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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/28541900/simultaneous-semi-coupled-dictionary-learning-for-matching-in-canonical-space
#1
Nilotpal Das, Devraj Mandal, Soma Biswas
Cross-modal recognition and matching with privileged information are important challenging problems in the field of computer vision. The cross-modal scenario deals with matching across different modalities and needs to take care of the large variations present across and within each modality. The privileged information scenario deals with the situation that all the information available during training may not be available during the testing stage and hence algorithms need to leverage the extra information from the training stage itself...
May 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541899/motion-compensated-compression-of-dynamic-voxelized-point-clouds
#2
Ricardo L De Queiroz, Philip A Chou
Dynamic point clouds are a potential new frontier in visual communication systems. A few articles have addressed the compression of point clouds, but very few references exist on exploring temporal redundancies. This paper presents a novel motion-compensated approach to encoding dynamic voxelized point clouds at low bit rates. A simple coder breaks the voxelized point cloud at each frame into blocks of voxels. Each block is either encoded in intra-frame mode or is replaced by a motion-compensated version of a block in the previous frame...
May 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541898/con-text-text-detection-for-fine-grained-object-classification
#3
Sezer Karaoglu, Ran Tao, Jan C van Gemert, Theo Gevers
This work focuses on fine-grained object classification using recognized scene text in natural images. While the state-of-the-art relies on visual cues only, this paper is the first work which proposes to combine textual and visual cues. Another novelty is the textual cue extraction. Unlike the state-of-the-art text detection methods, we focus more on the background instead of text regions. Once text regions are detected, they are further processed by two methods to perform text recognition i.e. ABBYY commercial OCR engine and a state-of-the-art character recognition algorithm...
May 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541206/high-quality-parallel-ray-x-ray-ct-back-projection-using-optimized-interpolation
#4
Michael T McCann, Michael Unser
We propose a new, cost-efficient method for computing back projections in parallel-ray X-ray CT. Forward and back projections are the basis of almost all X-ray CT reconstruction methods, but computing these accurately is costly. In the special case of parallel-ray geometry, it turns out that reconstruction requires back projection only. One approach to accelerate the back projection is through interpolation: fit a continuous representation to samples of the desired signal, then sample it at the required locations...
May 23, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541207/an-efficient-fusion-based-defogging
#5
Jing-Ming Guo, Jin-Yu Syue, Vincent Radzicki, Hua Lee
Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. To overcome this problem, conventional approaches focus mainly on the enhancement of the overall image contrast. However, because of the unspecified light-source distribution or unsuitable mathematical constraints of the cost functions, it is often difficult to achieve quality results. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models...
May 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541205/features-classification-forest-a-novel-development-that-is-adaptable-to-robust-blind-watermarking-techniques
#6
Chia-Sung Chang, Jau-Ji Shen
A novel watermarking scheme is proposed that could substantially improve current watermarking techniques. This scheme exploits the features of micro images of watermarks to build association rules and embeds the rules into a host image instead of the bit stream of the watermark, which is commonly used in digital watermarking. Next, similar micro images with the same rules are collected or even created from the host image to simulate an extracted watermark. This method, called the Features Classification Forest, can achieve blind extraction and is adaptable to any watermarking scheme using a quantization-based mechanism...
