Signal & Image Processing : An International Journal (SIPIJ) - WJCI Indexed - Profile on Academia.edu (original) (raw)

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Papers by Signal & Image Processing : An International Journal (SIPIJ) - WJCI Indexed

SIPIJ, 2011

The major threat in cyber crime for digital forensic examiner is to identify, analyze and interpr... more The major threat in cyber crime for digital forensic examiner is to identify, analyze and interpret the concealed information inside digital medium such as image, audio and video. There are strong indications that hiding information inside digital medium has been used for planning criminal activities. In this way, it is important to develop a steganalysis technique which detects the existence of hidden messages inside digital medium. This paper focuses on universal image steganalysis method which uses RGB to HSI colour model conversion. Any Universal Steganalysis algorithm developed should be tested with various stegoimages to prove its efficiency. The developed Universal Steganalysis algorithm is tested in stego-image database which is obtained by implementing various RGB Least Significant Bit Steganographic algorithms. Though there are many stego-image sources available on the internet it lacks in the information such as how many rows has been infected by the steganography algorithms, how many bits have been modified and which channel has been affected. These parameters are important for Steganalysis algorithms and it helps to rate its efficiency. Proposed Steganalysis using Colour Model has been tested with our Image Database and the results were affirmative.

SIPIJ, 2011

Sindhi language is primarily spoken in the Sindh province of Pakistan, and in some parts of India... more Sindhi language is primarily spoken in the Sindh province of Pakistan, and in some parts of India. Languages phonemic inventory include vowels, consonants and diphthongs. This paper presents acoustic analysis and properties of the glide consonants of Sindhi. Glides are considered having stable and predictable formant structure and associated acoustic properties like vowels and diphthongs. Understanding the corresponding acoustic similarities, differences and relationship between three types of these sounds is the subject of discussion of this paper.

Histopathological images are widely used to diagnose diseases including skin cancer. As digital h... more Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.

This study involves intra-and inter-individual emotion classifications from psychophysiological s... more This study involves intra-and inter-individual emotion classifications from psychophysiological signals and subgroup analysis on the influence of gender and age and their interaction on the emotion recognition. Individual classifications are conducted using a selection of feature optimization, classification and evaluation approaches. The subgroup analysis is based on the inter-individual classification. Emotion elicitation is conducted using standardized pictures in the Valence-Arousal-Dominance dimensions and affective states are classified into five different category classes. Advantageous intra-individual rates are obtained via multi-channel classification and the respiration best contributes to the recognition. High inter-individual variances are obtained showing large variability in physiological responses between the subjects. Classification rates are significantly higher for women than for men for the 3-category-class of Valence. Compared to old subjects, young subjects have significantly higher rates for the 3-category-class and 2-category-class of Dominance. Moreover, young men's classification performed the best among the other subgroups for the 5-category-class of Valence/Arousal.

Color Image segmentation splits an image into modules, with high correlation among objects contai... more Color Image segmentation splits an image into modules, with high correlation among objects contained in the image. Many color image segmentation algorithms in the literature, segment an image on the basis of color, texture and as a combination of both color and texture. In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the Sequential Minimal Optimization-Support Vector Machine (SMO-SVM) classifier for segmentation. Gabor filter decomposition is used for extracting the textural features and homogeneity model is used for obtaining the color features. The SMO-SVM is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set based membership functions efficiently handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets effectively deal with uncertainty, vagueness, and incompleteness in data. Parameterization tools are not necessary in defining Soft set theory. The goodness aspects of soft sets, rough sets and fuzzy sets are integrated in the proposed algorithm to achieve improved segmentation performance. The proposed algorithm is comparable and achieved better performance compared with the state of the art algorithms found in the literature.

This paper studies the respective performance of minimax search and endgame databases in competin... more This paper studies the respective performance of minimax search and endgame databases in competing against the Awale shareware. It also investigates the performance of combining both techniques to evolve a hybrid player against the Awale shareware.

