IEEE Trans Image Process - Dictionary learning for stereo image representation.

Tópicos

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Resumo

One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of the visual information. Sparse image representations with overcomplete geometric dictionaries offer a way to efficiently approximate these images, such that the multi-view geometric structure becomes explicit in the representation. However, the choice of a good dictionary in this case is far from obvious. We propose a new method for learning overcomplete dictionaries that are adapted to the joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms of dictionary elements (atoms) in two stereo views. A maximum-likelihood (ML) method for learning stereo dictionaries is then proposed, where a multi-view geometry constraint is included in the probabilistic model. The ML objective function is optimized using the expectation-maximization algorithm. We apply the learning algorithm to the case of omnidirectional images, where we learn scales of atoms in a parametric dictionary. The resulting dictionaries provide better performance in the joint representation of stereo omnidirectional images as well as improved multi-view feature matching. We finally discuss and demonstrate the benefits of dictionary learning for distributed scene representation and camera pose estimation.

Resumo Limpo

one major challeng multiview imag definit represent reveal intrins geometri visual inform spars imag represent overcomplet geometr dictionari offer way effici approxim imag multiview geometr structur becom explicit represent howev choic good dictionari case far obvious propos new method learn overcomplet dictionari adapt joint represent stereo imag first formul spars stereo imag model multiview correl describ local geometr transform dictionari element atom two stereo view maximumlikelihood ml method learn stereo dictionari propos multiview geometri constraint includ probabilist model ml object function optim use expectationmaxim algorithm appli learn algorithm case omnidirect imag learn scale atom parametr dictionari result dictionari provid better perform joint represent stereo omnidirect imag well improv multiview featur match final discuss demonstr benefit dictionari learn distribut scene represent camera pose estim

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