IEEE Trans Pattern Anal Mach Intell - Maximum Likelihood Estimation of Depth Maps Using Photometric Stereo.

Tópicos

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Resumo

Photometric stereo and depth-map estimation provide a way to construct a depth map from images of an object under one viewpoint but with varying illumination directions. While estimating surface normals using the Lambertian model of reflectance is well established, depth-map estimation is an ongoing field of research and dealing with image noise is an active topic. Using the zero-mean Gaussian model of image noise, this paper introduces a method for maximum likelihood depth-map estimation that accounts for the propagation of noise through all steps of the estimation process. Solving for maximum likelihood depth-map estimates involves an independent sequence of nonlinear regression estimates, one for each pixel, followed by a single large and sparse linear regression estimate. The linear system employs anisotropic weights, which arise naturally and differ in value to related work. The new depth-map estimation method remains efficient and fast, making it practical for realistic image sizes. Experiments using synthetic images demonstrate the method's ability to robustly estimate depth maps under the noise model. Practical benefits of the method on challenging imaging scenarios are illustrated by experiments using the Extended Yale Face Database B and an extensive data set of 500 reflected light microscopy image sequences.

Resumo Limpo

photometr stereo depthmap estim provid way construct depth map imag object one viewpoint vari illumin direct estim surfac normal use lambertian model reflect well establish depthmap estim ongo field research deal imag nois activ topic use zeromean gaussian model imag nois paper introduc method maximum likelihood depthmap estim account propag nois step estim process solv maximum likelihood depthmap estim involv independ sequenc nonlinear regress estim one pixel follow singl larg spars linear regress estim linear system employ anisotrop weight aris natur differ valu relat work new depthmap estim method remain effici fast make practic realist imag size experi use synthet imag demonstr method abil robust estim depth map nois model practic benefit method challeng imag scenario illustr experi use extend yale face databas b extens data set reflect light microscopi imag sequenc

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