This research work presents the comparison analysis of Shunt Active Filter (SAF) with Parallel Hy... more This research work presents the comparison analysis of Shunt Active Filter (SAF) with Parallel Hybrid Active Filter (PHAF). The performances of SAF and PHAF have been analyzed with PI controllers based on Synchronous Reference Frame theory. In each case, simulation is carried out for three phase unbalanced non liner load conditions. The advantages of the designed system and the proposed current reference calculation methods are verified by simulations using MATLAB Power system Toolbox.

In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously s... more In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases may not appear to contain valuable relational information but practically such a relation exists. The large number of process parameter values are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for the expert system. With the changes in parameters there is a quite high possibility to form new rules using the dynamics of the process itself. We present an efficient algorithm that generates all significant rules based on the real data. The association based algorithms were compared and the best suited algorithm for this process application was selected. The application for the Learning system is studied in a Power Plant domain. The SCADA interface was developed to acquire online plant data.

Segmentation of organs from medical images is an active and interesting area of research. Liver s... more Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our experimental results confirm that the method yields a satisfactory overall segmentation outcome. Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.

The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue ... more The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue characterization, presenting an interest in the follow-up of various pathologies such as the multiple sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is proposed; finally the last section is organized around an experimental part allowing the study of the behavior of our model on textured images. In the aim to validate our model, different segmentations were down on pathological Brain MRI, the obtained results have been compared to the results obtained by another models. This results show the effectiveness and the robustness of the suggested approach.

This paper presents a binarization method for camera based natural scene (NS) images based on edg... more This paper presents a binarization method for camera based natural scene (NS) images based on edge analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes. The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are compared with other standard techniques. The method is fast and works well for camera based natural scene images.

Vehicle detection and tracking applications play an important role for civilian and military appl... more Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems.

In this paper, we aim to determine the location information of a node deployed in Wireless Sensor... more In this paper, we aim to determine the location information of a node deployed in Wireless Sensor Networks (WSN). We estimate the position of an unknown source node using localization based on linear approach on a single simulation platform. The Cramer Rao Lower Bound (CRLB) for position estimate is derived first and the four linear approaches namely Linear Least Squares (LLS), Subspace Approach (SA), Weighted Linear Least Squares (WLLS) and Two-step WLS have been derived and presented. Based on the simulation study the results have been compared. The simulation results show that the Two- step WLS approach is having higher localization accuracy.

Modern face recognition systems are vulnerable to spoofing attack. Spoofing attack occurs when a ... more Modern face recognition systems are vulnerable to spoofing attack. Spoofing attack occurs when a person tries to cheat the system by presenting fake biometric data gaining unlawful access. A lot of researchers have originated novel techniques to fascinate these types of face tampering attack. It seems that no comparative studies of different face recognition algorithms on same protocols and fake data have been incorporated. The motivation behind this paper is to present the effect of face tampering on various categories of face recognition algorithms. For this purpose four categories of facial recognition algorithms have been selected to present the obtained results in the form of facial identification accuracy at various tampering and experimental protocols but obtained results are very fluctuating in nature. Finally, we come to the conclusion that it is totally unpredictable to select particular type of algorithm for tampered face recognition.

The goal of this paper is to investigate the speech signal enhancement using Kernel Affine Proje... more The goal of this paper is to investigate the speech signal enhancement using Kernel Affine Projection Algorithm (KAPA ) and Normalized KAPA. The removal of background noise is very important in many applications like speech recognition, telephone conversations, hearing aids, forensic, etc. Kernel adaptive filters shown good performance for removal of noise. If the evaluation of background noise is more slowly than the speech, i.e., noise signal is more stationary than the speech, we can easily estimate the noise during the pauses in speech. Otherwise it is more difficult to estimate the noise which results in degradation of speech. In order to improve the quality and intelligibility of speech, unlike time and frequency domains, we can process the signal in new domain like Reproducing Kernel Hilbert Space (RKHS) for high dimensional to yield more powerful nonlinear extensions. For experiments, we have used the database of noisy speech corpus (NOIZEUS). From the results, we observed the removal noise in RKHS has great performance in signal to noise ratio values in comparison with conventional adaptive filters.

