IAES International Journal of Artificial Intelligence (IJ-AI)
Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artif... more Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
Pedestrian tracking and detection have become critical aspects of advanced driver assistance syst... more Pedestrian tracking and detection have become critical aspects of advanced driver assistance systems (ADASs), due to their academic and commercial potential. Their objective is to locate various pedestrians in videos and assign them unique identities. The data association task is problematic, particularly when dealing with inter-pedestrian occlusion. This occurs when multiple pedestrians cross paths or move too close together, making it difficult for the system to identify and track individual pedestrians. Inaccurate tracking can lead to false alarms, missed detections, and incorrect decisions. To overcome this challenge, our paper focuses on improving data association in our pedestrian detection system’s Deep-SORT tracking algorithm, which is solved as a linear optimization problem using a newly generated cost matrix. We introduce a set of new data association cost matrices that rely on metrics such as intersections, distances, and bounding boxes. To evaluate trackers in real time,...
Proceedings of the 2019 5th International Conference on Computer and Technology Applications
This paper presents an efficient method for the implementation of an image search system invarian... more This paper presents an efficient method for the implementation of an image search system invariant to rotation. This method consists of applying circular masks of different size on the image, and extracting the color descriptor from the visible region on the mask, and then combining a texture descriptor for more precision. In our work, we have used the color histogram and histogram of local binary patterns. The experimentation of this approach gave excellent results for color rotation-invariant image retrieval.
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2017
Characterizing GNSS signals reception environment using fisheye camera oriented to the sky is one... more Characterizing GNSS signals reception environment using fisheye camera oriented to the sky is one of the relevant approaches which have been proposed to compensate the lack of performance of GNSS occurring when operating in constrained environments (dense urbain areas). This solution consists, after classification of acquired images into two regions (sky and not-sky), in identifying satellites as line-of-sight (LOS) satellites or non-line-of-sight (NLOS) satellites by repositioning the satellites in the classified images. This paper proposes a region-based image classification method through local image region descriptors and Hellinger kernel-based distance. The objective is to try to improve results obtained previously by a state of the art method. The proposed approach starts by simplifying the acquired image with a suitable couple of colorimetric invariant and exponential transform. After that, a segmentation step is performed in order to extract from the simplified image regions of interest using Statistical Region Merging method. The next step consists of characterizing the obtained regions with local RGB color and a number of local color texture descriptors using image quantization. Finally, the characterized regions are classified into sky and non sky regions by using supervised M SR C (Maximal Similarity Based Region Classification) method through Hellinger kernel-based distance. Extensive experiments have been performed to prove the effectiveness of the proposed approach.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
2018 25th IEEE International Conference on Image Processing (ICIP), 2018
This paper proposes a deep background subtraction method based on conditional Generative Adversar... more This paper proposes a deep background subtraction method based on conditional Generative Adversarial Network (cGAN). The proposed model consists of two successive networks: generator and discriminator. The generator learns the mapping from the observing input (i.e., image and background), to the output (i.e., foreground mask). Then, the discriminator learns a loss function to train this mapping by comparing real foreground (i.e., ground-truth) and fake foreground (i.e., predicted output) with observing the input image and background. Evaluating the model performance with two public datasets, CDnet 2014 and BMC, shows that the proposed model outperforms the state-of-the-art methods.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Two Phase Test Sample Sparse Representation (TPTSSR) classifier was recently proposed as an effic... more Two Phase Test Sample Sparse Representation (TPTSSR) classifier was recently proposed as an efficient alternative to the Sparse Representation Classifier (SRC). It aims at classifying data using sparse coding in two phases with 2 regularization. Although high performances can be obtained by the TPTSSR classifier, since it is a supervised classifier, it is not able to benefit from unlabeled samples which are very often available. In this paper, we introduce a semisupervised version of the TPTSSR classifier called Semi-supervised Two Phase Test Sample Sparse Representation (STPTSSR). STPTSSR combines the merits of sparse coding, active learning and the two phase collaborative representation classifiers. The proposed framework is able to make any sparse representation based classifier semi-supervised. Extensive experiments carried out on six benchmark image datasets show that the proposed STPTSSR can outperform the classical TPTSSR as well as many state-of-the-art semi-supervised methods.
