Francisco Escolano Ruiz & Pablo Suau Pérez 
Information Theory in Computer Vision and Pattern Recognition [PDF ebook] 

Dukung

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information…), principles (maximum entropy, minimax entropy…) and theories (rate distortion theory, method of types…).
This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.

€106.95
cara pembayaran

Daftar Isi

Introduction Interest Points, Edges and Contour Grouping Contour and Region Based Image Segmentation Registration, Matching, and Recognition Image and Pattern Clustering Feature Selection and Transformation Classifier Design

Beli ebook ini dan dapatkan 1 lagi GRATIS!
Bahasa Inggris ● Format PDF ● Halaman 364 ● ISBN 9781848822979 ● Ukuran file 20.1 MB ● Penerbit Springer London ● Kota London ● Negara GB ● Diterbitkan 2009 ● Diunduh 24 bulan ● Mata uang EUR ● ID 2151895 ● Perlindungan salinan Adobe DRM
Membutuhkan pembaca ebook yang mampu DRM

Ebook lainnya dari penulis yang sama / Editor

16,941 Ebooks dalam kategori ini