Image segmentation is one of the important tasks in computer vision and image processing. Thresho... more Image segmentation is one of the important tasks in computer vision and image processing. Thresholding is a simple but most effective technique in segmentation. It based on classify image pixels into object and background depended on the relation between the gray level value of the pixels and the threshold. Otsu technique is a robust and fast thresholding techniques for most real world images with regard to uniformity and shape measures. Otsu technique splits the object from the background by increasing the separability factor between the classes. Our aim form this work is (1) making a comparison among five thresholding techniques (Otsu technique, valley emphasis technique, neighborhood valley emphasis technique, variance and intensity contrast technique, and variance discrepancy technique)on different applications. (2) determining the best thresholding technique that extracted the object from the background. Our experimental results ensure that every thresholding technique has shown a superior level of performance on specific type of bimodal images.

After several decades of research, the development of an effective feature extraction method for ... more After several decades of research, the development of an effective feature extraction method for texture classification is still an ongoing effort. Therefore , several techniques have been proposed to resolve such problems. In this paper a novel composite texture classification method based on innovative pre-processing techniques, skeletonization and Regional moments (RM) is proposed. This proposed texture classification approach, takes into account the ambiguity brought in by noise and the different caption and digitization processes. To offer better classification rate, innovative pre-processing methods are applied on various texture images first. Pre-processing mechanisms describe various methods of converting a grey level image into binary image with minimal consideration of the noise model. Then shape features are evaluated using RM on the proposed Morphological Skeleton (MS) method by suitable numerical characterization measures for a precise classification. This texture classification study using MS and RM has given a good performance. Good classification result is achieved from a single region moment RM10 while others failed in classification.

An edge may be defined as a set of connected pixels that forms a boundary between two disjoints r... more An edge may be defined as a set of connected pixels that forms a boundary between two disjoints regions. Edge detection is basically, a method of segmenting an image into regions of discontinuity. Edge detection plays an important role in digital image processing and practical aspects of our life. .In this paper we studied various edge detection techniques as Prewitt, Robert, Sobel, Marr Hildrith and Canny operators. On comparing them we can see that canny edge detector performs better than all other edge detectors on various aspects such as it is adaptive in nature, performs better for noisy image, gives sharp edges , low probability of detecting false edges etc

Researches based on animal detection plays a very vital role in many real life applications. Appl... more Researches based on animal detection plays a very vital role in many real life applications. Applications which are very important are preventing animal vehicle collision on roads, preventing dangerous animal intrusion in residential area, knowing locomotive behavioural of targeted animal and many more. There are limited areas of research related to animal detection. In this paper we will discuss some of these areas for detection of animals.

Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processi... more Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processing. VQA reflects the quality of a video as most people commonly perceive. This paper proposes a reducedreference mobile VQA, in which one-dimensional (1-D) motion vector (MV) distributions are used as features of videos. This paper focuses on reduction of data size using Laplacian modeling of MV distributions because network resource is restricted in the case of mobile video. The proposed method is more efficient than the conventional methods in view of the computation time, because the proposed quality metric decodes MVs directly from video stream in the parsing process rather than reconstructing the distorted video at a receiver. Moreover, in view of data size, the proposed method is efficient because a sender transmits only 28 parameters. We adopt the Laplacian distribution for modeling 1-D MV histograms. 1-D MV histograms accumulated over the whole video sequences are used, which is different from the conventional methods that assess each image frame independently. For testing the similarity between MV histogram of reference and distorted videos and for minimizing the fitting error in Laplacian modeling process, we use the chi-square method. To show the effectiveness of our proposed method, we compare the proposed method with the conventional methods with coded video clips, which are coded under varying bit rate, image size, and frame rate by H.263 and H.264/AVC. Experimental results show that the proposed method gives the performance comparable with the conventional methods, especially, the proposed method requires much lower transmission data.