International Journal of Machine Learning and Cybernetics, 2019
Sparse Representation Classifier (SRC) and its variants were considered as powerful classifiers i... more Sparse Representation Classifier (SRC) and its variants were considered as powerful classifiers in the domains of computer vision and pattern recognition. However, classifying test samples is computationally expensive due to the 1 norm minimization problem that should be solved in order to get the sparse code. Therefore, these classifiers could not be the right choice for scenarios requiring fast classification. In order to overcome the expensive computational cost of SRC, a two-phase coding classifier based on classic Regularized Least Square was proposed. This classifier is more efficient than SRC. A significant limitation of this classifier is the fact that the number of the samples that should be handed over to the next coding phase should be specified a priori. This paper overcomes this main limitation and proposes five data-driven schemes allowing an automatic estimation of the optimal size of the local samples. These schemes handle the three cases that are encountered in any learning system: supervised, unsupervised, and semi-supervised. Experiments are conducted on five image datasets. These experiments show that the introduced learning schemes can improve the performance of the two-phase linear coding classifier adopting ad-hoc choices for the number of local samples.
Pattern recognition and computer vision fields experienced the proposal of several architectures ... more Pattern recognition and computer vision fields experienced the proposal of several architectures and approaches to deal with the demands of real world applications including face recognition. They have almost the same structure, based generally on a series of steps where the main ones are feature extraction and classification. The literature works interessted in face recognition problems, insist on the role of texture description as one of the key elements in face analysis, since it greatly affects recognition accuracy. Therefore, texture feature extraction has gained much attention and became a long-standing research topic thanks to its abilities to efficiently understand the face recognition process, especially in terms of face description. Recently, several literature researches in face application proposed new architectures based on pattern description proved by their discriminative power when extracting the feature information from facial images. These advantages combined with an outstanding performance in many classification applications, allowed the LBP-like descriptors to be one of the most prominent texture description method. Given this period of remarkable evolution, this research work includes a comprehensive analytical study of the face recognition performance of 64 LBP-like and 3 non-LBP texture descriptors recently proposed in the literature. To this end, we adopted a face recognition framework composed of four stages: 1) image pre-processing using gamma correction; 2) feature extraction using texture descriptors; 3) histogram calculation and 4) face recognition and classification based on the simple parameter-free Nearest Neighbors classifier (NN). The conducted comprehensive evaluations and experiments on the challenging and widely used benchmarks ORL, YALE, Extended YALE B and FERET databases presenting different challenges, indicate that a number of evaluated texture descriptors, which are tested for the first time on face recognition task, achieve better or competitive compared to several recent systems reported in face recognition literature.
This paper presents new modeling of local binary patterns for texture representation. Referred to... more This paper presents new modeling of local binary patterns for texture representation. Referred to as local binary gradient contours (LBGC), the proposed models are expected to better represent the salient local texture structure. Thanks to the flexibility of repulsive-attractive characteristics, which represent the cornerstone of the proposed descriptors, two distinct LBP-like descriptors are built: repulsive and attractive local binary gradient contours (RLBGC and ALBGC). Conventional methods such as LBP, the family of binary gradient contours (BGC1, BGC2 and BGC3), LBP by neighborhoods (nLBP d) and several other LBP-like methods, are based on pairwise comparison of adjacent pixels. Unlike these methods, the proposed RLBGC and ALBGC operators encode the differences between local intensity values within triplets of pixels, along a closed path around the central pixel of a 3x3 gray-scale image patch. In order to increase the robustness of the proposed RLBGC and ALBGC descriptors, the triplet formed by the average local and average global gray levels (ALGL and AGGL) and the central pixel is incorporated in the modeling. In order to make the proposed approach more robust and stable, the RLBGC and ALBGC are concatenated together to form multi-scale repulsive-and-attractive local binary gradient contour (RALBGC) descriptor. Extensive experimental results from 13 challenging representative texture datasets show that the proposed descriptors, applied on each dataset, can achieve interesting classification accuracy, which is competitive or better than a great number of state-of-the-art LBP variants and non-LBP methods. Furthermore, statistical hypothesis testing is performed to prove the statistical significance of the achieved accuracy improvement over all the tested datasets.
