is done on various
image registration techniques. Also the different techniques
are compared with the proposed system of the projec
IRJET - Symmetric Image Registration based on Intensity and Spatial Informati...IRJET Journal
This document presents a proposed system for symmetric image registration based on intensity and spatial information using a technique called the Coloured Simple Algebraic Algorithm (CSAA). The system first preprocesses color images, extracts features, then classifies images as symmetric or asymmetric using a neural network. It is shown to provide accurate and robust registration of medical and biomedical images. The system is implemented and evaluated on sample images, demonstrating it can successfully identify symmetric versus asymmetric images. The proposed approach aims to improve on existing techniques for intensity-based image registration tasks.
A Survey on Image Retrieval By Different Features and TechniquesIRJET Journal
This document discusses various techniques for content-based image retrieval. It begins with an introduction to content-based image retrieval and describes how it uses visual features like color, texture, shape and regions to index and represent image content for retrieval. The document then reviews related work on image retrieval using different features. It discusses features used for image identification like color, edges, corners and texture. The document also outlines techniques for image retrieval including relevance feedback, support vector machines, block truncation coding, and image clustering. Finally, it evaluates parameters for comparing image retrieval algorithms.
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
Spectral approach to image projection with cubic b spline interpolationiaemedu
This document summarizes a research paper that proposes a new method for image projection using spectral interpolation with cubic B-spline interpolation. The key points are:
1) Existing super resolution methods based on Fourier transforms have limitations in providing high visual quality when scaling images.
2) The proposed method first transforms image frames into the frequency domain using FFT. It then interpolates in the spectral domain using cubic B-spline interpolation to project the images onto a higher resolution grid.
3) Experimental results show the proposed method provides higher visual quality projections compared to conventional Fourier-based approaches, while also having faster computation times.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
Feature integration for image information retrieval using image mining techni...iaemedu
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...IJCSEIT Journal
A face recognition system using different local features with different distance measures is proposed in this
paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values,
Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local
features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector
and diagonal vectors are computed for these matrices. Global feature vector is generated for face
recognition. Experiments are performed on benchmark face YALE database. Results indicate that the
proposed method gives better recognition performance in terms of average recognized rate and retrieval
time compared to the existing methods.
This document discusses techniques for enhancing thermal images, including converting to grayscale, histogram equalization, linear and adaptive filtering, and morphological operations. Histogram equalization spreads pixel intensities over the full range to improve contrast. Linear filtering is used for smoothing, sharpening and edge enhancement, while adaptive filtering better preserves edges. Morphological operations use a structuring element to quantify how well an element fits in an image. Fourier transforms change the image domain and can restore images through inverse transforms. The techniques can improve image quality for applications like quality control and diagnostics.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
OBJECT DETECTION, EXTRACTION AND CLASSIFICATION USING IMAGE PROCESSING TECHNIQUEJournal For Research
Domestic refrigerators are widely used household appliances and a large extent of energy is consumed by this system. A phase change material is a substances that can store or release significant amount of heat energy by changing the phase liquid to vapour or vice versa. So, reduction of temperature fluctuation and improvement of system performances is that main reason of using PCM enhances the heat transfer rate thus improves the COP of refrigeration as well as the quality frozen food. The release and storage rate of a refrigerator is depends upon the characteristics of refrigerators and its properties using phase change material for a certain thermal load it is found that COP of conventional refrigerator is increased . The phase change material used in chamber built manually and which surrounds the evaporator chamber of a conventional refrigerator the whole heat transfer for load given to refrigerator cabin (to evaporator) evaporator to phase change material by conduction. This system hence improves the performances of household refrigerator by increasing its compressor cut-off time and thereby minimizing electrical energy usage. The main objective is to improve the performance, cooling time period, storage capacity and to maintain the constant cooling effect for more time during power cut off hours using phase change material.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
This document describes an image preprocessing scheme for line detection using the Hough transform in a mobile robot vision system. The preprocessing includes resizing images to 128x96 pixels, converting to grayscale, performing edge detection using Sobel filters, and edge thinning. A newly developed edge thinning method is found to produce images better suited for the Hough transform than other thinning methods. The preprocessed images are then used as input for line detection and the robot's self-navigation system.
PERFORMANCE ANALYSIS OF CLUSTERING BASED IMAGE SEGMENTATION AND OPTIMIZATION ...cscpconf
Partitioning of an image into several constituent components is called image segmentation.