May 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541204/locator-checker-scaler-object-tracking-using-spatially-ordered-and-weighted-patch-descriptor
#7
Han-Ul Kim, Chang-Su Kim
In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541203/improved-recursive-geodesic-distance-computation-for-edge-preserving-filter
#8
Mikhail Mozerov, Joost van de Weijer
All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks which lead to known artifacts. In this paper a maximum influence propagation method is proposed to approximate the 2D extension for the geodesic distance based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541202/linear-support-tensor-machine-with-lsk-channels-pedestrian-detection-in-thermal-infrared-images
#9
Sujoy Kumar Biswas, Peyman Milanfar
Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image. Here we propose a mid-level attribute in the form of the multidimensional template, or tensor, using Local Steering Kernel (LSK) as low-level descriptors for detecting pedestrians in far infrared images. LSK is specifically designed to deal with intrinsic image noise and pixel level uncertainty by capturing local image geometry succinctly instead of collecting local orientation statistics (e...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541201/palmprint-recognition-based-on-complete-direction-representation
#10
Wei Jia, Bob Zhang, Jingting Lu, Yihai Zhu, Yang Zhao, Wangmeng Zuo, Haibin Ling
Direction information serves as one of the most important features for palmprint recognition. In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale. Hence, they did not fully utilized all potentials of DR. In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28541200/label-information-guided-graph-construction-for-semi-supervised-learning
#11
Liansheng Zhuang, Zihan Zhou, Shenghua Gao, Jingwen Yin, Zhouchen Lin, Yi Ma
In the literature, most existing graph-based semi- supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR)...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28534775/learning-rotation-invariant-local-binary-descriptor
#12
Yueqi Duan, Jiwen Lu, Jianjiang Feng, Jie Zhou
In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors such as LBP and its variants which require strong prior knowledge, local binary feature learning methods are more efficient and dataadaptive. Unlike existing learning-based local binary descriptors such as compact binary face descriptor (CBFD) and simultaneous local binary feature learning and encoding (SLBFLE) which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain rotation-invariant local binary descriptors...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28534774/a-fast-ellipse-detector-using-projective-invariant-pruning
#13
Qi Jia, Xin Fan, Zhongxuan Luo, Lianbo Song, Tie Qiu
Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28534773/sparsity-based-color-image-super-resolution-via-exploiting-cross-channel-constraints
#14
Hojjat Mousavi, Vishal Monga
Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coefficients of this representation to generate the highresolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for super resolution focus on the luminance channel information and do not capture interactions between color channels...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28534772/discriminative-transformation-for-multi-dimensional-temporal-sequences
#15
Bing Su, Xiaoqing Ding, Changsong Liu, Hao Wang, Ying Wu
Feature space transformation (FST) techniques have been widely studied for dimensionality reduction in vector-based feature space. However, these techniques are inapplicable to sequence data because the features in the same sequence are not independent. In this paper, we propose a method called max-min inter-sequence distance analysis (MMSDA) to transform features in sequences into a low-dimensional subspace such that different sequence classes are holistically separated. To utilize the temporal dependencies, MMSDA first aligns features in sequences from the same class to an adapted number of temporal states and then constructs the sequence class separability based on the statistics of these ordered states...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28534771/dynamic-graph-cuts-in-parallel
#16
Miao Yu, Shuhan Shen, Zhanyi Hu
This work aims at bridging the two important trends in efficient graph cuts in the literature, the one is to decompose a graph into several smaller subgraphs to take the advantage of parallel computation, the other is to reuse the solution of the max-flow problem on a residual graph to boost the efficiency on another similar graph. Our proposed parallel dynamic graph cuts algorithm takes the advantages of both, and is extremely efficient for certain dynamically changing MRF models in computer vision. The performance of our proposed algorithm is validated on two typical dynamic graph cuts problems: the foregroundbackground segmentation in video where similar graph cuts problems need to be solved in sequential and GrabCut where graph cuts is used iteratively...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28534770/boscc-bag-of-spatial-context-correlations-for-spatially-enhanced-3d-shape-representation
#17
Zhizhong Han, Zhenbao Liu, Chi-Man Vong, Yu-Shen Liu, Shuhui Bu, Junwei Han, Philip Chen
Highly discriminative 3D shape representations can be formed by encoding the spatial relationship among virtual words into the Bag of Words (BoW) method. To achieve this challenging task, several unresolved issues in the encoding procedure must be overcome for 3D shapes, including i) arbitrary mesh resolution, ii) irregular vertex topology, iii) orientation ambiguity on the 3D surface and iv) invariance to rigid and non-rigid shape transformations. In this work, a novel spatially enhanced 3D shape representation called Bag of Spatial Context Correlations (BoSCC) is proposed to address all these issues...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28500003/hybrid-laplace-distribution-based-low-complexity-rate-distortion-optimized-quantization
#18
Jing Cui, Shanshe Wang, Shiqi Wang, Xinfeng Zhang, Siwei Ma, Wen Gao
Rate Distortion Optimized Quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the High Efficiency Video Coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization including determination of optimal quantized level among available candidates for each transformed coefficient and determination of best quantized coefficients for transform units with the minimum total cost, to softly optimize the quantized coefficients...
May 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28500002/cross-label-suppression-a-discriminative-and-fast-dictionary-learning-with-group-regularization
#19
Xiudong Wang, Yuantao Gu
This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose crosslabel suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar...
May 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28500001/exemplar-based-image-and-video-stylization-using-fully-convolutional-semantic-features
#20
Feida Zhu, Zhicheng Yan, Jiajun Bu, Yizhou Yu
Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent...
May 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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