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Present day modern hand held communicating devices rely on forward error correction techniques for their proper functioning. Most digital communication systems nowadays convolutionally encode the transmitted data to compensate for Additive White Gaussian Noise (AWGN), fading of the channel, quantization distortions and other data degradation effects. For its efficient performance the Viterbi algorithm...
Stereo video offers enhanced visual experience for daily entertainment and attracts much attention in industry and academia recently. It is essential to make the delivery of 3D video efficient and friendly to the wide public. To reduce the stereo data and also reuse the existing infrastructure and equipment for 2D video, a frame-compatible stereo video format is usually used. In this format, the stereo...
We address the problem of automatically detecting anomalies in images, i.e., patterns that do not conform to those appearing in a reference training set. This is a very important feature for enabling an intelligent system to autonomously check the validity of acquired data, thus performing a preliminary, automatic, diagnosis. We approach this problem in a patch-wise manner, by learning a model to...
One of important applications of dynamic contrast-enhanced (DCE) MRI is to observe brain tissue dynamics. In this work, we applied a combined approach of dynamic compressed sensing (CS) and parallel imaging for volumetric DCE-MRI. The combination of the parallel imaging with CS reconstruction has been a topic of significant interest in MR community due to its potential for further acceleration in...
This paper proposes a novel variational dimension frequency domain method for feature-adaptive mesh Representation. The eigen functions given by the eigen decomposition of the Laplace-Beltrami operator are used to define Fourier like function basis to project the actual geometry of the mesh into the spectral space because of their geometry aware and orthogonal. But, low-pass filters based on Fourier-like...
Surveillance videos show a prominent feature of background redundancy. To full use this, we propose an adaptive background-frame based coding method for surveillance videos in this paper. In this algorithm, the adaptive background image is used as a long-term reference frame to provide better prediction for inter-frame coding. The background image is generated and updated dynamically by using layer...
We propose a single-image super-resolution algorithm based on sparse representation over a set of cluster dictionary pairs. For each cluster, a directionally structured dictionary pair is designed. The dominant angle in the patch gradient phase matrix is employed as an approximately scale-invariant measure. This measure serves for patch clustering and sparse model selection. The dominant phase angle...
Data compression on sensing data in Wireless Sensor Networks (WSNs) has long been the topic of extensive research in the last decade. Especially, in video sensor networks, the video and image data that need to be transmitted are relatively larger than common data. However WSNs usually have limited power supply and constrained communication bandwidth, it is significant to reduce the video and image...
Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary. It was successfully applied on a wide range of applications like the textual image Super-Resolution. Nevertheless, its complexity limits enormously its application. Looking for a reduced computational complexity, a coupled dictionary learning approach is proposed to generate...
Partially occluded or illuminated faces pose a significant obstacle for robust, real-world face recognition. The problem of how to characterize the error caused by occlusion or illumination is still a challenging task. There must exist some close relationship between the error metric and error distribution. However, some metric (e.g. Z2-norm) can't characterize this error distribution completely....
In this paper, we develop a new efficient graph construction algorithm that is useful for many learning tasks. Unlike the main stream for graph construction, our proposed data self-representativeness approach simultaneously estimates the graph structure and its edge weights through sample coding. Compared with the recent l1 graph that is based on sparse coding, our proposed objective function has...
Secret sharing is a technique that can distribute partial secret information (also called 'shares') to a specific group member. These individual shares are of no use on their own, but they can reconstruct the original secret information when the members collect all of the shares. In this paper, we proposed a novel, polynomial-based, secret sharing scheme using the absolute moment block truncation...
Nonlinear dimensionality reduction (DR) is a basic problem in manifold learning. However, many DR algorithms cannot deal with the out-of-sample extension problem and thus cannot be used in large-scale DR problem. Furthermore, many DR algorithms only consider how to reduce the dimensionality but seldom involve with how to reconstruct the original high dimensional data from the low dimensional embeddings...
In this paper, an in-loop sample edge offset compensation (SEOC) framework is proposed for High Efficiency Video Coding (HEVC) based 3D video (3D-HEVC) coding. The framework targets at improving the reconstruction quality of the depth images, especially in edge areas. In a typical 3DV system, depth images are used for synthesizing the virtual views, therefore preserving high quality depth images,...
In this paper, we propose to address online visual tracking on the basis of Local Coordinate Coding (LCC), which integrates the advantages of the discriminative method and the generative method. In the discriminative module, a nonlinear function is trained using the local coordinate codes of image patches to identify the foreground patches from background. In the generative module, we introduce a...
Representing images with their descriptive features is the fundamental problem in CBIR. Feature coding as a key-step in feature description has attracted the attentions in recent years. Among the proposed coding strategies, Bag-of-Words (BoW) is the most widely used model. Recently saliency has been mentioned as the fundamental characteristic of BoW. Base on this idea, Salient Coding (SaC) has been...
The recent advances in sparse coding and dictionary learning have shown extremely good performances and robustness in high-dimensional classification problems. Most often, dictionary-based methods rely either on the reconstruction power of the dictionary or on the structure of the sparse representation. In this paper we jointly exploit the discrimination power of both approaches by combining the reconstruction...
Video-based traffic sign recognition is one of the most important task for unmanned autonomous vehicle. However, there always exists unavoidable outliers in the practical scenario. Therefore, robust prototype extraction from the noisy sample set is highly expected to help traffic sign recognition in video sequence. In this paper, we propose a novel approach for simultaneous prototype extraction and...
Autonomous Underwater Vehicles (AUVs) gather large volumes of visual imagery, which can help monitor marine ecosystems and plan future surveys. One key task in marine ecology is benthic habitat mapping, the classification of large regions of the ocean floor into broad habitat categories. Since visual data only covers a small fraction of the ocean floor, traditional habitat mapping is performed using...
This paper addresses the patch size issue in sparse representation over learned dictionaries. A strategy for selecting the best patch size is proposed. It is empirically shown that the representation quality of natural image patches depends on the patch size considered. The proposed strategy selectively chooses the most appropriate patch size based on the resulting sparse representation error. The...
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