Myriad algorithms using different methods have been proposed for image segmentation. Many
clustering algorithms and optimization techniques are also being used for segmentation of
images. A major challenge in segmentation evaluation comes from the fundamental conflict
between generality and objectivity. As there is a glut of image segmentation techniques
available today, customer who is the real user of these techniques may get obfuscated. In this
paper to address the above described problem some image segmentation techniques are evaluated based on their consistency in different applications. Based on the parameters used quantification of different clustering algorithms is done.
FINGERPRINT MATCHING USING HYBRID SHAPE AND ORIENTATION DESCRIPTOR -AN IMPROV...IJCI JOURNAL
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process.
Fingerprints are broadly used for personal identification due to its feasibility, distinctiveness, permanence,
accuracy and acceptability. This paper proposes a way to improve the Equal Error Rate (EER) in
fingerprint matching techniques in the domain of hybrid shape and orientation descriptor. This type of
fingerprint matching domain is popular due to capability of filtering false and strange minutiae pairings.
EER is calculated by using FMR and FNMR to check the performance of proposed technique.
This document discusses using Fourier transforms to measure image similarity. It proposes a metric that uses both the real and complex components of the Fourier transform to compute a similarity ranking between two images. The metric calculates the intersection of the covariance matrix of the Fourier transform magnitude and phase spectra of the two images. The approach is shown to be advantageous for images with varying degrees of lighting. It summarizes existing methods for image comparison and feature extraction, and discusses implementing the proposed similarity metric using OpenCV. Sample results are given comparing the new Fourier-based method to existing histogram comparison techniques.
A novel embedded hybrid thinning algorithm forprjpublications
The document proposes a hybrid thinning algorithm that combines the Stentiford and Zhang-Suen thinning algorithms. It compares the hybrid algorithm to the original Stentiford and Zhang-Suen algorithms on an input image. The hybrid algorithm more accurately thins the image to a single pixel width but does not improve time complexity compared to the original algorithms. The hybrid approach uses four templates across two sub-iterations to identify and remove pixels based on connectivity values until no more can be removed. Experimental results show the hybrid algorithm more effectively increases image contrast than the original thinning algorithms.
Comparison of various Image Registration Techniques with the Proposed Hybrid ...idescitation
Image Registration is termed as the method to
transform different forms of image data into one coordinate
system. Registration is a important part in image processing
which is used for matching the pictures which are obtained at
different time intervals or from various sensors. A broad range
of registration techniques have been developed for the various
types of image data. These techniques are independently
studied for many applications resulting in the large body of
result. Vision is the most advanced of human sensors, so
naturally images play one of the most important roles in
human perception. Image registration is one of the branches
encompassed by the diverse field of digital image processing.
Due to its importance in many application areas as well as
since its nature is complicated; image registration is now the
topic of much recent research. Registration algorithms tend
to compute transformations to set correspondence betweenthe two images. In this paper the survey is done on various
image registration techniques. Also the different techniques
are compared with the proposed system of the projec
IRJET - Symmetric Image Registration based on Intensity and Spatial Informati...IRJET Journal
This document presents a proposed system for symmetric image registration based on intensity and spatial information using a technique called the Coloured Simple Algebraic Algorithm (CSAA). The system first preprocesses color images, extracts features, then classifies images as symmetric or asymmetric using a neural network. It is shown to provide accurate and robust registration of medical and biomedical images. The system is implemented and evaluated on sample images, demonstrating it can successfully identify symmetric versus asymmetric images. The proposed approach aims to improve on existing techniques for intensity-based image registration tasks.
A Survey on Image Retrieval By Different Features and TechniquesIRJET Journal
This document discusses various techniques for content-based image retrieval. It begins with an introduction to content-based image retrieval and describes how it uses visual features like color, texture, shape and regions to index and represent image content for retrieval. The document then reviews related work on image retrieval using different features. It discusses features used for image identification like color, edges, corners and texture. The document also outlines techniques for image retrieval including relevance feedback, support vector machines, block truncation coding, and image clustering. Finally, it evaluates parameters for comparing image retrieval algorithms.
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
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.
IRJET- Shape based Image Classification using Geometric –PropertiesIRJET Journal
This document discusses shape-based image classification using geometric properties. It proposes classifying shapes based on extracting geometric properties like area, perimeter, circularity, and eccentricity. The Discrete Wavelet Transform is used to remove noise and compress images. Then a K-Nearest Neighbor classifier is used to classify objects like squares, circles, ellipses and rectangles. The method is evaluated on the MPEG-7 dataset and achieves a maximum accuracy. Geometric properties provide powerful representations for shape recognition in content-based image retrieval applications.
