Entropy And Vision
Probability
2007-05-23 v3 Computer Vision and Pattern Recognition
Databases
Discrete Mathematics
Machine Learning
Combinatorics
Abstract
In vector quantization the number of vectors used to construct the codebook is always an undefined problem, there is always a compromise between the number of vectors and the quantity of information lost during the compression. In this text we present a minimum of Entropy principle that gives solution to this compromise and represents an Entropy point of view of signal compression in general. Also we present a new adaptive Object Quantization technique that is the same for the compression and the perception.
Keywords
Cite
@article{arxiv.math/0606643,
title = {Entropy And Vision},
author = {Rami Kanhouche},
journal= {arXiv preprint arXiv:math/0606643},
year = {2007}
}