TY - BOOK ID - 38295190 TI - Machine learning in computer vision PY - 2005 SN - 9781402032752 1280283599 9786610283590 1402032757 PB - Dordrecht : Springer, DB - UniCat KW - informatietechnologie KW - Computer. Automation KW - informatica KW - Operational research. Game theory KW - grafische vormgeving KW - Artificial intelligence. Robotics. Simulation. Graphics KW - multimedia KW - stochastische analyse KW - Computer vision. KW - Computer science. KW - Multimedia systems. KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - User Interfaces and Human Computer Interaction. KW - Multimedia Information Systems. KW - Probability and Statistics in Computer Science. KW - Computer-based multimedia information systems KW - Multimedia computing KW - Multimedia information systems KW - Multimedia knowledge systems KW - Information storage and retrieval systems KW - Informatics KW - Science KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Image processing KW - Pattern recognition systems KW - Optical data processing. KW - User interfaces (Computer systems). KW - Multimedia information systems. KW - Mathematical statistics. KW - Interfaces, User (Computer systems) KW - Human-machine systems KW - Human-computer interaction KW - Optical computing KW - Visual data processing KW - Bionics KW - Electronic data processing KW - Integrated optics KW - Photonics KW - Computers KW - Mathematics KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Probabilities KW - Sampling (Statistics) KW - Optical equipment KW - Statistical methods KW - Machine learning. UR - https://www.unicat.be/uniCat?func=search&query=sysid:38295190 AB - The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations ER -