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IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society

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https://www.readbyqxmd.com/read/28320667/learning-correspondence-structures-for-person-re-identification
#1
Weiyao Lin, Yang Shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu
This paper addresses the problem of handling spatial misalignments due to camera-view changes or humanpose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or humanpose variation in individual images. We further introduce a global constraint-based matching process...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320666/rgbd-salient-object-detection-via-deep-fusion
#2
Liangqiong Qu, Shengfeng He, Jiawei Zhang, Jiandong Tian, Yandong Tang, Qingxiong Yang
Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320665/disjunctive-normal-parametric-level-set-with-application-to-image-segmentation
#3
Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen
Level set methods are widely used for image segmentation because of their convenient shape representation for numerical computations, and capability to handle topological changes. However, in spite of the numerous works in the literature, the use of level set methods in image segmentation still has several drawbacks. These shortcomings include formation of irregularities of the signed distance function, sensitivity to initialization, lack of locality, and expensive computational cost which increases dramatically as the number of objects to be simultaneously segmented grows...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320664/blind-forensics-of-successive-geometric-transformations-in-digital-images-using-spectral-method-theory-and-applications
#4
Jiangqun Ni, Chenglong Chen, Zhaoyi Shen, Yun-Qing Shi
Geometric transformations, such as resizing and rotation, are almost always needed when two or more images are spliced together to create convincing image forgeries. In recent years, researchers have developed many digital forensic techniques to identify these operations. Most previous works in this area focus on the analysis of images that have undergone single geometric transformations, e.g., resizing or rotation. In several recent works, researchers have addressed yet another practical and realistic situation: successive geometric transformations, e...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320661/fast-unsupervised-bayesian-image-segmentation-with-adaptive-spatial-regularisation
#5
Marcelo Pereyra, Stephen McLaughlin
This paper presents a new Bayesian estimation technique for hidden Potts-Markov random fields with unknown regularisation parameters, with application to fast unsupervised K-class image segmentation. The technique is derived by first removing the regularisation parameter from the Bayesian model by marginalisation, followed by a small-variance-asymptotic (SVA) analysis in which the spatial regularisation and the integer-constrained terms of the Potts model are decoupled. The evaluation of this SVA Bayesian estimator is then relaxed into a problem that can be computed efficiently by iteratively solving a convex total-variation denoising problem and a least-squares clustering (K-means) problem, both of which can be solved straightforwardly, even in high-dimensions, and with parallel computing techniques...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320663/iterative-re-constrained-group-sparse-face-recognition-with-adaptive-weights-learning
#6
Jianwei Zheng, Ping Yang, Shengyong Chen, Guojiang Shen, Wanliang Wang
In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier with adaptive weights learning (IRGSC). Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence...
March 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320662/codebook-guided-feature-preserving-for-recognition-oriented-image-retargeting
#7
Bo Yan, Weimin Tan, Ke Li, Qi Tian
Traditional image resizing methods, such as uniform scaling and content-aware image retargeting, are designed to preserve the visually salient contents of an image while resizing it. In this paper, we propose a novel image resizing approach called recognition-oriented image retargeting. Its goal is to preserve the distinctive local features for recognition instead of the traditional visual saliency during resizing. Moreover, we also apply our approach to image matching and image retrieval applications to verify its performance...
March 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28287974/low-complexity-multidimensional-dct-approximations-for-high-order-tensor-data-decorrelation
#8
Vitor A Coutinho, Renato J Cintra, Fabio M Bayer
In this paper, we introduce low-complexity multidimensional discrete cosine transform (DCT) approximations. Three dimensional DCT (3D DCT) approximations are formalized in terms of high-order tensor theory. The formulation is extended to higher dimensions with arbitrary lengths. Several multiplierless 8 ˆ 8 ˆ 8 approximate methods are proposed and the computational complexity is discussed for the general multidimensional case. The proposed methods complexity cost was assessed, presenting considerably lower arithmetic operations when compared with the exact 3D DCT...
March 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28287973/optimized-color-filter-arrays-for-sparse-representation-based-demosaicking
#9
Jia Li, Chenyan Bai, Zhouchen Lin, Jian Yu
Demosaicking is the problem of reconstructing a color image from the raw image captured by a digital color camera that covers its only imaging sensor with a color filter array (CFA). Sparse representation based demosaicking has been shown to produce superior reconstruction quality. However, almost all existing algorithms in this category use the CFAs which are not specifically optimized for the algorithms. In this paper, we consider optimally designing CFAs for sparse representation based demosaicking, where the dictionary is well-chosen...
March 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28287968/style-transfer-via-texture-synthesis
#10
Michael Elad, Peyman Milanfar
Style transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image which is an artistic mixture of the two. Recent work on this problem adopting Convolutional Neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path towards handling the style transfer task, via generalization of texture synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared to the CNN ones...
March 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28287972/videography-based-unconstrained-video-analysis
#11
Kang Li, Sheng Li, Sangmin Oh, Yun Fu
Video analysis and understanding play a central role in visual intelligence. In this paper, we aim to analyze unconstrained videos, by designing features and approaches to represent and analyze videography styles in the videos. Videography denotes the process of making videos. The unconstrained videos are defined as the long duration consumer videos that usually have diverse editing artifacts and significant complexity of contents. We propose to construct a videography dictionary, which can be utilized to represent every video clip as a sequence of videography words...
