conferenceObject
A new approach to image segmentation with two-dimensional hidden Markov models
Fecha
2013Autor
Baumgartner, Josef
Flesia, Ana Georgina
Gimenez, Javier
Pucheta, Julian
Institución
Resumen
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 2DHMM 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.