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.
Multibiometric systems are expected to be more reliable than unimodal biometric systems for personal identification due to the presence of multiple, fairly independent pieces of evidence e.g. Unique Identification Project "Aadhaar" of Government of India. In this paper, we present a novel wavelet based technique to perform fusion at the feature level and score level by considering two biometric modalities, face and fingerprint. The results indicate that the proposed technique can lead to substantial improvement in multimodal matching performance. The proposed technique is simple because of no preprocessing of raw biometric traits as well as no feature and score normalization.
The document proposes a new feature descriptor called Local Bit-Plane Wavelet Pattern (LBWP) to improve content-based retrieval of biomedical images like CT and MRI scans. LBWP encodes relationships between pixel intensities in different bit planes and applies a wavelet function, capturing more fine-grained image details than prior methods. Evaluation on a dataset from The Cancer Imaging Archive showed LBWP outperformed existing approaches like Local Wavelet Pattern with higher average retrieval precision, rate, and F-score.
The document summarizes a novel approach for multisensor biometric fusion of face and palmprint images using wavelet decomposition and SIFT features for person authentication. Face and palmprint images are decomposed using wavelets and fused to create an enhanced fused image. SIFT features are extracted from the fused image and used for matching based on a monotonic-decreasing graph approach. Experimental results on a 150 person database show the proposed fusion method achieves 98.19% accuracy, outperforming individual face and palmprint recognition.
Application of Image Retrieval Techniques to Understand Evolving Weatherijsrd.com
Multispectral satellite images provide valuable information to understand the evolution of various weather systems such as tropical cyclones, shifting of intra tropical convergence zone, moments of various troughs etc., accurate prediction and estimation will save live and property. This work will deal with the development of an application which will enable users to search an image from database using either gray level, texture and shape features for meteorological satellite image retrieval .Gray level feature is extracted using histogram method. The Texture feature is extracted using gray level co-occurrence method and wavelet approach. The shape feature vector is extracted using morphological operations. The similarity between query image and database images is calculated using Euclidian distance. The performance of the system is evaluated using precision
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
The biometric is used to identify a person effectively and employ in almost all applications of day to day
activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform
(DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person
into one image using averaging technique is introduced to reduce execution time and memory. The DWT is
applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients
are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
This document reviews techniques for multi-image morphing. It discusses early cross-dissolve morphing methods and their limitations. Mesh warping and field morphing are introduced as improved techniques that use control points and line mappings to better align images during transition. The document also summarizes point distribution, critical point filters, and other common morphing methods. It concludes by noting that effective morphing requires mechanisms for feature specification, warp generation, and transition control.
Face skin color based recognition using local spectral and gray scale featureseSAT Journals
Abstract Human face conveys more information about identity of person. Human face recognition is one of the most challenging problem and it can be used in many applications at different security places in airports, defense and banking sectors etc.In this work used colored features obtained from color segmentation because in real time scenario color provides the more information than gray scale image but it has a drawback. To overcome this drawback gray scale feature extracted from co-occurrence matrix of an image and for efficient face recognition of human Face under different illumination conditions spectral features can be extracted from face texture. These three feature vectors concatenated into a single feature vector and applied Lenc-Kral matching technique to measure similarity between the database and query image, the similarity is high then face is recognized. Keywords: Face recognition, illumination condition, local texture features, color segmentation.
A Powerful Automated Image Indexing and Retrieval Tool for Social Media SampleIRJET Journal
The document proposes an automated image indexing and retrieval tool for social media images. It uses local binary patterns for face detection and matching global self-similarities for background detection. This allows identifying similar images to reduce sending redundant photos. The method segments faces from backgrounds, extracts features using local binary patterns, and indexes images based on faces and backgrounds. Experimental results show this compressed method significantly reduces the number and size of images sent by identifying and removing redundant images.
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.
