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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/28613168/convolution-neural-networks-with-two-pathways-for-image-style-recognition
#1
Tiancheng Sun, Yulong Wang, Jian Yang, Xiaolin Hu
Automatic recognition of an image's style is important for many applications including artwork analysis, photo organization and image retrieval. Traditional convolution neural network (CNN) approach uses only object features for image style recognition. This approach may not be optimal because the same object in two images may have different styles. We propose a CNN architecture with two pathways extracting object features and texture features, respectively. The object pathway represents the standard CNN architecture and the texture pathway intermixes the object pathway by outputting the gram matrices of intermediate features in the object pathway...
June 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613176/derivative-kernels-numerics-and-applications
#2
Mahdi S Hosseini, Konstantinos N Plataniotis
A generalized framework for numerical differentiation (ND) is proposed for constructing a finite impulse response (FIR) filter in closed form. The framework regulates the frequency response of ND filters for arbitrary derivative-order and cutoff frequency selected parameters relying on interpolating power polynomials and maximally flat design techniques. Compared to the state-of-the-art solutions such as Gaussian kernels, the proposed ND filter is sharply localized in the Fourier domain with ripple-free artifacts...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613175/two-dimensional-feature-selection-by-sparse-matrix-regression
#3
Chenping Hou, Yuanyuan Jiao, Feiping Nie, Tingjin Luo, Zhi-Hua Zhou
For many image processing and computer vision problems, data points are in matrix form. Traditional methods often convert a matrix to a vector and then use vector based approaches. They will ignore the location of matrix elements and the converted vector often has high dimensionality. How to select features for two dimensional matrix data directly is still an uninvestigated important issue. In this paper, we propose an algorithm named as Sparse Matrix Regression (SMR) for direct feature selection on matrix data...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613174/mulog-or-how-to-apply-gaussian-denoisers-to-multi-channel-sar-speckle-reduction
#4
Charles-Alban Deledalle, Loic Denis, Sonia Tabti, Florence Tupin
Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613173/large-scale-crowdsourced-study-for-tone-mapped-hdr-pictures
#5
Debarati Kundu, Deepti Ghadiyaram, Alan Bovik, Brian Evans
Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 bits/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 bits/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multi-exposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613172/learning-the-image-processing-pipeline
#6
Haomiao Jiang, Qiyuan Tian, Joyce Farrell, Brian Wandell
Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image processing pipeline that transforms the sensor data into a form that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613171/simultaneous-feature-and-dictionary-learning-for-image-set-based-face-recognition
#7
Jiwen Lu, Gang Wang, Jie Zhou
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set based face recognition, where each training and testing example contains a set of face images which were captured from different variations of pose, illumination, expression, resolution and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613170/direct-pattern-control-halftoning-of-neugebauer-primaries
#8
Peter Morovic, Jan Morovic, Jay Gondek, Robert Ulichney
Halftoning is a key stage of any printing image processing pipeline. With colorant-channel approaches, a key challenge for matrix-based halftoning is the co-optimization of the matrices used for individual colorants, which becomes increasingly complex and over-constrained as the number of colorants increases. Both choices of screen angles (in clustered-dot cases) or structures, and control over how individual matrices relate to each other and result in over- versus side-by-side printing of the colorants, impose challenging restrictions...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613169/temporal-dependent-rate-distortion-optimization-for-low-delay-hierarchical-video-coding
#9
Yanbo Gao, Ce Zhu, Shuai Li, Tianwu Yang
Low-Delay hierarchical coding structure (LD-HCS), as one of the most important components in the latest High Efficiency Video Coding (HEVC) standard, greatly improves coding performance. It groups consecutive P/B frames into different layers and encodes them with different quantization parameters (QP) and reference mechanisms in such a way that temporal dependency among frames can be exploited. However, due to varying characteristics of video contents, temporal dependency among coding units differs significantly from each other in the same or different layer, while a fixed LD-HCS scheme cannot take full advantage of the dependency, leading to a substantial loss in coding performance...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28600248/unsupervised-sequential-outlier-detection-with-deep-architectures
#10
Weining Lu, Yu Cheng, Cao Xiao, Shiyu Chang, Shuai Huang, Bin Liang, Thomas Huang
Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis, video surveillance. It also gains longstanding attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction...
June 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28600247/multimodal-similarity-gaussian-process-latent-variable-model
#11
Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian
Data from real applications involve multiple modalities representing content with the same semantics from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized modality specific mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy in multimodal data. In this paper, we build our work based on Gaussian process latent variable model (GPLVM) to learn the non-parametric mapping functions and transform heterogeneous modalities into a shared latent space...
June 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28600246/single-and-multiple-illuminant-estimation-using-convolutional-neural-networks
#12
Simone Bianco, Claudio Cusano, Raimondo Schettini
In this paper we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a Convolutional Neural Network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard datasets with single and multiple illuminants, proves the effectiveness of our method...