Motivated by researching new image texture modeling that improves state-of-the-art LBP variants a... more Motivated by researching new image texture modeling that improves state-of-the-art LBP variants and non-LBP descriptors, this paper proposes a novel approach for constructing local image descriptors, which are suitable for histogram based image representation. Instead of heuristic code constructions, the proposed approach is based on local concave-and-convex characteristics, which have high ability to extract discriminative and stable texture representation. Different from the majority of descriptors that only encode relationships between the pixels in doublets around central pixel (within 3×3 neighborhood), the proposed approach encodes relationships between the pixels in triplets by including the central pixel in the modeling. We build two distinct descriptors by dividing local features into two distinct groups, i.e., local concave and convex microstructure patterns (LCvMSP and LCxMSP), according to relationships between the pixels inside the triplets, formed along closed path around the central pixel of a 3x3-grayscale image patch. To make the descriptors more insensitive to noise and invariant to monotonic gray scale transformation, two supplementary triplets are added in the modeling. These triplets are formed using the central pixel and four virtual pixels set to the median of the grey-scale values of the 3×3 neighbourhood and the whole image and the average local and global gray levels respectively. The histograms obtained from the single scale descriptors LCvMSP and LCxMSP are concatenated together to build multi-scale histogram feature vector referred to as local concave-and-convex micro-structure pattern (LCCMSP), that is expected to better represent salient local texture structure. We evaluated the effectiveness of the proposed methods on thirteen challenging representative widely-used texture datasets, and found that the proposed LCvMSP, LCxMSP and LCCMSP operators achieve performances that are competitive or better than a large number of recent most promising state-of-the-art LBP variants and non-LBP descriptors. Statistical comparison based on Wilcoxon signed rank test demonstrated that the proposed methods are the top three over all the tested datasets.
Engineering Applications of Artificial Intelligence, 2019
Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new mo... more Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new modeling of the conventional LBP operator for texture classification. Named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns (ACS-LBP and RCS-LBP), the proposed new texture descriptors preserve the advantageous characteristics of uniform LBP. Based on local attractive-and-repulsive characteristics, the proposed local texture modeling can really inherit good properties from both gradient and texture operators than the Center-Symmetric Local Binary Patterns (CS-LBP) does. Different from CS-LBP, which considers four doublets around the center pixel, the proposed methods take into account the four triplets corresponding to the vertical and horizontal directions, and the two diagonal directions by including the value of the central pixel in the modeling. In addition, Average Local Gray Level (ALGL), Average Global Gray Level (AGGL) and the median value over 3 × 3 neighborhood are introduced to capture both microstructure and macrostructure texture information. To capture the coarse and fine information of the features and thus to make ACS-LBP and RCS-LBP more robust and stable, multiscale ARCS-LBP descriptor is proposed. There is no necessity to learn texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experiments performed on thirteen challenging representative texture databases show that the proposed operators can achieve impressive classification accuracy. Furthermore, we clearly validate the feasibility of the proposed ACS-LBP, RCS-LBP and ARCS-LBP descriptors by comparing their results with those obtained with a large number of recent state-of-the-art texture descriptors including deep features. Statistical significance of achieved accuracy improvement is demonstrated through Wilcoxon signed rank test.
Maximal similarity based region classification method through local image region descriptors and ... more Maximal similarity based region classification method through local image region descriptors and Bhattacharyya coefficient-based distance: Application to horizon line detection using wide-angle camera, Neurocomputing (2017),
In this article we present a novel method of extraction and combination descriptor to represent i... more In this article we present a novel method of extraction and combination descriptor to represent image. First we extract a descriptor shape (HOG) from entire image, and in second we applied method of segmentation and then we extract the color and texture descriptor from each segment in order to have a local and global aspect for each image. These characteristics will be concatenate, stored and compared to those of the image query using the Euclidean distance. The performance of this system is evaluated with a precision factor. The results experimental show a good performance.
Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems
In the robotic domain, stereo vision is a popular technique for extracting 3-D depth information ... more In the robotic domain, stereo vision is a popular technique for extracting 3-D depth information of a scene seen by two or more video cameras. The key problem is the matching task, which consists of identifying correspondences between features extracted from two stereo images. This paper presents a hierarchical neural approach for matching edges extracted from stereo linear images. The
2012 Eighth International Conference on Signal Image Technology and Internet Based Systems, 2012
In this paper, we show some results about 3D urban scenes reconstruction using a fisheye stereovi... more In this paper, we show some results about 3D urban scenes reconstruction using a fisheye stereovision setup. We propose an analytical analysis of epipolar geometry of the system and an analytical description of tools to compute a 3D point cloud from matched pixels. The novelty is that we do not rectify the images and that we match points along 3D or 2D epipolar curves. The matching process is based on a global dynamic programming algorithm that we adapt to take into account continuous epipolar curve equation. We show 3D point cloud in the case of synthetic images.
2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014
In this paper, an efficient and automatic method for detection of multiple-objects of interest fr... more In this paper, an efficient and automatic method for detection of multiple-objects of interest from images is presented. This method is based on using region similarity measures. The method starts by constructing two knowledge databases in which significant and distinctive textures extracted from both objects of interest and background are respectively represented. The proposed procedure continues by an initialization step in which the processed image is segmented into homogeneous regions. In the purpose of separating objects of interest from image background, an evaluation of the similarity between regions of the segmented image and those of the constructed knowledge databases is then performed. The main advantages of this method are simplicity, applicability and suitability. Applying this method on building roof detection from orthophotoplans has shown its robustness and performance.
International Journal of Advanced Computer Science and Applications, 2014
This paper presents a new method for tracking objects using stereo vision with linear cameras. Ed... more This paper presents a new method for tracking objects using stereo vision with linear cameras. Edge points extracted from the stereo linear images are first matched to reconstruct points that represent the objects in the scene. To detect the objects, a clustering process based on a spectral analysis is then applied to the reconstructed points. The obtained clusters are finally tracked throughout their center of gravity using Kalman filter and a Nearest Neighbour based data association algorithm. Experimental results using real stereo linear images are shown to demonstrate the effectiveness of the proposed method for obstacle tracking in front of a vehicle.
IAES International Journal of Artificial Intelligence (IJ-AI)
Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artif... more Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
Pedestrian tracking and detection have become critical aspects of advanced driver assistance syst... more Pedestrian tracking and detection have become critical aspects of advanced driver assistance systems (ADASs), due to their academic and commercial potential. Their objective is to locate various pedestrians in videos and assign them unique identities. The data association task is problematic, particularly when dealing with inter-pedestrian occlusion. This occurs when multiple pedestrians cross paths or move too close together, making it difficult for the system to identify and track individual pedestrians. Inaccurate tracking can lead to false alarms, missed detections, and incorrect decisions. To overcome this challenge, our paper focuses on improving data association in our pedestrian detection system’s Deep-SORT tracking algorithm, which is solved as a linear optimization problem using a newly generated cost matrix. We introduce a set of new data association cost matrices that rely on metrics such as intersections, distances, and bounding boxes. To evaluate trackers in real time,...
Proceedings of the 2019 5th International Conference on Computer and Technology Applications
This paper presents an efficient method for the implementation of an image search system invarian... more This paper presents an efficient method for the implementation of an image search system invariant to rotation. This method consists of applying circular masks of different size on the image, and extracting the color descriptor from the visible region on the mask, and then combining a texture descriptor for more precision. In our work, we have used the color histogram and histogram of local binary patterns. The experimentation of this approach gave excellent results for color rotation-invariant image retrieval.
