Bibliothèque de L'IMIST/CNRST

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An introduction to object recognition : selected algorithms for a wide variety of applications par Treiber, Marco Alexander. Publication : [S.l.] Springer 2010 . 220 p. , Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum. Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters). Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses. Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Human ear recognition by computer par Bhanu, Bir. Publication : [S.l.] Springer 2010 . 201 p. , At the frontier of research, this book offers complete coverage of human ear recognition. It explores all aspects of 3D ear recognition: representation, detection, recognition, indexing and performance prediction. It uses large datasets to quantify and compare the performance of various techniques. Features and topics include: Ear detection and recognition in 2D image; 3D object recognition and 3D biometrics; 3D ear recognition; Performance comparison and prediction. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (2),

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Machine learning for vision-based motion analysis : theory and techniques   Publication : [S.l.] Springer 2010 . 384 p. , Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Imaging Spectroscopy for Scene Analysis par Robles-Kelly, Antonio. Publication : . XVIII, 270 p. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-4652-0,

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Visual Texture Accurate Material Appearance Measurement, Representation and Modeling / par Haindl, Michal. Publication : . XXXI, 284 p. 158 illus., 148 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-4902-6,

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods par Aldrich, Chris. Publication : . XIX, 374 p. 208 illus., 151 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-5185-2,

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Optimization for Computer Vision An Introduction to Core Concepts and Methods / par Treiber, Marco Alexander. Publication : . XI, 257 p. 107 illus., 93 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-5283-5,

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Compression Schemes for Mining Large Datasets A Machine Learning Perspective / par Ravindra Babu, T. Publication : . XVI, 197 p. 62 illus., 3 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-5607-9,

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Markov Models for Pattern Recognition From Theory to Applications / par Fink, Gernot A. Publication : . XIII, 276 p. 45 illus. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-6308-4,

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Video Text Detection par Lu, Tong. Publication : . X, 264 p. 160 illus. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-6515-6,

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