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In this paper, we aim at synthesizing a texture from a high-resolution patch and a low-resolution image. To do so, we solve a nonconvex optimization problem that involves a statistical prior and a Fourier spectrum constraint. The numerical analysis shows that the proposed approach achieves better results (in terms of visual quality) than state-of-the-art methods tailored to super-resolution or texture...
This paper investigates variational region-level criterion for supervised and unsupervised texture-based image segmentation. The focus is given to the demonstration of the effectiveness and robustness of this region-based formulation compared to most common variational approaches. The main contributions of this global criterion are twofold. First, the proposed methods circumvent a major problem related...
This paper addresses weakly supervised object recognition. We show how the combination of an image-level inference, in terms of image-level object class priors, can lead to better training of object recognition models. Stated within a probabilistic setting, the proposed approach is applied to fisheries acoustics and fish school recognition.
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