IMIST


Affiner votre recherche

Votre recherche a retourné 148 résultats.

Advances in Digital Image Processing and Information Technology First International Conference on Digital Image Processing and Pattern Recognition, DPPR 2011, Tirunelveli, Tamil Nadu, India, September 23-25, 2011. Proceedings / par Nagamalai, Dhinaharan. Publication : Berlin, Heidelberg : Springer Berlin Heidelberg : | Springer e-books : | Imprint: Springer : | Springer e-books, 2011 . 1 online resource. Date : 2011 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

Applications of pulse-coupled neural networks par Ma, Yide. Publication : [S.l.] Springer 2010 . 199 p. , "Applications of Pulse-Coupled Neural Networks" explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields. This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science. Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Science and Engineering, Lanzhou University, China. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

Machine learning for multimedia content analysis par Gong,, Yihong. Publication : [S.l.] Springer 2010 . 277 p. , This volume introduces machine learning techniques that are particularly powerful and effective for modeling multimedia data and common tasks of multimedia content analysis. It systematically covers key machine learning techniques in an intuitive fashion and demonstrates their applications through case studies. Coverage includes examples of unsupervised learning, generative models and discriminative models. In addition, the book examines Maximum Margin Markov (M3) networks, which strive to combine the advantages of both the graphical models and Support Vector Machines (SVM). 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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),
3D Computer Vision Efficient Methods and Applications / par Wöhler, Christian. Publication : . XVIII, 382 p. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-4150-1,

Omnidirectional Vision Systems Calibration, Feature Extraction and 3D Information / par Puig, Luis. Publication : . XI, 122 p. 68 illus., 35 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-4947-7,

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,

Semantic Analysis and Understanding of Human Behavior in Video Streaming par Amato, Alberto. Publication : . XII, 108 p. Disponibilité :  http://dx.doi.org/10.1007/978-1-4614-5486-1,

3D Surface Reconstruction Multi-Scale Hierarchical Approaches / par Bellocchio, Francesco. Publication : . VI, 162 p. Disponibilité :  http://dx.doi.org/10.1007/978-1-4614-5632-2,

Abstraction in Artificial Intelligence and Complex Systems par Saitta, Lorenza. Publication : . XVI, 484 p. Disponibilité :  http://dx.doi.org/10.1007/978-1-4614-7052-6,

Cage-based Performance Capture par Savoye, Yann. Publication : . X, 141 p. 86 illus., 85 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-3-319-01538-5,

Probabilistic Approaches to Robotic Perception par Ferreira, João Filipe. Publication : . XXIX, 242 p. Disponibilité :  http://dx.doi.org/10.1007/978-3-319-02006-8,

Vous ne trouvez pas ce que vous cherchez ?
© Tous droits résérvés IMIST/CNRST
Angle Av. Allal Al Fassi et Av. des FAR, Hay Ryad, BP 8027, 10102 Rabat, Maroc
Tél:(+212) 05 37.56.98.00
CNRST / IMIST

Propulsé par Koha