Other affiliations: Centre national de la recherche scientifique
Bio: Yassine Ruichek is an academic researcher from Universite de technologie de Belfort-Montbeliard. The author has contributed to research in topics: Object detection & Image segmentation. The author has an hindex of 24, co-authored 205 publications receiving 1809 citations. Previous affiliations of Yassine Ruichek include Centre national de la recherche scientifique.
Papers published on a yearly basis
TL;DR: A survey of semantic segmentation methods by categorizing them into ten different classes according to the common concepts underlying their architectures, and providing an overview of the publicly available datasets on which they have been assessed.
Abstract: Semantic segmentation is a challenging task in computer vision systems. A lot of methods have been developed to tackle this problem ranging from autonomous vehicles, human-computer interaction, to robotics, medical research, agriculture and so on. Many of these methods have been built using the deep learning paradigm that has shown a salient performance. For this reason, we propose to survey these methods by, first categorizing them into ten different classes according to the common concepts underlying their architectures. Second, by providing an overview of the publicly available datasets on which they have been assessed. In addition, we present the common evaluation matrix used to measure their accuracy. Moreover, we focus on some of the methods and look closely at their architectures in order to find out how they have achieved their reported performances. Finally, we conclude by discussing some of the open problems and their possible solutions.
TL;DR: A vehicle localization method by integrating a stereoscopic system, a laser range finder (LRF) and a global localization sensor GPS and an outlier-rejection invariant closest point method (ICP) is proposed to reduce the matching ambiguities of scan alignment.
Abstract: Vehicle localization and autonomous navigation consist of precisely positioning a vehicle on road by the use of different kinds of sensors. This paper presents a vehicle localization method by integrating a stereoscopic system, a laser range finder (LRF) and a global localization sensor GPS. For more accurate LRF-based vehicle motion estimation, an outlier-rejection invariant closest point method (ICP) is proposed to reduce the matching ambiguities of scan alignment. The fusion approach starts by a sensor selection step that is applied to validate the coherence of the observations from different sensors. Then the information provided by the validated sensors is fused with an unscented information filter. To demonstrate its performance, the proposed multisensor localization method is tested with real data and evaluated by RTK-GPS data as ground truth. The fusion approach also facilitates the incorporation of more sensors if needed.
TL;DR: The experiments carried out on nine publicly available texture datasets demonstrated that the proposed LDTP descriptor achieves classification performance, which is competitive or better than several recent and old state-of-the-art LBP variants.
Abstract: In this paper, the three level descriptions from LTP and the directional features from LDP are combined to form a new local feature descriptor, referred to as local directional ternary pattern (LDTP) for texture classification. LDTP is a framework, which consists in encoding both contrast information and directional pattern features in a compact way based on local derivative variations. To achieve robustness, the proposed operator first computes for each pixel within its 3 × 3 overlapping grayscale image patch, on the one hand, eight directional edge responses using the eight Frei–Chen masks, and on the other hand, central edge response through the 2nd derivative of Gaussian filter to capture more detailed information. This allows producing a more discriminative encoding than several state-of-the art methods based only on intensity information. Then, spatial relationships among the neighboring pixels through the edge responses are exploited independently with the help of both LDP’s and LTP’s concepts to enhance the discrimination capability. Indeed, the implicit utilization of both concepts of LTP and LDP encodes more information in comparison to the existing directional and derivative methods in less space, and simultaneously allows discriminating more textures. Finally, the resultant LDTP pattern is divided into two distinct parts: local directional ternary pattern upper (LDTPU) and local directional ternary pattern lower (LDTPL), and the final feature descriptor vector is obtained by linear concatenation of both LDTPU and LDTPL histograms. The experiments carried out on nine publicly available texture datasets demonstrated that the proposed LDTP descriptor achieves classification performance, which is competitive or better than several recent and old state-of-the-art LBP variants. Statistical significance of the achieved accuracy improvement by the proposed descriptor has been also demonstrated through the Wilcoxon signed rank test applied on all the tested datasets.
TL;DR: A new modeling of the conventional LBP operator for texture classification named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns and its variants and multiscale ARCS-LBP descriptor is presented, showing that the proposed operators can achieve impressive classification accuracy.
Abstract: 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.
TL;DR: Evaluating the effectiveness of the proposed LCvMSP, LCxMSP and LCCMSP operators found that the proposed methods 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.
Abstract: A formal definition of Concave and Convex Binary Thresholding Functions are introduced.Two new LBP-like descriptors: Local Concave and Convex Micro-Structures Pattern (LCvMSP and LCxMSP) descriptors are proposed.LCvMSP and LCxMSP are concatened into a single vector feature to obtain the multiscale LCCMSP descriptor.A statistical hypothesis testing based method for parameters optimization on several datasets is proposed.The proposed methods demonstrate superior performance to 79 LBP variants and non-LBP methods over 13 texture datasets. 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 33 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 33-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 33 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.
TL;DR: The various applications of neural networks in image processing are categorised into a novel two-dimensional taxonomy for image processing algorithms and their specific conditions are discussed in detail.
Abstract: We review more than 200 applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies the type of task performed by the algorithm: preprocessing, data reduction/feature extraction, segmentation, object recognition, image understanding and optimisation. The other dimension captures the abstraction level of the input data processed by the algorithm: pixel-level, local feature-level, structure-level, object-level, object-set-level and scene characterisation. Each of the six types of tasks poses specific constraints to a neural-based approach. These specific conditions are discussed in detail. A synthesis is made of unresolved problems related to the application of pattern recognition techniques in image processing and specifically to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments.
TL;DR: This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding, and discusses the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on- road behavior.
Abstract: This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.
TL;DR: Two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery are provided, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels.
Abstract: Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.
TL;DR: A new approach to visual navigation under changing conditions dubbed SeqSLAM, which removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images.
Abstract: Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.
TL;DR: A brief introduction of SVMs is provided, many applications are described and challenges and trends are summarized, especially in the some fields.
Abstract: In recent years, an enormous amount of research has been carried out on support vector machines (SVMs) and their application in several fields of science. SVMs are one of the most powerful and robust classification and regression algorithms in multiple fields of application. The SVM has been playing a significant role in pattern recognition which is an extensively popular and active research area among the researchers. Research in some fields where SVMs do not perform well has spurred development of other applications such as SVM for large data sets, SVM for multi classification and SVM for unbalanced data sets. Further, SVM has been integrated with other advanced methods such as evolve algorithms, to enhance the ability of classification and optimize parameters. SVM algorithms have gained recognition in research and applications in several scientific and engineering areas. This paper provides a brief introduction of SVMs, describes many applications and summarizes challenges and trends. Furthermore, limitations of SVMs will be identified. The future of SVMs will be discussed in conjunction with further applications. The applications of SVMs will be reviewed as well, especially in the some fields.