Machine Learning in Computer Vision
Machine Learning in Computer Vision By Nicu Sebe, Ira Cohen, Ashutosh Garg, Thomas S. Huang
Publisher: Springer 2005 | 242 Pages | ISBN: 1402032749 | File type: PDF | 5 mb
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 for the learnable (input) and learned (internal) entities of the system.
In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
All my books in one folder is here below, please! Follow Rules!
Morphological Image Analysis: Principles and Applications (2nd edition)
Echo Signal Processing
Detection and Signal Processing: Technical Realization
Digital Signal Processing - A Modern Introduction
Genomic Signal Processing and Statistics
Image Processing Using Pulse-Coupled Neural Networks
Fourier Transform: Signal Processing and Physical Sciences
Digital Image Processing, 3rd Edition
Digital Signal Processing: Fundamentals and Applications
Adaptive Filtering: Algorithms and Practical Implementation
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