June 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28600245/ocular-recognition-for-blinking-eyes
#13
Peizhong Liu, Jing-Ming Guo, Szu-Han Tseng, KokSheik Wong, Jiann-Der Lee, Chen-Chieh Yao, Daxin Zhu
Ocular recognition is expected to provide a higher flexibility in handling practical applications as oppose to the iris recognition, which only works for the ideal open-eye case. However, the accuracy of the recent efforts is still far from satisfactory at uncontrollable conditions, such as eye blinking which implies any poses of eyes. To address these issues, the skin texture, eyelids, and additional geometrical features are employed. In addition, to achieve higher accuracy, sequential forward floating selection (SFFS) is utilized to select the best feature combinations...
June 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28600244/illumination-decomposition-for-photograph-with-multiple-light-sources
#14
Ling Zhang, Qingan Yan, Zheng Liu, Hua Zou, Chunxia Xiao
Illumination decomposition for a single photograph is an important and challenging problem in image editing operation. In this paper, we present a novel coarse-to-fine strategy to perform the illumination decomposition for photograph with multiple light sources. We first reconstruct the lighting environment of the image using the estimated geometry structure of the scene. With the position of lights, we detect the shadow regions as well as the highlights in the projected image for each light. Then using the illumination cues from shadows, we estimate the coarse illumination decomposed image emitted by each light source...
June 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28600243/robust-non-local-tv-l1-optical-flow-estimation-with-occlusion-detection
#15
Congxuan Zhang, Zhen Chen, Mingrun Wang, Ming Li, Shaofeng Jiang
In this paper, we propose a robust non-local TV-L1 optical flow method with occlusion detection to address the problem of weak robustness of optical flow estimation with motion occlusion. Firstly, a TV-L1 form for flow estimation is defined using a combination of the brightness constancy and gradient constancy assumptions in the data term and by varying the weight under the Charbonnier function in the smoothing term. Secondly, to handle the potential risk of the outlier in the flow field, a general non-local term is added in the TV-L1 optical flow model to engender the typical non-local TV-L1 form...
June 5, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28574354/superpatchmatch-an-algorithm-for-robust-correspondences-using-superpixel-patches
#16
Remi Giraud, Vinh-Thong Ta, Aurelie Bugeau, Pierrik Coupe, Nicolas Papadakis
Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non stable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced...
May 29, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28574359/diversity-aware-multi-video-summarization
#17
Rameswar Panda, Niluthpol Chowdhury Mithun, Amit Roy-Chowdhury
Most video summarization approaches have focused on extracting a summary from a single video; we propose an unsupervised framework for summarizing a collection of videos. We observe that each video in the collection may contain some information that other videos do not have, and thus exploring the underlying complementarity could be beneficial in creating a diverse informative summary. We develop a novel diversity-aware sparse optimization method for multi-video summarization by exploring the complementarity within the videos...
May 26, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28574358/beyond-group-multiple-person-tracking-via-minimal-topology-energy-variation
#18
Shan Gao, Qixiang Ye, Junliang Xing, Arjan Kuijper, Zhenjun Han, Jianbin Jiao, Xiangyang Ji
Tracking multiple persons is a challenging task when persons move in groups and occlude each other. Existing groupbased methods have extensively investigated how to make group division more accurate in a tracking-by-detection framework; however, few of them quantify the group dynamics from the perspective of targets' spatial topology or consider the group in a dynamic view. Inspired by the sociological properties of pedestrians, we propose a novel socio-topology model with a topology-energy function to factor the group dynamics of moving persons and groups...
May 26, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28574357/single-image-rain-streak-separation-using-layer-priors
#19
Yu Li, Robby T Tan, Xiaojie Guo, Jiangbo Lu, Michael S Brown
Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision and image analysis systems. Rain streak removal algorithms try to recover a rain streak free background scene. In this paper, we address the problem of rain streak removal from a single image by formulating it as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either sparse dictionary learning methods or impose a low rank structure on the appearance of the rain streaks...
May 26, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28574356/weakly-supervised-part-proposal-segmentation-from-multiple-images
#20
Fanman Meng, Hongliang Li, Qingbo Wu, Bing Luo, King Ngi Ngan
Weakly supervised local part segmentation is challenging due to the difficulty of modeling multiple local parts from image level prior. In this paper, we propose a new weakly supervised local part proposal segmentation method based on the observation that local parts will keep fixed along the object pose variations. Hence, the local part can be segmented by capturing object pose variations. Based on such observation, a new local part proposal segmentation model is proposed. Three aspects, such as shape similarity based cosegmentation, shape matching based part detection and segmentation, and graph matching based part assignment are considered...
May 26, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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