SIPIJ, 2011

The major threat in cyber crime for digital forensic examiner is to identify, analyze and interpr... more The major threat in cyber crime for digital forensic examiner is to identify, analyze and interpret the concealed information inside digital medium such as image, audio and video. There are strong indications that hiding information inside digital medium has been used for planning criminal activities. In this way, it is important to develop a steganalysis technique which detects the existence of hidden messages inside digital medium. This paper focuses on universal image steganalysis method which uses RGB to HSI colour model conversion. Any Universal Steganalysis algorithm developed should be tested with various stegoimages to prove its efficiency. The developed Universal Steganalysis algorithm is tested in stego-image database which is obtained by implementing various RGB Least Significant Bit Steganographic algorithms. Though there are many stego-image sources available on the internet it lacks in the information such as how many rows has been infected by the steganography algorithms, how many bits have been modified and which channel has been affected. These parameters are important for Steganalysis algorithms and it helps to rate its efficiency. Proposed Steganalysis using Colour Model has been tested with our Image Database and the results were affirmative.

SIPIJ, 2011

Sindhi language is primarily spoken in the Sindh province of Pakistan, and in some parts of India... more Sindhi language is primarily spoken in the Sindh province of Pakistan, and in some parts of India. Languages phonemic inventory include vowels, consonants and diphthongs. This paper presents acoustic analysis and properties of the glide consonants of Sindhi. Glides are considered having stable and predictable formant structure and associated acoustic properties like vowels and diphthongs. Understanding the corresponding acoustic similarities, differences and relationship between three types of these sounds is the subject of discussion of this paper.

Histopathological images are widely used to diagnose diseases including skin cancer. As digital h... more Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.

This study involves intra-and inter-individual emotion classifications from psychophysiological s... more This study involves intra-and inter-individual emotion classifications from psychophysiological signals and subgroup analysis on the influence of gender and age and their interaction on the emotion recognition. Individual classifications are conducted using a selection of feature optimization, classification and evaluation approaches. The subgroup analysis is based on the inter-individual classification. Emotion elicitation is conducted using standardized pictures in the Valence-Arousal-Dominance dimensions and affective states are classified into five different category classes. Advantageous intra-individual rates are obtained via multi-channel classification and the respiration best contributes to the recognition. High inter-individual variances are obtained showing large variability in physiological responses between the subjects. Classification rates are significantly higher for women than for men for the 3-category-class of Valence. Compared to old subjects, young subjects have significantly higher rates for the 3-category-class and 2-category-class of Dominance. Moreover, young men's classification performed the best among the other subgroups for the 5-category-class of Valence/Arousal.

Color Image segmentation splits an image into modules, with high correlation among objects contai... more Color Image segmentation splits an image into modules, with high correlation among objects contained in the image. Many color image segmentation algorithms in the literature, segment an image on the basis of color, texture and as a combination of both color and texture. In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the Sequential Minimal Optimization-Support Vector Machine (SMO-SVM) classifier for segmentation. Gabor filter decomposition is used for extracting the textural features and homogeneity model is used for obtaining the color features. The SMO-SVM is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set based membership functions efficiently handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets effectively deal with uncertainty, vagueness, and incompleteness in data. Parameterization tools are not necessary in defining Soft set theory. The goodness aspects of soft sets, rough sets and fuzzy sets are integrated in the proposed algorithm to achieve improved segmentation performance. The proposed algorithm is comparable and achieved better performance compared with the state of the art algorithms found in the literature.

This paper studies the respective performance of minimax search and endgame databases in competin... more This paper studies the respective performance of minimax search and endgame databases in competing against the Awale shareware. It also investigates the performance of combining both techniques to evolve a hybrid player against the Awale shareware.