Medical Image Fusion Using Discrete Wavelet TransformIJERA Editor
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multimodal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. The domain where image fusion is readily used nowadays is in medical diagnostics to fuse medical images such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and MRA. This paper aims to present a new algorithm to improve the quality of multimodality medical image fusion using Discrete Wavelet Transform (DWT) approach. Discrete Wavelet transform has been implemented using different fusion techniques including pixel averaging, maximum minimum and minimum maximum methods for medical image fusion. Performance of fusion is calculated on the basis of PSNR, MSE and the total processing time and the results demonstrate the effectiveness of fusion scheme based on wavelet transform.
V. Karthikeyan proposes a novel histogram-based image registration technique. The method segments images using multiple histogram thresholds to extract objects. Extracted objects are characterized by attributes like area, axis ratio, and fractal dimension. Objects between images are matched to estimate rotation and translation. The technique was tested on pairs of images with different rotations and translations and achieved sub-pixel accuracy in registration. The method outperformed other techniques like SIFT for remote sensing images. Future work could optimize the segmentation and apply the technique to multispectral images.
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...IOSR Journals
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
Spectral approach to image projection with cubiciaemedu
This document summarizes a research paper that proposes a new method for image projection using spectral interpolation with cubic B-spline interpolation. The key points are:
1) Existing super resolution methods based on Fourier transforms have limitations in providing high visual quality when scaling images.
2) The proposed method first transforms image frames into the frequency domain using FFT. It then interpolates in the spectral domain using cubic B-spline interpolation before projecting the interpolated data onto a high resolution grid.
3) Experimental results on a test video sequence show the proposed method provides higher visual quality compared to conventional Fourier-based approaches, while also having faster computation time.
This document provides an overview of image feature detection, description, and matching techniques. It discusses the importance of these techniques for various computer vision applications and defines key concepts like local vs global features. The document also describes important properties of feature detectors like robustness and repeatability. It explains scale and affine invariance and provides examples of single-scale, multi-scale, and affine invariant feature detectors. Finally, it discusses evaluation of feature detection and description algorithms.
Similar to Features for Cross Spectral Image Matching: A Survey (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Plant leaf identification system using convolutional neural networkjournalBEEI
This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Understanding the role of individual learner in adaptive and personalized e-l...journalBEEI
Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
1) Researchers developed a prototype contactless transaction system using QR codes and digital payments to support physical distancing during the COVID-19 pandemic in traditional markets.
2) The system allows sellers and buyers in traditional markets to conduct fast, secure transactions via smartphones without direct cash exchange. Buyers scan sellers' QR codes to view product details and make e-wallet payments.
3) Testing showed the system's functions worked properly and users found it easy to use and useful for supporting contactless transactions and digital transformation of traditional markets. However, further development is needed to increase trust in digital payments for users unfamiliar with the technology.
Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
This document describes the implementation of a double-layer structure on an octagon microstrip yagi antenna (OMYA) to improve its performance at 5.8 GHz. The double-layer consists of two double positive (DPS) substrates placed above the OMYA. Simulation and experimental results show that the double-layer configuration increases the gain of the OMYA by 2.5 dB compared to without the double-layer. The measured bandwidth of the OMYA with double-layer is 14.6%, indicating the double-layer can increase both the gain and bandwidth of the OMYA.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
In this paper, we study uplink-downlink non-orthogonal multiple access (NOMA) systems by considering the secure performance at the physical layer. In the considered system model, the base station acts a relay to allow two users at the left side communicate with two users at the right side. By considering imperfect channel state information (CSI), the secure performance need be studied since an eavesdropper wants to overhear signals processed at the downlink. To provide secure performance metric, we derive exact expressions of secrecy outage probability (SOP) and and evaluating the impacts of main parameters on SOP metric. The important finding is that we can achieve the higher secrecy performance at high signal to noise ratio (SNR). Moreover, the numerical results demonstrate that the SOP tends to a constant at high SNR. Finally, our results show that the power allocation factors, target rates are main factors affecting to the secrecy performance of considered uplink-downlink NOMA systems.
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Key performance requirement of future next wireless networks (6G)journalBEEI
The document provides an overview of the key performance indicators (KPIs) for 6G wireless networks compared to 5G networks. Some of the major KPIs discussed for 6G include: achieving data rates of up to 1 Tbps and individual user data rates up to 100 Gbps; reducing latency below 10 milliseconds; supporting up to 10 million connected devices per square kilometer; improving spectral efficiency by up to 100 times through technologies like terahertz communications and smart surfaces; and achieving an energy efficiency of 1 pico-joule per bit transmitted through techniques like wireless power transmission and energy harvesting. The document outlines how 6G aims to integrate terrestrial, aerial and maritime communications into a single network to provide ubiquitous connectivity with higher
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
Modeling climate phenomenon with software grids analysis and display system i...journalBEEI
This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.
