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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/29053462/implicit-block-diagonal-low-rank-representation
#1
Xingyu Xie, Xianglin Guo, Guangcan Liu, Jun Wang
While current block diagonal constrained subspace clustering methods are performed explicitly on the original data space, in practice it is often more desirable to embed the block diagonal prior into the reproducing kernel Hilbert feature space by kernelization techniques, as the underlying data structure in reality is usually nonlinear. However, it is still unknown how to carry out the embedding and kernelization in the models with block diagonal constraints. In this work, we shall take a step in this direction...
October 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29053457/partial-deconvolution-with-inaccurate-blur-kernel
#2
Dongwei Ren, Wangmeng Zuo, David Zhang, Jun Xu, Lei Zhang
Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel...
October 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29053465/automated-and-robust-quantification-of-colocalization-in-dual-color-fluorescence-microscopy-a-nonparametric-statistical-approach
#3
Shulei Wang, Ellen T Arena, Kevin W Eliceiri, Ming Yuan
Colocalization is a powerful tool to study the interactions between fluorescently labeled molecules in biological fluorescence microscopy. However, existing techniques for colocalization analysis have not undergone continued development especially in regards to robust statistical support. In this paper, we examine two of the most popular quantification techniques for colocalization and argue that they could be improved upon using ideas from nonparametric statistics and scan statistics. In particular, we propose a new colocalization metric that is robust, easily implementable, and optimal in a rigorous statistical testing framework...
October 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29053461/micro-lens-based-matching-for-scene-recovery-in-lenslet-cameras
#4
Shuo Zhang, Hao Sheng, Da Yang, Jun Zhang, Zhang Xiong
Since a light field camera is able to capture more information than a traditional camera, a lot of methods, such as depth estimation, image super-resolution and view synthesis, are explored for recovering scene information. In this paper, we propose a novel framework for scene recovery based on lensletbased light field camera images. Instead of using traditional matching terms, we design a new micro-lens based matching term to calculate structure information and recover several kinds of scene information simultaneously...
October 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29053460/sdl-saliency-based-dictionary-learning-framework-for-image-similarity
#5
Rituparna Sarkar, Scott T Acton
In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features...
October 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29053458/performance-re-evaluation-on-codewords-distribution-based-optimal-combination-of-equal-average-equal-variance-equal-norm-nearest-neighbor-fast-search-algorithm-for-vector-quantization-encoding
#6
Yang Wang, Zhibin Pan, Rui Li
In the re-evaluated paper, Xie et al. proposed a new fast search algorithm for vector quantization encoding, which optimized the priority checking order of variance and norm inequality in order to speed up the encoding procedure. CPU time of different encoding algorithms is given to support their algorithm. However, firstly some of the experimental data in the re-evaluated paper is unreasonable and unrepeatable. And secondly, as an improved algorithm of Equal-average Equal-variance Equal-norm Nearest Neighbor Fast Search Algorithm (EEENNS), the re-evaluated algorithm in fact cannot achieve a better performance than the existing Improved Equal-average Equal-variance Nearest Neighbor Fast Search Algorithm (IEENNS)...
October 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028199/tensor-factorization-for-low-rank-tensor-completion
#7
Pan Zhou, Canyi Lu, Zhouchen Lin, Chao Zhang
Recently, a tensor nuclear norm (TNN) based method [1] was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028198/a-benchmark-dataset-and-saliency-guided-stacked-autoencoders-for-video-based-salient-object-detection
#8
Jia Li, Changqun Xia, Xiaowu Chen
Image-based salient object detection (SOD) has been extensively studied in past decades. However, video-based SOD is much less explored due to the lack of large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos. In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects who free-view all videos...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028197/multi-task-vehicle-detection-with-region-of-interest-voting
#9
Wenqing Chu, Yao Liu, Chen Shen, Deng Cai, Xian-Sheng Hua
Vehicle detection is a challenging problem in autonomous driving systems, due to its large structural and appearance variations. In this paper, we propose a novel vehicle detection scheme based on multi-task deep convolutional neural networks (CNN) and region-of-interest (RoI) voting. In the design of CNN architecture, we enrich the supervised information with subcategory, region overlap, bounding-box regression and category of each training RoI as a multi-task learning framework. This design allows the CNN model to share visual knowledge among different vehicle attributes simultaneously, thus detection robustness can be effectively improved...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028196/edge-probability-and-pixel-relativity-based-speckle-reducing-anisotropic-diffusion
#10
Deepak Mishra, Santanu Chaudhury, Mukul Sarkar, Arvinder Singh Soin, Vivek Sharma
Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion based speckle reducing filter is proposed in this paper...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028195/tensor-rank-preserving-discriminant-analysis-for-facial-recognition
#11
Dapeng Tao, Yanan Guo, Yaotang Li, Xinbo Gao
Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028194/temporal-scalability-of-dynamic-volume-data-using-mesh-compensated-wavelet-lifting
#12
Wolfgang Schnurrer, Niklas Pallast, Thomas Richter, Andre Kaup
Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028193/landmark-based-shape-encoding-and-sparse-dictionary-learning-in-the-continuous-domain
#13
Daniel Schmitter, Michael Unser
We provide a generic framework to learn shape dictionaries of landmark-based curves that are defined in the continuous domain. We first present an unbiased alignment method that involves the construction of a mean shape as well as training sets whose elements are subspaces that contain all affine transformations of the training samples. The alignment relies on orthogonal projection operators that have a closed form. We then present algorithms to learn shape dictionaries according to the structure of the data that needs to be encoded: a) projectionbased functional principal-component analysis for homogeneous data and b) continuous-domain sparse shape encoding to learn dictionaries that contain imbalanced data, outliers, or different types of shape structures...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028192/reversion-correction-and-regularized-random-walk-ranking-for-saliency-detection
#14
Yuchen Yuan, Changyang Li, Jinman Kim, Weidong Cai, David Dagan Feng
In recent saliency detection research, many graph-based algorithms have applied boundary priors as background queries, which may generate completely "reversed" saliency maps if the salient objects are on the image boundaries. Moreover, these algorithms usually depend heavily on pre-processed superpixel segmentation, which may lead to notable degradation in image detail features. In this paper, a novel saliency detection method is proposed to overcome the above issues. First, we propose a saliency reversion correction (RC) process, which locates and removes the boundary-adjacent foreground superpixels, and thereby increases the accuracy and robustness of the boundary prior based saliency estimations...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028191/deep-neural-networks-for-no-reference-and-full-reference-image-quality-assessment
#15
Sebastian Bosse, Dominique Maniry, Klaus-Robert Muller, Thomas Wiegand, Wojciech Samek
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained endto- end and comprises 10 convolutional layers and 5 pooling layers for feature extraction, and 2 fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that (1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and (2) it allows for joint learning of local quality and local weights, i...
October 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28991745/multiple-level-feature-based-measure-for-retargeted-image-quality
#16
Yabin Zhang, Weisi Lin, Qiaohong Li, Wentao Cheng, Xinfeng Zhang
Objective image retargeting quality assessment (IRQA) aims to use computational models to predict the retargeted image quality consistent with subjective perception. In this paper we propose a multiple-level feature (MLF) based quality measure to predict the perceptual quality of retargeted images. We first provide an in-depth analysis on the low-level aspect ratio similarity feature, and then propose a mid-level edge group similarity feature, to better address the shape/structure related distortion. Furthermore, a high-level face block similarity feature is designed to deal with sensitive region deformation...
October 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28991744/convolutional-dictionary-learning-acceleration-and-convergence
#17
Il Yong Chun, Jeffrey A Fessler
Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems...
October 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28991743/curvature-integration-in-a-5d-kernel-for-extracting-vessel-connections-in-retinal-images
#18
Samaneh Abbasi-Sureshjani, Marta Favali, Giovanna Citti, Alessandro Sarti, Bart M Ter Haar Romeny
Tree-like structures such as retinal images are widely studied in computer-aided diagnosis systems for large-scale screening programs. Despite several segmentation and tracking methods proposed in the literature, there still exist several limitations specifically when two or more curvilinear structures cross or bifurcate, or in the presence of interrupted lines or highly curved blood vessels. In this paper, we propose a novel approach based on multi-orientation scores augmented with a contextual affinity matrix, which both are inspired by the geometry of the primary visual cortex (V1) and their contextual connections...
October 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28991742/a-physics-based-deep-learning-approach-to-shadow-invariant-representations-of-hyperspectral-images
#19
Lloyd Windrim, Rishi Ramakrishnan, Arman Melkumyan, Richard J Murphy
This paper proposes the Relit Spectral Angle- Stacked Autoencoder, a novel unsupervised feature learning approach for mapping pixel reflectances to illumination invariant encodings. This work extends the Spectral Angle-Stacked Autoencoder so that it can learn a shadow-invariant mapping. The method is inspired by a deep learning technique, Denoising Autoencoders, with the incorporation of a physics-based model for illumination such that the algorithm learns a shadow invariant mapping without the need for any labelled training data, additional sensors, a priori knowledge of the scene or the assumption of Planckian illumination...
October 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28991741/variational-decompression-of-image-data-from-djvu-encoded-files
#20
Martin Holler, Kamil S Kazimierski
The DjVu file format and image compression techniques are widely used in the archival of digital documents. Its key ingredients are the separation of the document into foreand background layers and a binary switching mask, followed by a lossy, transform-based compression of the former and a dictionary-based compression of the latter. The lossy compression of the layers is based on a wavelet decomposition and bit truncation, which leads, in particular at higher compression rates, to severe compression artifacts in the standard decompression of the layers...
October 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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