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!
Image Processing and Acquisition using Python
The Technology of Binaural Listening (Modern Acoustics and Signal Processing)
Theoretical Foundations of Digital Imaging Using MATLAB
Signal Processing: A Mathematical Approach, Second Edition
Noise and Vibration Analysis: Signal Analysis and Experimental Procedures
Principles of Digital Image Processing: Advanced Methods
Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPtools, 2nd Ed
The Application of Programmable DSPs in Mobile Communications
Wavelet Transforms and Their Recent Applications in Biology and Geoscience
Advances in Signal Processing and Intelligent Recognition Systems
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