This research work presents the comparison analysis of Shunt Active Filter (SAF) with Parallel Hy... more This research work presents the comparison analysis of Shunt Active Filter (SAF) with Parallel Hybrid Active Filter (PHAF). The performances of SAF and PHAF have been analyzed with PI controllers based on Synchronous Reference Frame theory. In each case, simulation is carried out for three phase unbalanced non liner load conditions. The advantages of the designed system and the proposed current reference calculation methods are verified by simulations using MATLAB Power system Toolbox.

In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously s... more In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases may not appear to contain valuable relational information but practically such a relation exists. The large number of process parameter values are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for the expert system. With the changes in parameters there is a quite high possibility to form new rules using the dynamics of the process itself. We present an efficient algorithm that generates all significant rules based on the real data. The association based algorithms were compared and the best suited algorithm for this process application was selected. The application for the Learning system is studied in a Power Plant domain. The SCADA interface was developed to acquire online plant data.

Segmentation of organs from medical images is an active and interesting area of research. Liver s... more Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our experimental results confirm that the method yields a satisfactory overall segmentation outcome. Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.

The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue ... more The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue characterization, presenting an interest in the follow-up of various pathologies such as the multiple sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is proposed; finally the last section is organized around an experimental part allowing the study of the behavior of our model on textured images. In the aim to validate our model, different segmentations were down on pathological Brain MRI, the obtained results have been compared to the results obtained by another models. This results show the effectiveness and the robustness of the suggested approach.

This paper presents a binarization method for camera based natural scene (NS) images based on edg... more This paper presents a binarization method for camera based natural scene (NS) images based on edge analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes. The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are compared with other standard techniques. The method is fast and works well for camera based natural scene images.

Vehicle detection and tracking applications play an important role for civilian and military appl... more Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems.

In this paper, we aim to determine the location information of a node deployed in Wireless Sensor... more In this paper, we aim to determine the location information of a node deployed in Wireless Sensor Networks (WSN). We estimate the position of an unknown source node using localization based on linear approach on a single simulation platform. The Cramer Rao Lower Bound (CRLB) for position estimate is derived first and the four linear approaches namely Linear Least Squares (LLS), Subspace Approach (SA), Weighted Linear Least Squares (WLLS) and Two-step WLS have been derived and presented. Based on the simulation study the results have been compared. The simulation results show that the Two- step WLS approach is having higher localization accuracy.

Modern face recognition systems are vulnerable to spoofing attack. Spoofing attack occurs when a ... more Modern face recognition systems are vulnerable to spoofing attack. Spoofing attack occurs when a person tries to cheat the system by presenting fake biometric data gaining unlawful access. A lot of researchers have originated novel techniques to fascinate these types of face tampering attack. It seems that no comparative studies of different face recognition algorithms on same protocols and fake data have been incorporated. The motivation behind this paper is to present the effect of face tampering on various categories of face recognition algorithms. For this purpose four categories of facial recognition algorithms have been selected to present the obtained results in the form of facial identification accuracy at various tampering and experimental protocols but obtained results are very fluctuating in nature. Finally, we come to the conclusion that it is totally unpredictable to select particular type of algorithm for tampered face recognition.

The goal of this paper is to investigate the speech signal enhancement using Kernel Affine Proje... more The goal of this paper is to investigate the speech signal enhancement using Kernel Affine Projection Algorithm (KAPA ) and Normalized KAPA. The removal of background noise is very important in many applications like speech recognition, telephone conversations, hearing aids, forensic, etc. Kernel adaptive filters shown good performance for removal of noise. If the evaluation of background noise is more slowly than the speech, i.e., noise signal is more stationary than the speech, we can easily estimate the noise during the pauses in speech. Otherwise it is more difficult to estimate the noise which results in degradation of speech. In order to improve the quality and intelligibility of speech, unlike time and frequency domains, we can process the signal in new domain like Reproducing Kernel Hilbert Space (RKHS) for high dimensional to yield more powerful nonlinear extensions. For experiments, we have used the database of noisy speech corpus (NOIZEUS). From the results, we observed the removal noise in RKHS has great performance in signal to noise ratio values in comparison with conventional adaptive filters.

