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!
Signal Processing with Free Software
Fractional Order Signal Processing: Introductory Concepts and Applications
Principles of Digital Image Processing: Advanced Methods
Matrix Information Geometry
Sparse Representations and Compressive Sensing for Imaging and Vision
Simulation of Communication Systems (Applications of Communications Theory)
Advances in Signal Transforms: Theory and Applications
The Application of Programmable DSPs in Mobile Communications
Digital Audio Signal Processing
Handbook of Time Series Analysis, Signal Processing, and Dynamics
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