IEEE Trans Image Process - Image segmentation by MAP-ML estimations.

Tópicos

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Resumo

Image segmentation plays an important role in computer vision and image analysis. In this paper, image segmentation is formulated as a labeling problem under a probability maximization framework. To estimate the label configuration, an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori (MAP) estimation and the maximum likelihood (ML) estimation. The MAP estimation problem is modeled with Markov random fields (MRFs) and a graph cut algorithm is used to find the solution to the MAP estimation. The ML estimation is achieved by computing the means of region features in a Gaussian model. Our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance. Its results match image edges very well and are consistent with human perception. Comparing to six state-of-the-art algorithms, extensive experiments have shown that our algorithm performs the best.

Resumo Limpo

imag segment play import role comput vision imag analysi paper imag segment formul label problem probabl maxim framework estim label configur iter optim scheme propos altern carri maximum posteriori map estim maximum likelihood ml estim map estim problem model markov random field mrfs graph cut algorithm use find solut map estim ml estim achiev comput mean region featur gaussian model algorithm can automat segment imag region relev textur color without need know number region advanc result match imag edg well consist human percept compar six stateoftheart algorithm extens experi shown algorithm perform best

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