Image segmentation is one of the important tasks in computer vision and image processing. Thresho... more Image segmentation is one of the important tasks in computer vision and image processing. Thresholding is a simple but most effective technique in segmentation. It based on classify image pixels into object and background depended on the relation between the gray level value of the pixels and the threshold. Otsu technique is a robust and fast thresholding techniques for most real world images with regard to uniformity and shape measures. Otsu technique splits the object from the background by increasing the separability factor between the classes. Our aim form this work is (1) making a comparison among five thresholding techniques (Otsu technique, valley emphasis technique, neighborhood valley emphasis technique, variance and intensity contrast technique, and variance discrepancy technique)on different applications. (2) determining the best thresholding technique that extracted the object from the background. Our experimental results ensure that every thresholding technique has shown a superior level of performance on specific type of bimodal images.

After several decades of research, the development of an effective feature extraction method for ... more After several decades of research, the development of an effective feature extraction method for texture classification is still an ongoing effort. Therefore , several techniques have been proposed to resolve such problems. In this paper a novel composite texture classification method based on innovative pre-processing techniques, skeletonization and Regional moments (RM) is proposed. This proposed texture classification approach, takes into account the ambiguity brought in by noise and the different caption and digitization processes. To offer better classification rate, innovative pre-processing methods are applied on various texture images first. Pre-processing mechanisms describe various methods of converting a grey level image into binary image with minimal consideration of the noise model. Then shape features are evaluated using RM on the proposed Morphological Skeleton (MS) method by suitable numerical characterization measures for a precise classification. This texture classification study using MS and RM has given a good performance. Good classification result is achieved from a single region moment RM10 while others failed in classification.

An edge may be defined as a set of connected pixels that forms a boundary between two disjoints r... more An edge may be defined as a set of connected pixels that forms a boundary between two disjoints regions. Edge detection is basically, a method of segmenting an image into regions of discontinuity. Edge detection plays an important role in digital image processing and practical aspects of our life. .In this paper we studied various edge detection techniques as Prewitt, Robert, Sobel, Marr Hildrith and Canny operators. On comparing them we can see that canny edge detector performs better than all other edge detectors on various aspects such as it is adaptive in nature, performs better for noisy image, gives sharp edges , low probability of detecting false edges etc

Researches based on animal detection plays a very vital role in many real life applications. Appl... more Researches based on animal detection plays a very vital role in many real life applications. Applications which are very important are preventing animal vehicle collision on roads, preventing dangerous animal intrusion in residential area, knowing locomotive behavioural of targeted animal and many more. There are limited areas of research related to animal detection. In this paper we will discuss some of these areas for detection of animals.

Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processi... more Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processing. VQA reflects the quality of a video as most people commonly perceive. This paper proposes a reducedreference mobile VQA, in which one-dimensional (1-D) motion vector (MV) distributions are used as features of videos. This paper focuses on reduction of data size using Laplacian modeling of MV distributions because network resource is restricted in the case of mobile video. The proposed method is more efficient than the conventional methods in view of the computation time, because the proposed quality metric decodes MVs directly from video stream in the parsing process rather than reconstructing the distorted video at a receiver. Moreover, in view of data size, the proposed method is efficient because a sender transmits only 28 parameters. We adopt the Laplacian distribution for modeling 1-D MV histograms. 1-D MV histograms accumulated over the whole video sequences are used, which is different from the conventional methods that assess each image frame independently. For testing the similarity between MV histogram of reference and distorted videos and for minimizing the fitting error in Laplacian modeling process, we use the chi-square method. To show the effectiveness of our proposed method, we compare the proposed method with the conventional methods with coded video clips, which are coded under varying bit rate, image size, and frame rate by H.263 and H.264/AVC. Experimental results show that the proposed method gives the performance comparable with the conventional methods, especially, the proposed method requires much lower transmission data.