<![CDATA[ IEEE Transactions on Image Processing - new TOC ]]>
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TOC Alert for Publication# 83 2017September21<![CDATA[Specificity and Latent Correlation Learning for Action Recognition Using Synthetic Multi-View Data From Depth Maps]]>2612556055743008<![CDATA[Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation]]>2612557555895475<![CDATA[Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation]]>2612559056029030<![CDATA[Nonnegative Decompositions for Dynamic Visual Data Analysis]]>unsupervised analysis of the temporal phases of facial expressions and facial action units (AUs) and 2) temporal alignment of a certain facial behavior displayed by two different persons. To this end, the slow features nonnegative matrix factorization (SFNMF) is proposed in order to learn slow varying parts-based representations of time varying sequences capturing the underlying dynamics of temporal phenomena, such as facial expressions. Moreover, the SFNMF is extended in order to handle two temporally misaligned data sequences depicting the same visual phenomena. To do so, the dynamic time warping is incorporated into the SFNMF, allowing the temporal alignment of the data sets onto the subspace spanned by the estimated nonnegative shared latent features amongst the two visual sequences. Extensive experimental results in two video databases demonstrate the effectiveness of the proposed methods in: 1) unsupervised detection of the temporal phases of posed and spontaneous facial events and 2) temporal alignment of facial expressions, outperforming by a large margin the state-of-the-art methods that they are compared to.]]>2612560356173714<![CDATA[Combination of Sharing Matrix and Image Encryption for Lossless $(k,n)$ -Secret Image Sharing]]>$(k,n)$ -sharing matrix $S^{(k, n)}$ and its generation algorithm. Mathematical analysis is provided to show its potential for secret image sharing. Combining sharing matrix with image encryption, we further propose a lossless $(k,n)$ -secret image sharing scheme (SMIE-SIS). Only with no less than $k$ shares, all the ciphertext information and security key can be reconstructed, which results in a lossless recovery of original information. This can be proved by the correctness and security analysis. Performance evaluation and security analysis demonstrate that the proposed SMIE-SIS with arbitrary settings of $k$ and $n$ has at least five advantages: 1) it is able to fully recover the original image without any distortion; 2) it has much lower pixel expansion than many existing methods; 3) its computation cost is much lower than the polynomial-based secret image sharing methods; 4) it is able to verify and detect a fake share; and 5) even using the same original image with the same initial settings of parameters, every execution of SMIE-SIS is able to generate completely different secret shares that are unpredictable and non-repetitive. This property offers SMIE-SIS a high level of security to withstand many different attacks.]]>2612561856313254<![CDATA[Convergence of Proximal Iteratively Reweighted Nuclear Norm Algorithm for Image Processing]]>2612563256443052<![CDATA[Selective Video Object Cutout]]>2612564556552196<![CDATA[Unifying the Video and Question Attentions for Open-Ended Video Question Answering]]>2612565656663382<![CDATA[A Hybrid Data Association Framework for Robust Online Multi-Object Tracking]]>2612566756793645<![CDATA[Event Detection in Continuous Video: An Inference in Point Process Approach]]>2612568056912298<![CDATA[Structure-Regularized Compressive Tracking With Online Data-Driven Sampling]]>2612569257054125<![CDATA[Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval]]>2612570657175458<![CDATA[Convex Multiview Semi-Supervised Classification]]>2612571857292489<![CDATA[Nonlocaly Multi-Morphological Representation for Image Reconstruction From Compressive Measurements]]>nonoverlapping patches with Gaussian random matrices. The results demonstrate that our algorithms can suppress undesirable block artifacts efficiently, and delivers reconstructed images with higher qualities than other state-of-the-art methods.]]>2612573057429488<![CDATA[Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos]]>2612574357574782<![CDATA[Geometric Occlusion Analysis in Depth Estimation Using Integral Guided Filter for Light-Field Image]]>2612575857719831<![CDATA[Log-Euclidean Metrics for Contrast Preserving Decolorization]]>2612577257834497<![CDATA[Unsupervised Hierarchical Dynamic Parsing and Encoding for Action Recognition]]>2612578457993545<![CDATA[Visual Tracking by Sampling in Part Space]]>part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods.]]>2612580058102792<![CDATA[Learning-Based Shadow Recognition and Removal From Monochromatic Natural Images]]>2612581158246147