An approach of re-organizing input dataset to enhance the quality of emotion ...journalBEEI
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Jain [12] exploiting Histogram of Oriented Gradient (HOG) integrated with Local Binary Pattern (LBP) to
describe the structure of the face in NIR and VL images. Experimental result showed that HOG and LBP
have better performance in representing NIR and VIS images.
A survey on state of the art feature in cross-spectral image matching is carried out in this paper. We
describe several features based on the representation of VL and thermal image features. The main
contribution of this paper is to provide a brief description about features in cross-spectral image matching so
that it can help in selecting the most suitable feature used in the face and iris recognition. This paper is
organized as follows. Section 2 describes basic background concepts in cross-spectral matching and the
feature extraction, such as the definition of cross-spectral image matching, and block diagram matching
process in the cross-spectral domain. Section 3 reviews the properties and characteristics of the VL and the
thermal image features. Also, how to extracts features from the VL image and the NIR images for cross
spectral matching are described in section 3. Section 4 reviews the robust feature used in cross-spectral
matching including the feature extraction process. Conclusions are summarized in section 5.
2. CROSS SPECTRAL MATCHING
Cross spectral matching represents the ability to recognize the objects presented in two different
modalities. Cross spectral matching is illustrated in Figure 1. First, pre-processing was applied to prepare an
image for further processing. The pre-processing step comprises image cropping, photometric and geometric
normalization, and restoration. Next step is feature extraction which is carried out by taking unique features
of thermal and VL image by using certain descriptors. The results of this process are used as inputs to the
matching step using matching or classification algorithms.
Figure 1. Cross spectral matching
3. VISIBLE AND THERMAL IMAGES FEATURES
Image features are unique attributes of both numerical and alphanumerical that represent
information about the content of a digital image. Numerical representation means any physical information of
an image, such as heat, color, pressure, range, non visible wavelength, etc. replaced by numerical values that
make it easier to be analyzed in various applications. In VL and thermal images, a feature can be either an
image visual characteristics or interpretative response to the spatial, symbolic, semantic, or emotional image
characteristics [13].
Feature extraction is an initial stage for all applications in image analysis. Feature extraction is
known as the process of getting the unique characteristics of an image that distinguish one image with
another. Feature extraction is used to indicate the relevant information of an image in completing the
computational tasks associated with a particular application [14]. Feature extraction process for VL and
thermal images are described in Figure 2.
In cross spectral matching, performance and matching accuracy depends on proper feature
extraction. VL and thermal images are retrieved based on the value of the vector feature, therefore, feature
selection and feature extraction process are important to be considered. Feature selection is defined as a
process of selecting the best features that suitable for a particular application [15].
The feature selection based on the visual properties of the image is shown in Table 1. Low level is
defined as the image features can be extracted directly without considering the spatial relationship. While
high-level concerns the spatial information. The feature selection process must consider [16]:
Pre-
processing
Feature
Extraction
Probe
Pre-
processing
Feature
Extraction
Matching Result
Gallery
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a. Similarities between two matched images, if the images similar the feature distance between this image
is small. We can confirm that if VL and thermal images are similar the distance of feature vector of
those two image is small.
b. Computational task is not complex.
c. Small feature dimensions do not affect the matching efficiency.
Figure 2. Representation of image features
Table 1. Visual properties of visible and thermal images
Low level Middle level High level
Color
Shape
Texture
Regions
Spatial relationship
Localization
Image categorization
Objects identification
d. The size of the dataset does not affect matching performance.
e. Robust against variation illuminations and geometric transformation.
Figure 3 presents several methods of feature extraction classification for cross spectral matching.
Feature extraction methods divided into:
a. Structural methods : this method identifies structural features of an image. Feature calculated based on
topological and geometric properties [17].
b. Statistical methods : identifies statistical features of an image based on statistical distributions of
pixels [17].
Visible
Images
Visual properties
of VL images
Numerical
representation
Feature
extraction
Feature
vector
Thermal
Images
Properties of
thermal images
Numerical
representation
Feature
Extraction
Feature
vector
Matching
Indexing/Classify
Similarity rate
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c. Spectrum or transform based : feature calculated over spectral composition, low and high frequencies of
an image. They can further be devided into : spatial, frequencies, and combined transform [14].
d. Block based : based on images block. This method combines the important feature of image into one
block, consequently size and number of blocks affect the resulting features [14].
e. Combined methods : feature calculated by concatenated several individual methods to provide greater
ability [14].
