José R. A. Torreão

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Binocular disparities arise from positional differences of scene features projected in the two retinae, and constitute the primary sensory cue for stereo vision. Here we introduce a new computational model for disparity estimation, based on the Green’s function of an image matching equation. When filtering a Gabor-function-modulated signal, the considered(More)
We present a new algorithm for shape from shading, inspired on the recently introduced disparity-based approach to photometric stereo. Assuming that the single input image will be matched to a second image through a uniform disparity eld, we construct an irradiance conservation equation and solve it for the matching image, via Green's function. When a(More)
The estimation of derivatives is an important and sensitive task in digital image processing and analysis, both accuracy and computational efficiency being expected of a differential operator. Here we propose a new filter—designed through a strategy based on the Green’s function of a signal matching equation—that responds to such demands. When used for edge(More)
We describe a data-driven motion simulation algorithm based on an affine image-matching equation. Solving such equation via the Green's function approach, we have obtained a pair of filters which, when applied over an input image, allow the generation of virtual sequences that convey a compelling motion impression. Complex rigid and nonrigid motions have(More)
We present the study of a data-driven motion synthesis approach based on a 1D affine image-matching equation. We start by deriving the relevant properties of the exact matching operator, such as the existence of a singular point. Next, we approximate such operator by the Green’s function of a second-order differential equation, finding that it leads to a(More)
We present a Bayesian approach to the machine vision processes of shape-from-shading and photometric stereo, also considering the associated question of the detection of shape discontinuities. The shape reconstruction problem is formulated as a maximum a posteriori (MAP) estimation from probability distributions of Gibbs form, and is solved via simulated(More)