ADAPTIVE, NONPARAMETRIC MARKOV MODELS AND INFORMATION-THEORETIC METHODS FOR IMAGE RESTORATION AND SEGMENTATION

منو

" ADAPTIVE, NONPARAMETRIC MARKOV MODELS AND INFORMATION-THEORETIC METHODS FOR IMAGE RESTORATION AND SEGMENTATION "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	152109
Doc. No	:	ET23901
Main Entry	:	Suyash P. Awate
Title Proper	:	ADAPTIVE, NONPARAMETRIC MARKOV MODELS AND INFORMATION-THEORETIC METHODS FOR IMAGE RESTORATION AND SEGMENTATION
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	The regularity in data fundamentally distinguishes itself from random noise. De-scribing this regularity in generic, yet powerful, ways is one of the key problems insignal processing. One way of capturing image regularity is by incorporating a prioriinformation into the image model itself. Approaches extracting such prior informationfrom training data have limited utility because of the lack of effective training sets formost applications. Unsupervised approaches that, typically, encode prior informationvia parametric models work best only when the data conforms to that model. Certainkinds of problems do not adhere to strict models, entailing unsupervised approaches tobe adaptive. Statistical-.
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL48047.pdf
Title and statement of responsibility and	:	ADAPTIVE, NONPARAMETRIC MARKOV MODELS AND INFORMATION-THEORETIC METHODS FOR IMAGE RESTORATION AND SEGMENTATION [Thesis]