Figure 3. Classification of features extraction methods for cross spectral image
4. STATE OF THE ART FEATURE DESCRIPTORS
In this section, we present some popular descriptors used in cross spectral matching. These
descriptors frequently use because robust in describing VL and thermal image. The descriptors commonly
used in the cross spectral matching are presented in Table 2.
4.1. Difference of Gaussian Filter
The Difference of Gaussian (DoG) is a bandpass filter that used Gaussians filter to produce a
normalized image [2]. DoG can suppress variations of noise and aliasing on the cross-spectral image caused
by the frequency difference between VL and thermal images. Also, the DOG has a low computational
complexity and popularly used in publications [18], [19].
DOG can accommodate difficult lighting condition by setting inner dan outer Gaussian values. For
strong lighting variations datasets, the recognition gives the best result at outer Gaussian ≈ 2 pixels and up to
about 4 for datasets less extreme lighting variations while the inner Gaussian is quite narrow at 1 pixel [20].
Band-pass filter is obtained by subtracting two Gaussians with different σ that eliminating all
frequencies between frequencies cut-off of the two Gaussian. The image feature is located between this
frequency band was extracted.
The DOG feature extractions consists of three stages [20]:
a. The input image is convolving with two Gaussian kernels having differing standard deviations as shown
in (1) to produce a blurred image.
b. Next two blurred images version is subtracted each other to obtain a normalized image.
Fourier
Transform
Statistical
Methods
Local Binary
Pattern
(LBP)
Structural /
Geometric
Histogram of
Oriented
Gradient
(HOG)
Combined
Methods
Log
Gabor
Wavelet
Scale
Invariant
Feature
Transform
(SIFT)
Gabor
Local
Binary
Pattern
(GLBP)
Spatial Frequencies
Spectrum Based
Cumm
ulative
Sum
based
Fractal
and Chaos
Theory
Color
Texture
Feature
Gabor
Transform
Wavelet
Transform
Discrete
Cosine
Transform
Cross Spectral Features Extraction Methods
Binarized
Statistical
Image
Features
(BSIF)
Block
Based
Additive
Fusion
Combined
Transform
Shift
Invariant
(SIFE)
Fourier
Mellin
Radon
Transform
Local Ternary
Pattern (LTP)
Pyramid
Histogram
of
Oriented
Gradient
(PHOG)
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c. To construct a band-pass filter, the values of δ0 must be smaller than δ1 which set to (1) and
(2) respectively
( ) [ ( ) ( )] ( ) (1)
Where : ( ) : original image
( ) : blurred image
: Gaussian kernel function defined as :
( )
√
( )⁄
(2)
Table 2. Various descriptors in cross spectral matching
Publications Domain Modality Applications Feature Extraction Technique
[2] NIR vs. VL Iris recognition Real valued -Log Gabor Phase
[4] NIR vs. VL Face recognition LBP, DLBP
[10] NIR vs. VL Iris recognition DCT, Gabor wavelet
[11] NIR vs. VL Iris recognition Binarized Statistical Image Feature
[12] NIR vs. VL Face recognition uniform LBP and HOG
[16] NIR vs. VL Face recognition Log DoG LBP and Log DoG HOG
[19] NIR vs. VL Face recognition DoG, Center Surround Divisive Normalization
(CSDN), SIFT (Scale Invariant Feature Transform),
and Multiscale Local Binary Pattern (MLBP)
[21] NIR vs. VL, SWIR
vs. VL
Face recognition LBP and
Generalized LBP (GLBP)
[22] SWIR vs. VL Face recognition LBP and LTP
[23] NIR vs. VL Face recognition Directional binary code
[24] Thermal vs. VL Face recognition Pyramid Histogram of Oriented Gradient (PHOG),
PSFIT
[25] NIR vs. VL Face recognition Multi resolution LBP
[26] MWIR vs. VL
LWIR vs. VL
Face recognition Canonical Correlation Analysis (CCA)
[27] NIR vs. VL Face recognition Wavelet transform
[28] NIR vs. VL Face recognition Sparse dictionary
[29] SWIR vs. VL Face recognition Gabor filter, LBP, Simplified Weber Local
Descriptor (SWLD) and GLBP
[30] Thermal vs. VL Face recognition Deep Perceptual Mapping (DPM)
[31] SWIR vs. VL Face recognition Contrast Limited Adaptive Histogram Equalization
(CLAHE)
[32] NIR vs. VL Face recognition Gaussian filter and SIFT
4.2. Local Binary Pattern
At 1996, Local Binary Pattern (LBP) was first introduced by Ojala et al. [33]. The LBP operator is
a texture descriptor gray-scale invariant that analyzes the texture of an image based its texture spectrum
called Texture Unit (TU).