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2017
Characterizing GNSS signals reception environment using fisheye camera oriented to the sky is one... more Characterizing GNSS signals reception environment using fisheye camera oriented to the sky is one of the relevant approaches which have been proposed to compensate the lack of performance of GNSS occurring when operating in constrained environments (dense urbain areas). This solution consists, after classification of acquired images into two regions (sky and not-sky), in identifying satellites as line-of-sight (LOS) satellites or non-line-of-sight (NLOS) satellites by repositioning the satellites in the classified images. This paper proposes a region-based image classification method through local image region descriptors and Hellinger kernel-based distance. The objective is to try to improve results obtained previously by a state of the art method. The proposed approach starts by simplifying the acquired image with a suitable couple of colorimetric invariant and exponential transform. After that, a segmentation step is performed in order to extract from the simplified image regions of interest using Statistical Region Merging method. The next step consists of characterizing the obtained regions with local RGB color and a number of local color texture descriptors using image quantization. Finally, the characterized regions are classified into sky and non sky regions by using supervised M SR C (Maximal Similarity Based Region Classification) method through Hellinger kernel-based distance. Extensive experiments have been performed to prove the effectiveness of the proposed approach.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
2018 25th IEEE International Conference on Image Processing (ICIP), 2018
This paper proposes a deep background subtraction method based on conditional Generative Adversar... more This paper proposes a deep background subtraction method based on conditional Generative Adversarial Network (cGAN). The proposed model consists of two successive networks: generator and discriminator. The generator learns the mapping from the observing input (i.e., image and background), to the output (i.e., foreground mask). Then, the discriminator learns a loss function to train this mapping by comparing real foreground (i.e., ground-truth) and fake foreground (i.e., predicted output) with observing the input image and background. Evaluating the model performance with two public datasets, CDnet 2014 and BMC, shows that the proposed model outperforms the state-of-the-art methods.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Two Phase Test Sample Sparse Representation (TPTSSR) classifier was recently proposed as an effic... more Two Phase Test Sample Sparse Representation (TPTSSR) classifier was recently proposed as an efficient alternative to the Sparse Representation Classifier (SRC). It aims at classifying data using sparse coding in two phases with 2 regularization. Although high performances can be obtained by the TPTSSR classifier, since it is a supervised classifier, it is not able to benefit from unlabeled samples which are very often available. In this paper, we introduce a semisupervised version of the TPTSSR classifier called Semi-supervised Two Phase Test Sample Sparse Representation (STPTSSR). STPTSSR combines the merits of sparse coding, active learning and the two phase collaborative representation classifiers. The proposed framework is able to make any sparse representation based classifier semi-supervised. Extensive experiments carried out on six benchmark image datasets show that the proposed STPTSSR can outperform the classical TPTSSR as well as many state-of-the-art semi-supervised methods.
International Journal of Machine Learning and Cybernetics, 2019
Sparse Representation Classifier (SRC) and its variants were considered as powerful classifiers i... more Sparse Representation Classifier (SRC) and its variants were considered as powerful classifiers in the domains of computer vision and pattern recognition. However, classifying test samples is computationally expensive due to the 1 norm minimization problem that should be solved in order to get the sparse code. Therefore, these classifiers could not be the right choice for scenarios requiring fast classification. In order to overcome the expensive computational cost of SRC, a two-phase coding classifier based on classic Regularized Least Square was proposed. This classifier is more efficient than SRC. A significant limitation of this classifier is the fact that the number of the samples that should be handed over to the next coding phase should be specified a priori. This paper overcomes this main limitation and proposes five data-driven schemes allowing an automatic estimation of the optimal size of the local samples. These schemes handle the three cases that are encountered in any learning system: supervised, unsupervised, and semi-supervised. Experiments are conducted on five image datasets. These experiments show that the introduced learning schemes can improve the performance of the two-phase linear coding classifier adopting ad-hoc choices for the number of local samples.
Pattern recognition and computer vision fields experienced the proposal of several architectures ... more Pattern recognition and computer vision fields experienced the proposal of several architectures and approaches to deal with the demands of real world applications including face recognition. They have almost the same structure, based generally on a series of steps where the main ones are feature extraction and classification. The literature works interessted in face recognition problems, insist on the role of texture description as one of the key elements in face analysis, since it greatly affects recognition accuracy. Therefore, texture feature extraction has gained much attention and became a long-standing research topic thanks to its abilities to efficiently understand the face recognition process, especially in terms of face description. Recently, several literature researches in face application proposed new architectures based on pattern description proved by their discriminative power when extracting the feature information from facial images. These advantages combined with an outstanding performance in many classification applications, allowed the LBP-like descriptors to be one of the most prominent texture description method. Given this period of remarkable evolution, this research work includes a comprehensive analytical study of the face recognition performance of 64 LBP-like and 3 non-LBP texture descriptors recently proposed in the literature. To this end, we adopted a face recognition framework composed of four stages: 1) image pre-processing using gamma correction; 2) feature extraction using texture descriptors; 3) histogram calculation and 4) face recognition and classification based on the simple parameter-free Nearest Neighbors classifier (NN). The conducted comprehensive evaluations and experiments on the challenging and widely used benchmarks ORL, YALE, Extended YALE B and FERET databases presenting different challenges, indicate that a number of evaluated texture descriptors, which are tested for the first time on face recognition task, achieve better or competitive compared to several recent systems reported in face recognition literature.
