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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/29346101/stacked-denoising-tensor-auto-encoder-for-action-recognition-with-spatiotemporal-corruptions
#1
Chengcheng Jia, Ming Shao, Sheng Li, Handong Zhao, Yun Fu
Spatially or temporally corrupted action videos are impractical for recognition via vision or learning models. It usually happens when streaming data are captured from unintended moving cameras, which bring occlusion or camera vibration and accordingly result in arbitrary loss of spatiotemporal information. In reality, it is intractable to deal with both spatial and temporal corruptions at the same time. In this paper, we propose a coupled stacked denoising tensor auto-encoder (CSDTAE) model, which approaches this corruption problem in a divide-and-conquer fashion by jointing both the spatial and temporal schemes together...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346100/improving-color-constancy-in-an-ambient-light-environment-using-the-phong-reflection-model
#2
Sung-Min Woo, Sang-Ho Lee, Jun-Sang Yoo, Jong-Ok Kim
We present a physics-based illumination estimation approach explicitly designed to handle natural images under ambient light. Existing physics-based color constancy methods are theoretically perfect but do not handle real-world images well because the majority of these methods assume a single illuminant. Therefore, specular pixels selected using existing methods produce estimated dichromatic lines that are thick or curvilinear in the presence of ambient light, thus generating significant errors. Based on the Phong reflection model, we show that a group of specular pixels on a uniformly colored object, although they are subject to intensity thresholding, produce a unique dichromatic line length depending on the geometry of each image path...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346099/cell-membrane-tracking-in-living-brain-tissue-using-differential-interference-contrast-microscopy
#3
John Lee, Ilya Kolb, Craig R Forest, Christopher J Rozell
Differential interference contrast (DIC) microscopy is widely used for observing unstained biological samples that are otherwise optically transparent. Combining this optical technique with machine vision could enable the automation of many life science experiments; however, identifying relevant features under DIC is challenging. In particular, precise tracking of cell boundaries in a thick ( ) slice of tissue has not previously been accomplished. We present a novel deconvolution algorithm that achieves the state-of-the-art performance at identifying and tracking these membrane locations...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346098/region-based-prediction-for-image-compression-in-the-cloud
#4
Jean Begaint, Dominique Thoreau, Philippe Guillotel, Christine Guillemot
Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346097/change-detection-in-heterogenous-remote-sensing-images-via-homogeneous-pixel-transformation
#5
Zhunga Liu, Gang Li, Gregoire Mercier, You He, Quan Pan
The change detection in heterogeneous remote sensing images remains an important and open problem for damage assessment. We propose a new change detection method for heterogeneous images (i.e., SAR and optical images) based on homogeneous pixel transformation (HPT). HPT transfers one image from its original feature space (e.g., gray space) to another space (e.g., spectral space) in pixel-level to make the pre-event and post-event images represented in a common space for the convenience of change detection. HPT consists of two operations, i...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346096/iterative-graph-seeking-for-object-tracking
#6
Dawei Du, Longyin Wen, Honggang Qi, Qingming Huang, Qi Tian, Siwei Lyu
To effectively solve the challenges in object tracking, such as large deformation and severe occlusion, many existing methods use graph-based models to capture target part relations, and adopt a sequential scheme of target part selection, part matching, and state estimation. However, such methods have two major drawbacks: 1) inaccurate part selection leads to performance deterioration of part matching and state estimation and 2) there are insufficient effective global constraints for local part selection and matching...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346095/feature-map-quality-score-estimation-through-regression
#7
Ibrahim M H Rahman, Christopher Hollitt, Mengjie Zhang
Understanding the visual quality of a feature map plays a significant role in many active vision applications. Previous works mostly rely on object-level features, such as compactness, to estimate the quality score of a feature map. However, the compactness is leveraged on feature maps produced by salient object detection techniques where the maps tend to be compact. As a result, the compactness feature fails when the feature maps are blurry (e.g., fixation maps). In this paper, we regard the process of estimating the quality score of feature maps, specifically fixation maps, as a regression problem...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346094/reweighted-low-rank-matrix-analysis-with-structural-smoothness-for-image-denoising
#8
Hengyou Wang, Yigang Cen, Zhiquan He, Zhihai He, Ruizhen Zhao, Fengzhen Zhang
In this paper, we develop a new low-rank matrix recovery algorithm for image denoising. We incorporate the total variation (TV) norm and the pixel range constraint into the existing reweighted low-rank matrix analysis to achieve structural smoothness and to significantly improve quality in the recovered image. Our proposed mathematical formulation of the low-rank matrix recovery problem combines the nuclear norm, TV norm, and norm, thereby allowing us to exploit the low-rank property of natural images, enhance the structural smoothness, and detect and remove large sparse noise...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346093/multipolarization-through-wall-radar-imaging-using-low-rank-and-jointly-sparse-representations
#9
Van Ha Tang, Abdesselam Bouzerdoum, Son Lam Phung
Compressed sensing techniques have been applied to through-the-wall radar imaging (TWRI) and multipolarization TWRI for fast data acquisition and enhanced target localization. The studies so far in this area have either assumed effective wall clutter removal prior to image formation or performed signal estimation, wall clutter mitigation, and image formation independently. This paper proposes a low-rank and sparse imaging model for jointly addressing the problem of wall clutter mitigation and image formation in multichannel TWRI...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346092/hierarchical-and-spatio-temporal-sparse-representation-for-human-action-recognition
#10
Yi Tian, Yu Kong, Qiuqi Ruan, Gaoyun An, Yun Fu
