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In this paper, we introduce a novel local feature-based hierarchical framework to produce invariant sparse codes for object recognition. In order to enforce the invariant property for each sample patch (local feature descriptor) in the image, its sparse code is recovered with a dedicated dictionary whose atoms are adaptively chosen from several bags of candidate atoms. The single-layer invariant sparse...
Sparsity-based techniques have been popular in many applications in signal and image processing. In particular, the data-driven adaptation of sparse signal models such as the synthesis model has shown promise in applications. However, dictionary learning problems are typically nonconvex and NP-hard, and the usual alternating minimization approaches for learning are often expensive and lack convergence...
Recently deep learning methods have been applied to image super-resolution (SR). Typically, these approaches involve training a single convolutional neural network that is trained to perform resolution enhancement. We propose a new low-complexity but effective algorithm called Superresolution with Coupled Backpropagation (SR-CBP) which builds two Coupled Auto-encoder Networks (CAN), resp. the high-resolution...
In this paper, three sparse models for the human auditory system are proposed. Biological studies shows that the haircells in the inner ear of the auditory system generate sparse codes from the output of cochlea filterbank. Here, we employ two mathematical sparse representation methods, which are Orthogonal Matching Pursuit (OMP) and K Singular Value Decomposition (K-SVD), in three different strategies...
In contrast to still image analysis, motion information offers a powerful means to analyze video. In particular, motion trajectories determined from keypoints have become very popular in recent years for a variety of video analysis tasks, including search, retrieval and classification. Additionally, cloud-based analysis of media content has been gaining momentum, so efficient communication of salient...
Product codes are a concatenated error-correction scheme that has been often considered for applications requiring very low bit-error rates, which demand that the error floor be decreased as much as possible. In this work, we consider product codes constructed from polynomial algebraic codes, and propose a novel low-complexity post-processing technique that is able to improve the error-correction...
In this paper, a duality between wiretap and state-dependent channels with non-causal channel state information at the transmitter is established. First, a common achievable scheme is described for a certain class of state-dependent and wiretap channels. Further, state-dependent and wiretap channels for which this scheme is capacity (resp. secrecy capacity) achieving are identified. These channels...
We study a secure communication scenario in which the channel is under two classes of attacks at the same time: a passive eavesdropper and an active jammer. This scenario is modelled using the arbitrarily varying wiretap channel (AVWC), in which the channel varies from one channel use to the other. It has been shown that in general reliable communication over AVWCs requires some sort of coordination...
The data-driven adaptation of synthesis dictionaries has been exploited in many applications in signal processing and imaging. This paper exploits efficient methods for aggregate sparsity penalized dictionary learning by first approximating the matrix of image patches with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns,...
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