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In this paper, we present a method for stereo super-resolution which employs a deep network. The network is trained using the residual image so as to obtain a high resolution image from two, low resolution views. Our network is comprised by two deep sub-nets which share, at their output, a single convolutional layer. This last layer in the network delivers an estimate of the residual image which is...
In this paper, we present a method which aims at learning a Gaussian basis which can be used to represent the reflectance spectra in the image while yielding a high recognition rate when used as input to an SVM classifier. To do this, we view the reflectance spectra as a Gaussian mixture and depart from a maximum-likelihood formulation which allows the introduction of posterior probabilities as a...
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