In this paper, we present a novel two-layer video representation for human action recognition employing hierarchical group sparse encoding technique and spatio-temporal structure. In the first layer, a new sparse encoding method named locally consistent group sparse coding (LCGSC) is proposed to make full use of motion and appearance information of local features. LCGSC method not only encodes global layouts of features within the same video-level groups, but also captures local correlations between them, which obtains expressive sparse representations of video sequences...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346091/plenoptic-image-motion-deblurring
#11
Paramanand Chandramouli, Meiguang Jin, Daniele Perrone, Paolo Favaro
We propose a method to remove motion blur in a single light field captured with a moving plenoptic camera. Since motion is unknown, we resort to a blind deconvolution formulation, where one aims to identify both the blur point spread function and the latent sharp image. Even in the absence of motion, light field images captured by a plenoptic camera are affected by a non-trivial combination of both aliasing and defocus, which depends on the 3D geometry of the scene. Therefore, motion deblurring algorithms designed for standard cameras are not directly applicable...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29346090/fast-2d-complex-gabor-filter-with-kernel-decomposition
#12
Jaeyoon Kim, Suhyuk Um, Dongbo Min
2D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2D complex Gabor filter bank consisting of filtering outputs at multiple orientations and frequencies. Although several approaches for fast Gabor filtering have been proposed, they focus primarily on reducing the runtime for performing filtering once at specific orientation and frequency. To obtain the Gabor filter bank, the existing methods are repeatedly applied with respect to multiple orientations and frequencies...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324421/analysis-on-the-effect-of-sensor-views-in-image-reconstruction-produced-by-optical-tomography-system-using-charge-coupled-device
#13
Juliza Jamaludin, Ruzairi Abdul Rahim, Mohd Hafiz Fazul Rahiman, Jemmy Mohd Rohani
Optical tomography (OPT) is a method to capture a cross-sectional image based on the data obtained by sensors, distributed around the periphery of the analyzed system. This system is based on the measurement of the final light attenuation or absorption of radiation after crossing the measured objects. The number of sensor views will affect the results of image reconstruction, where the high number of sensor views per projection will give a high image quality. This research presents an application of charge-coupled device linear sensor and laser diode in an OPT system...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324420/evaluation-of-hierarchical-watersheds
#14
Benjamin Perret, Jean Cousty, Silvio Jamil F Guimaraes, Deise S Maia
This paper aims to understand the practical features of hierarchies of morphological segmentations, namely the quasi-flat zones hierarchy and watershed hierarchies, and to evaluate their potential in the context of natural image analysis. We propose a novel evaluation framework for the hierarchies of partitions designed to capture various aspects of those representations: precision of their regions and contours, possibility to extract high quality horizontal cuts and optimal non-horizontal cuts for image segmentation, and the ease of finding a set of regions representing a semantic object...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324419/visual-saliency-detection-using-spatiotemporal-decomposition
#15
Saumik Bhattacharya, K Subramanian Venkatesh, Sumana Gupta
We propose a novel technique for detection of visual saliency in dynamic video based on video decomposition. The decomposition obtains the sparse features in a particular orientation by exploiting the spatiotemporal discontinuities present in a video cube. A weighted sum of the sparse features along three orthogonal directions determines the salient regions in the video cubes. The weights computed using the frame correlation along three directions are based on the characteristic of human visual system that identifies the sparsest feature as the most salient feature in a video...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324418/niqsv-a-no-reference-synthesized-view-quality-assessment-metric
#16
Shishun Tian, Lu Zhang, Luce Morin, Olivier Deforges
Benefiting from multi-view video plus depth and depth-image-based-rendering technologies, only limited views of a real 3-D scene need to be captured, compressed, and transmitted. However, the quality assessment of synthesized views is very challenging, since some new types of distortions, which are inherently different from the texture coding errors, are inevitably produced by view synthesis and depth map compression, and the corresponding original views (reference views) are usually not available. Thus the full-reference quality metrics cannot be used for synthesized views...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324417/robust-object-co-segmentation-using-background-prior
#17
Junwei Han, Rong Quan, Dingwen Zhang, Feiping Nie
Given a set of images that contain objects from a common category, object co-segmentation aims at automatically discovering and segmenting such common objects from each image. During the past few years, object co-segmentation has received great attention in the computer vision community. However, the existing approaches are usually designed with misleading assumptions, unscalable priors, or subjective computational models, which do not have sufficient robustness for dealing with complex and unconstrained real-world image contents...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324416/unsupervised-deep-hashing-with-pseudo-labels-for-scalable-image-retrieval
#18
Haofeng Zhang, Li Liu, Yang Long, Ling Shao
In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324415/convolutional-sparse-coding-for-rgb-nir-imaging
#19
Xuemei Hu, Felix Heide, Qionghai Dai, Gordon Wetzstein
Emerging sensor designs increasingly rely on novel color filter arrays (CFAs) to sample the incident spectrum in unconventional ways. In particular, capturing a near-infrared (NIR) channel along with conventional RGB color is an exciting new imaging modality. RGB+NIR sensing has broad applications in computational photography, such as low-light denoising, it has applications in computer vision, such as facial recognition and tracking, and it paves the way toward low-cost single-sensor RGB and depth imaging using structured illumination...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29324414/no-reference-quality-assessment-for-screen-content-images-with-both-local-and-global-feature-representation
#20
Yuming Fang, Jiebin Yan, Leida Li, Jinjian Wu, Weisi Lin
In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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