This paper presents new modeling of local binary patterns for texture representation. Referred to... more This paper presents new modeling of local binary patterns for texture representation. Referred to as local binary gradient contours (LBGC), the proposed models are expected to better represent the salient local texture structure. Thanks to the flexibility of repulsive-attractive characteristics, which represent the cornerstone of the proposed descriptors, two distinct LBP-like descriptors are built: repulsive and attractive local binary gradient contours (RLBGC and ALBGC). Conventional methods such as LBP, the family of binary gradient contours (BGC1, BGC2 and BGC3), LBP by neighborhoods (nLBP d) and several other LBP-like methods, are based on pairwise comparison of adjacent pixels. Unlike these methods, the proposed RLBGC and ALBGC operators encode the differences between local intensity values within triplets of pixels, along a closed path around the central pixel of a 3x3 gray-scale image patch. In order to increase the robustness of the proposed RLBGC and ALBGC descriptors, the triplet formed by the average local and average global gray levels (ALGL and AGGL) and the central pixel is incorporated in the modeling. In order to make the proposed approach more robust and stable, the RLBGC and ALBGC are concatenated together to form multi-scale repulsive-and-attractive local binary gradient contour (RALBGC) descriptor. Extensive experimental results from 13 challenging representative texture datasets show that the proposed descriptors, applied on each dataset, can achieve interesting classification accuracy, which is competitive or better than a great number of state-of-the-art LBP variants and non-LBP methods. Furthermore, statistical hypothesis testing is performed to prove the statistical significance of the achieved accuracy improvement over all the tested datasets.
Motivated by researching new image texture modeling that improves state-of-the-art LBP variants a... more Motivated by researching new image texture modeling that improves state-of-the-art LBP variants and non-LBP descriptors, this paper proposes a novel approach for constructing local image descriptors, which are suitable for histogram based image representation. Instead of heuristic code constructions, the proposed approach is based on local concave-and-convex characteristics, which have high ability to extract discriminative and stable texture representation. Different from the majority of descriptors that only encode relationships between the pixels in doublets around central pixel (within 3×3 neighborhood), the proposed approach encodes relationships between the pixels in triplets by including the central pixel in the modeling. We build two distinct descriptors by dividing local features into two distinct groups, i.e., local concave and convex microstructure patterns (LCvMSP and LCxMSP), according to relationships between the pixels inside the triplets, formed along closed path around the central pixel of a 3x3-grayscale image patch. To make the descriptors more insensitive to noise and invariant to monotonic gray scale transformation, two supplementary triplets are added in the modeling. These triplets are formed using the central pixel and four virtual pixels set to the median of the grey-scale values of the 3×3 neighbourhood and the whole image and the average local and global gray levels respectively. The histograms obtained from the single scale descriptors LCvMSP and LCxMSP are concatenated together to build multi-scale histogram feature vector referred to as local concave-and-convex micro-structure pattern (LCCMSP), that is expected to better represent salient local texture structure. We evaluated the effectiveness of the proposed methods on thirteen challenging representative widely-used texture datasets, and found that the proposed LCvMSP, LCxMSP and LCCMSP operators achieve performances that are competitive or better than a large number of recent most promising state-of-the-art LBP variants and non-LBP descriptors. Statistical comparison based on Wilcoxon signed rank test demonstrated that the proposed methods are the top three over all the tested datasets.