March 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28287971/fast-2d-convolutions-and-cross-correlations-using-scalable-architectures
#12
Cesar Carranza, Daniel Llamocca, Marios Pattichis
The manuscript describes fast and scalable architectures and associated algorithms for computing convolutions and cross-correlations. The basic idea is to map 2D convolutions and cross-correlations to a collection of 1D convolutions and cross-correlations in the transform domain. This is accomplished through the use of the Discrete Periodic Radon Transform (DPRT) for general kernels and the use of SVD-LU decompositions for low-rank kernels.
March 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28287970/dct-regularized-extreme-visual-recovery
#13
Yunhe Wang, Chang Xu, Shan You, Chao Xu, Dacheng Tao
Here we study the extreme visual recovery problem, in which over 90% of pixel values in a given image are missing. Existing low rank-based algorithms are only effective for recovering data with at most 90% missing values. Thus, we exploit visual data's smoothness property to help solve this challenging extreme visual recovery problem. Based on the Discrete Cosine Transform (DCT), we propose a novel DCT regularizer that involves all pixels and produces smooth estimations in any view. Our theoretical analysis shows that the total variation (TV) regularizer, which only achieves local smoothness, is a special case of the proposed DCT regularizer...
March 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28287969/a-variational-bayesian-approach-to-multiframe-image-restoration
#14
Motoharu Sonogashira, Takuya Funatomi, Masaaki Iiyama, Michihiko Minoh
Image restoration is a fundamental problem in the field of image processing. The key objective of image restoration is to recover clean images from images degraded by noise and blur. Recently, a family of new statistical techniques called variational Bayes (VB) has been introduced to image restoration, which enables us to automatically tune parameters that control restoration. While information from one image is often insufficient for high-quality restoration, however, current state-of-theart methods of image restoration via VB approaches use only a single degraded image to recover a clean image...
March 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28278469/deep-hashing-for-scalable-image-search
#15
Jiwen Lu, Venice Erin Liong, Jie Zhou
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible...
March 3, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28278468/blind-deconvolution-with-model-discrepancies
#16
Jan Kotera, Vaclav Smidl, Filip Sroubek
Blind deconvolution is a strongly ill-posed problem comprising of simultaneous blur and image estimation. Recent advances in prior modeling and/or inference methodology led to methods that started to perform reasonably well in real cases. However, as we show here, they tend to fail if the convolution model is violated even in a small part of the image. Methods based on Variational Bayesian inference play a prominent role. In this work, we use this inference in combination with the same prior for noise, image and blur that belongs to the family of independent non-identical Gaussian distributions, known as the Automatic Relevance Determination prior...
March 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28278467/mixed-noise-removal-via-laplacian-scale-mixture-modeling-and-nonlocal-low-rank-approximation
#17
Tao Huang, Weisheng Dong, Xuemei Xie, Guangming Shi, Xiang Bai
Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse noise (IN) is a challenging problem due to its difficulties in accurate modeling of the distributions of the mixture noise. Many efforts have been made to first detect the locations of the impulse noise and then recover the clean image with image inpainting techniques from an incomplete image corrupted by AWGN. However, it is quite challenging to accurately detect the locations of the impulse noise when the mixture noise is strong...
March 1, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28278466/geodesic-video-stabilization-in-transformation-space
#18
Lei Zhang, Xiao-Quan Chen, Xin-Yi Kong, Hua Huang
We present a novel formulation of video stabilization in the space of geometric transformations. With the setting of the Riemannian metric, the optimized smooth path is cast as the geodesics on the Lie group embedded in transformation space. While solving the geodesics has a closed-form expression in a certain space, path smoothing can be easily implemented by using geometric interpolation, rather than optimizing any space-time energy function. Specially, by using the geodesic solution in the space of rigid transformations, our approach even gains speedup 10 faster than state-of-the-art methods for path smoothing and motion compensation, and guarantees no extra distortion drawn into the stabilized frames...
March 1, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28278465/fast-pixel-wise-adaptive-visual-tracking-of-non-rigid-objects
#19
Stefan Duffner, Christophe Garcia
In this paper, we present a new algorithm for realtime single-object tracking in videos in unconstrained environments. The algorithm comprises two different components that are trained "in one shot" at the first video frame: a detector that makes use of the generalised Hough transform with colour and gradient descriptors, and a probabilistic segmentation method based on global models for foreground and background colour distributions. Both components work at pixel level and are used for tracking in a combined way adapting each other in a cotraining manner...
March 1, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28278464/learning-discriminative-binary-codes-for-large-scale-cross-modal-retrieval
#20
Xing Xu, Fumin Shen, Yang Yang, Heng Tao Shen, Xuelong Li
Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying correlations between heterogeneous features from different modalities. A majority of recent approaches aim at learning hash functions to preserve the pairwise similarities defined by given class labels. However, these methods fail to explicitly explore the discriminative property of class labels during hash function learning...
March 1, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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