Texture spectrum is a distribution of texture units happening in a region. Originally, LBP uses 3x3
neighborhood and generates 8-bit code based on the number of 8 pixels around the center pixel. Texture unit
has 28=256 possibility histogram bin in describing the spatial pattern in a 3x3 neighborhood. They compute
by multiplying the weights of the corresponding pixel with the values of the pixels in the previous threshold
neighborhood. Then the pixel value of this neighborhood summed resulting the number of (169) texture unit
[34]. The LBP operator defined as:
( ) ∑ ( ) (3)
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Where ( ) {
c, n represents a central pixel and 8 neighbors of the central pixel respectively.
The 3x3 neighbor's pixel is thresholding by center pixel. If the neighbor's pixel greater than center
pixel then the value is 1, and 0 otherwise. LBP operators further developed to accommodate variations of
texture scaling into circular neighborhoods (8.1), (16.2) and (8.2).
4.3. Binary Statistical Image Feature
Binary Statistical Image Feature (BSIF) is a texture descriptor which is inspired by Local Binary
pattern (LBP). BSIF uses a binary code to represent each pixel neighborhood in an image [35]. The binarized
feature is generated by convolving image with a set of linear filter. Thus, the response of a linear filter is
binarized with a threshold at zero. To construct the linear filter, independent component analysis (ICA) is
used. The statistical independence of the filter responses is maximizing by ICA from a training set of natural
image patches. The BSIF extraction process as described in [36]:
a. First, the response of the linear filter is constructed. Let X is image patch with size l x l pixels. is a
linear filter and represents the response of the filter.
∑ ( ) ( ) (4)
b. Binarized feature is obtained by :
{ (5)
4.4. Scale Invariant Feature Transform
The Scale Invariant Feature Transform (SIFT) is a robust descriptor that combinations of DoG
interest region detector with a corresponding feature descriptor. SIFT encodes image information in a
localized set of gradient orientation histograms. Thus, SIFT can accommodates illumination variations and
small positional shift [37].
4.5. Histogram of Oriented Gradient
Histogram of Oriented Gradient (HOG) also known as histogram normalization due to its ability to
normalize from local responses [38]. HOG is adopted human detection and used for object detection
applications. HOG resistant of shadowing process, illumination invariant, and reliable to photometric
variations. HOG does not have spatial image representation, therefore, HOG only computes the individual
pixel energy regardless spatial distribution of an image. The feature extraction process [39]:
a. The image is divided into a small area called cell (8x8 pixels).
b. Calculate the magnitude of the pixel orientation.
c. Interpolation of result no 2 into histogram orientation bin 20 degrees.
d. Cells are grouped into overlapping blocks.
e. Then overlapping blocks are normalized.
f. Finally the normalized histograms are concatenated resulted in vector features.
4.6. Additive Fusion Block Based
Additive fusion block based proposed by Varadarajan et al. that used block-based extraction process
[40]. Block based can maintain the optimum number of features that need to be extracted. Size and number of
blocks affect the resulting features in this techniques. The more blocks, the less feature is extracted because
of, the smaller block size causing the resultant block reduction. The ideal block sizes are 4,8, or 16 pixels.
Feature extraction process consists:
a. Image is divided into individual blocks of size 4.8, or 16 pixels
b. Each block is applied Chirp Z-Transform (CZT) and Goertzel algorithm for preprocessing and image
enhancement.
c. Individual blocks are summed to yield a single resultant block that contains the essential features of
each block.
d. This resultant block is then become a vector feature.
4.7. Discrete Cosine Transform
Discrete Cosine Transform (DCT) transforms spatial image information into frequency domain.
DCT consists cosine part of Fourier transform models [41]. DCT concentrates energy image into some DCT
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coefficients (energy compaction). Signal energy is concentrated on large DCT coefficient magnitudes (called
low frequencies) and located in the upper-left corner of the DCT array.
