Poster: A Hidden Markov Model for Copy Number Variant prediction from Whole genome resequencing data

Image segmentation is one of the fundamental problems in computer vision. In this work, we present a new segmentation algorithm that is based on the theory of twodimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not apply the Viterbi Algorithm, instead we present a computationally efficient algorithm that propagates the state probabilities through the image. This approach can easily be extended to higher dimensions. We compare the proposed method with a 2D-HMM standard algorithm and Iterated Conditional Modes using real world images like a radiography or a satellite image as well as synthetic images. The experimental results show that our approach is highly capable of condensing image segments. This gives our algorithm a significant advantage over the standard algorithm when dealing with noisy images with few classes.

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A New Approach to Image Segmentation with Two-Dimensional Hidden Markov Models

Baumgartner

Flesia

Giménez

et al. 2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

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Poster: A Hidden Markov Model for Copy Number Variant prediction from Whole genome resequencing data

Cited by 1 publication

References 18 publications

A New Approach to Image Segmentation with Two-Dimensional Hidden Markov Models

A New Approach to Image Segmentation with Two-Dimensional Hidden Markov Models

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