<![CDATA[ Image Processing, IEEE Transactions on - new TOC ]]>
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TOC Alert for Publication# 83 2016February 08<![CDATA[Subpixel-Based Image Scaling for Grid-like Subpixel Arrangements: A Generalized Continuous-Domain Analysis Model]]>253101710324596<![CDATA[Deep Fusion of Multiple Semantic Cues for Complex Event Recognition]]>253103310466336<![CDATA[Separability Criteria for the Evaluation of Boundary Detection Benchmarks]]>253104710551958<![CDATA[Sparse Contextual Activation for Efficient Visual Re-Ranking]]>253105610692056<![CDATA[Analysis of Crosstalk in 3D Circularly Polarized LCDs Depending on the Vertical Viewing Location]]>253107010834439<![CDATA[Feature and Region Selection for Visual Learning]]>2 and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.]]>253108410944050<![CDATA[Motion Estimation Based on Mutual Information and Adaptive Multi-Scale Thresholding]]>253109511082908<![CDATA[Identification of Transform Coding Chains]]>253110911233907<![CDATA[Unified Photo Enhancement by Discovering Aesthetic Communities From Flickr]]>253112411353989<![CDATA[Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images]]>253113611514150<![CDATA[Efficient, Edge-Aware, Combined Color Quantization and Dithering]]>253115211626054<![CDATA[Robust Point Set Matching for Partial Face Recognition]]>253116311763950<![CDATA[LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation]]> -norm sparse coding. Furthermore, we propose a joint learning model for simultaneous optimization of the sparse coding and the optimal subspace representation. In addition, we generalize the proposed LSDT model into a kernel-based linear/nonlinear basis transformation learning framework for tackling nonlinear subspace shifts in reproduced kernel Hilbert space. The proposed methods have three advantages: 1) the latent space and the reconstruction are jointly learned for pursuit of an optimal subspace transfer; 2) with the theory of sparse subspace clustering, a few valuable source and target data points are formulated to reconstruct the target data with noise (outliers) from source domain removed during domain adaptation, such that the robustness is guaranteed; and 3) a nonlinear projection of some latent space with kernel is easily generalized for dealing with highly nonlinear domain shift (e.g., face poses). Extensive experiments on several benchmark vision data sets demonstrate that the proposed approaches outperform other state-of-the-art representation-based domain adaptation methods.]]>253117711915768<![CDATA[Correction to “Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images”]]>[1], the spelling of the second author’s name was incorrect. The correct spelling is as follows:]]>2531192119270<![CDATA[Shapes From Pixels]]>253119312063037<![CDATA[A Forward Regridding Method With Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms]]>a priori knowledge, have been developed to tackle this problem during the last few decades. Most of these iterative algorithms are based on an implementation of the Radon transform that acts as forward projector. This operator and its adjoint, the backprojector, are typically called few times per iteration and represent the computational bottleneck of the reconstruction process. Here, we present a Fourier-based forward projector, founded on the regridding method with minimal oversampling. We show that this implementation of the Radon transform significantly outperforms in efficiency other state-of-the-art operators with O(N^{2}log_{2}N) complexity. Despite its reduced computational cost, this regridding method provides comparable accuracy to more sophisticated projectors and can, therefore, be exploited in iterative algorithms to substantially decrease the time required for the reconstruction of underconstrained tomographic data sets without loss in the quality of the results.]]>253120712185492<![CDATA[A Retinal Mechanism Inspired Color Constancy Model]]>253121912324102<![CDATA[Face Alignment via Regressing Local Binary Features]]>253123312454441<![CDATA[CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking]]>a posteriori framework, and a novel algorithm for image deblocking using constrained non-convex low-rank model is proposed. The penalty function is extended on singular values of a matrix to characterize low-rank prior model rather than the nuclear norm, while the quantization constraint is explicitly transformed into the feasible solution space to constrain the non-convex low-rank optimization. Moreover, a new quantization noise model is developed, and an alternatively minimizing strategy with adaptive parameter adjustment is developed to solve the proposed optimization problem. This parameter-free advantage enables the whole algorithm more attractive and practical. Experiments demonstrate that the proposed image deblocking algorithm outperforms the current state-of-the-art methods in both the objective quality and the perceptual quality.]]>253124612594934<![CDATA[Correction to “An Efficient Adaptive Binary Arithmetic Coder Based on Logarithmic Domain”]]>[1], the following sentence should have been included in the first footnote.]]>2531260126069<![CDATA[Visual Object Tracking Performance Measures Revisited]]>253126112742364<![CDATA[Discriminative Semantic Subspace Analysis for Relevance Feedback]]>253127512871697<![CDATA[Beyond Color Difference: Residual Interpolation for Color Image Demosaicking]]>253128813005970<![CDATA[Interactive Image Segmentation Using Adaptive Constraint Propagation]]>253130113113343