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Image Labeling with Markov Random Fields and Conditional Random Fields

Salvato in:
Pubblicato in:arXiv.org (May 19, 2019), p. n/a
Autore principale: Wu, Shangxuan
Altri autori: Weng, Xinshuo
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Computer vision
Pixels
Image segmentation
Markov processes
Entropy
Markov analysis
Labeling
Conditional random fields
Accesso online:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 2139387503 
045 0 |b d20190519 
100 1 |a Wu, Shangxuan 
245 1 |a Image Labeling with Markov Random Fields and Conditional Random Fields 
260 |b Cornell University Library, arXiv.org  |c May 19, 2019 
513 |a Working Paper 
520 3 |a Most existing methods for object segmentation in computer vision are formulated as a labeling task. This, in general, could be transferred to a pixel-wise label assignment task, which is quite similar to the structure of hidden Markov random field. In terms of Markov random field, each pixel can be regarded as a state and has a transition probability to its neighbor pixel, the label behind each pixel is a latent variable and has an emission probability from its corresponding state. In this paper, we reviewed several modern image labeling methods based on Markov random field and conditional random Field. And we compare the result of these methods with some classical image labeling methods. The experiment demonstrates that the introduction of Markov random field and conditional random field make a big difference in the segmentation result. 
653 |a Computer vision 
653 |a Pixels 
653 |a Image segmentation 
653 |a Markov processes 
653 |a Entropy 
653 |a Markov analysis 
653 |a Labeling 
653 |a Conditional random fields 
700 1 |a Weng, Xinshuo 
773 0 |t arXiv.org  |g (May 19, 2019), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2139387503/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/1811.11323