Low frequencies coefficients contain most of an essential image information. Therefore the original
image can be reconstructed only by using low frequencies coefficients. While less important information is
located in lower-right values of the DCT array (called high frequencies). This high-frequency coefficients
can be discharged through the quantization process without significantly affecting the image quality [42-43].
DCT feature extraction process [44]:
a. An original image is divided into 8x8 blocks.
b. Calculated DCT coefficients using (4) resulted in DCT coefficient arrays.
c. Quantized.the DCT coefficients.
d. The value that is in upper-left corner of the DCT is used as a feature vector (low frequency).
( ) ( ) ( ) ∑ ∑ [ ( )] [ ( )] ( )
(6)
( ) ( )
{
√
√
Where ; and ( ) represents intensity of the pixel in row x and
column y [45].
4.8. Radon Transform
Radon transform is one of the transformations that can enhance low-frequency components by
describing the integral line of an image. Radon transform is turned rotation into translation and often used in
face recognition. Radon feature extraction process [46] :
a. Calculated radon space using (5).
b. Discriminative information located in radon space which computes using projections for 0°–179°
orientations.
c. Calculated DCT of radon space to obtain feature frequencies.
d. Concatenated 25% of DCT coefficients resulted in feature vector.
( )[ ( )] ∫ ∫ ( ) ( ) (7)
Where ( ) represents Dirac function, perpendicular distance of a line from the origin represented r
ϵ[ ] and ϴ is an angle between X-axis and distance vector.
5. CONCLUSIONS
The literature relevant cross spectral matching is overgrowing, and many researchers have proposed
a cross spectral matching framework with better matching performance. This survey presents an overview
feature of thermal and visible images. Also, brief descriptions the current state of the art feature extraction
methods for cross spectral matching. The image features affect the performance of cross spectral matching.
Therefore, the selection of feature extraction methods that suitable for an application becomes essential issue.
Also, the researchers must consider visual properties of VL and thermal images.
ACKNOWLEDGEMENTS
This research was supported by the Ministry of Research, Technology, and Higher Education of the
Republic of Indonesia, under the scheme, namely PMDSU.
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BIOGRAPHIES OF AUTHORS
Maulisa Oktiana received B.Eng degree in electrical engineering from Syiah Kuala University,
Banda Aceh, Indonesia, in 2013. She is currently a doctoral student at Graduate Program of
Engineering, Syiah Kuala University. Her research interests include image processing and
computer vision.
Fitri Arnia received B. Eng degree from Universitas Sumatera Utara (USU), Medan in 1997. She
finished her master and doctoral degree from Universsity of New South Wales (UNSW),
Sydney, Australia and Tokyo Metropolitan University, Japan in 2004 and 2008 respectively. She
has been with the Department of Electrical Engineering, Faculty of Engineering, Syiah Kuala
University since 1999. Dr. Arnia was a visiting scholar in Tokyo Metropolitan University
(TMU), Tokyo, Japan in 2013 and Suleyman Demirel University (SDU), Isparta, Turkey in
2017. She is a member of IEEE and APSIPA. Her research interests are signal, image and
multimedia information processing.
Yuwaldi Away received his B. Eng degree in Electrical-Computer Engineering from Sepuluh
Nopember Institute of Technology Surabaya, Indonesia in 1964, the M.Sc from Bandung
Institute of Technology (ITB) Bandung, Indonesia. He obtained his Ph.D. degrees in computer
technology from the National University of Malaysia. Since 1990, he joined Syiah Kuala
University as a teaching staff, and from 2007 until now he has been appointed as a Professor in
Electrical Engineering. His current research interest include microprocessor based system,
embedded system, FGPA, optimation and visualization.
Khairul Munadi received the B.E. degree from Sepuluh Nopember Institute of Technology,
Surabaya, Indonesia, in 1996, and the M.Eng. and Ph.D. degrees from Tokyo
MetropolitanUniversity (TMU), Japan, in 2004 and 2007 respectively, all in electrical
engineering. From1996 to 1999, he was with Alcatel Indonesia as a system engineer. Since 1999,
he joined SyiahKuala University, Banda Aceh, Indonesia, as a lecturer at the Electrical
Engineering Department.He was a visiting researcher at the Information and Communication
Systems Engineering,Faculty of System Design, TMU, Japan, from March 2007 to March 2008.
His research interests include multimedia signal processing and communications.