Engineering Applications of Artificial Intelligence, 2019
Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new mo... more Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new modeling of the conventional LBP operator for texture classification. Named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns (ACS-LBP and RCS-LBP), the proposed new texture descriptors preserve the advantageous characteristics of uniform LBP. Based on local attractive-and-repulsive characteristics, the proposed local texture modeling can really inherit good properties from both gradient and texture operators than the Center-Symmetric Local Binary Patterns (CS-LBP) does. Different from CS-LBP, which considers four doublets around the center pixel, the proposed methods take into account the four triplets corresponding to the vertical and horizontal directions, and the two diagonal directions by including the value of the central pixel in the modeling. In addition, Average Local Gray Level (ALGL), Average Global Gray Level (AGGL) and the median value over 3 × 3 neighborhood are introduced to capture both microstructure and macrostructure texture information. To capture the coarse and fine information of the features and thus to make ACS-LBP and RCS-LBP more robust and stable, multiscale ARCS-LBP descriptor is proposed. There is no necessity to learn texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experiments performed on thirteen challenging representative texture databases show that the proposed operators can achieve impressive classification accuracy. Furthermore, we clearly validate the feasibility of the proposed ACS-LBP, RCS-LBP and ARCS-LBP descriptors by comparing their results with those obtained with a large number of recent state-of-the-art texture descriptors including deep features. Statistical significance of achieved accuracy improvement is demonstrated through Wilcoxon signed rank test.
Maximal similarity based region classification method through local image region descriptors and ... more Maximal similarity based region classification method through local image region descriptors and Bhattacharyya coefficient-based distance: Application to horizon line detection using wide-angle camera, Neurocomputing (2017),
In this article we present a novel method of extraction and combination descriptor to represent i... more In this article we present a novel method of extraction and combination descriptor to represent image. First we extract a descriptor shape (HOG) from entire image, and in second we applied method of segmentation and then we extract the color and texture descriptor from each segment in order to have a local and global aspect for each image. These characteristics will be concatenate, stored and compared to those of the image query using the Euclidean distance. The performance of this system is evaluated with a precision factor. The results experimental show a good performance.
Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems
In the robotic domain, stereo vision is a popular technique for extracting 3-D depth information ... more In the robotic domain, stereo vision is a popular technique for extracting 3-D depth information of a scene seen by two or more video cameras. The key problem is the matching task, which consists of identifying correspondences between features extracted from two stereo images. This paper presents a hierarchical neural approach for matching edges extracted from stereo linear images. The
2012 Eighth International Conference on Signal Image Technology and Internet Based Systems, 2012
In this paper, we show some results about 3D urban scenes reconstruction using a fisheye stereovi... more In this paper, we show some results about 3D urban scenes reconstruction using a fisheye stereovision setup. We propose an analytical analysis of epipolar geometry of the system and an analytical description of tools to compute a 3D point cloud from matched pixels. The novelty is that we do not rectify the images and that we match points along 3D or 2D epipolar curves. The matching process is based on a global dynamic programming algorithm that we adapt to take into account continuous epipolar curve equation. We show 3D point cloud in the case of synthetic images.
2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014
In this paper, an efficient and automatic method for detection of multiple-objects of interest fr... more In this paper, an efficient and automatic method for detection of multiple-objects of interest from images is presented. This method is based on using region similarity measures. The method starts by constructing two knowledge databases in which significant and distinctive textures extracted from both objects of interest and background are respectively represented. The proposed procedure continues by an initialization step in which the processed image is segmented into homogeneous regions. In the purpose of separating objects of interest from image background, an evaluation of the similarity between regions of the segmented image and those of the constructed knowledge databases is then performed. The main advantages of this method are simplicity, applicability and suitability. Applying this method on building roof detection from orthophotoplans has shown its robustness and performance.
International Journal of Advanced Computer Science and Applications, 2014
This paper presents a new method for tracking objects using stereo vision with linear cameras. Ed... more This paper presents a new method for tracking objects using stereo vision with linear cameras. Edge points extracted from the stereo linear images are first matched to reconstruct points that represent the objects in the scene. To detect the objects, a clustering process based on a spectral analysis is then applied to the reconstructed points. The obtained clusters are finally tracked throughout their center of gravity using Kalman filter and a Nearest Neighbour based data association algorithm. Experimental results using real stereo linear images are shown to demonstrate the effectiveness of the proposed method for obstacle tracking in front of a vehicle.
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Papers by Y. Ruichek