IEEE Transactions on Image Processing

Issue 11 • Nov. 2016

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Displaying Results 1 - 25 of 51
  • Front cover

    Publication Year: 2016, Page(s): C1
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  • IEEE Transactions on Image Processing publication information

    Publication Year: 2016, Page(s): C2
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  • Table of contents

    Publication Year: 2016, Page(s):4993 - 4994
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  • Table of contents

    Publication Year: 2016, Page(s):4995 - 4997
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  • [Blank page]

    Publication Year: 2016, Page(s): B4998
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  • Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation

    Publication Year: 2016, Page(s):4999 - 5011
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4797 KB) | HTML iconHTML

    In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a sim... View full abstract»

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  • Visual Saliency Detection Based on Multiscale Deep CNN Features

    Publication Year: 2016, Page(s):5012 - 5024
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4465 KB) | HTML iconHTML

    Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural ne... View full abstract»

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  • Correspondence Driven Saliency Transfer

    Publication Year: 2016, Page(s):5025 - 5034
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1309 KB) | HTML iconHTML

    In this paper, we show that large annotated data sets have great potential to provide strong priors for saliency estimation rather than merely serving for benchmark evaluations. To this end, we present a novel image saliency detection method called saliency transfer. Given an input image, we first retrieve a support set of best matches from the large database of saliency annotated images. Then, we... View full abstract»

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  • Foreground Detection With Simultaneous Dictionary Learning and Historical Pixel Maintenance

    Publication Year: 2016, Page(s):5035 - 5049
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3907 KB) | HTML iconHTML

    Foreground detection is fundamental in surveillance video analysis and meaningful toward object tracking and higher level tasks, such as anomaly detection and activity analysis. Nevertheless, existing methods are still limited in accurately detecting the foreground due to the complex scene settings. To robustly handle the diverse background variations and foreground challenges, this paper proposes... View full abstract»

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  • Perceptually Motivated Image Features Using Contours

    Publication Year: 2016, Page(s):5050 - 5062
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (10802 KB) | HTML iconHTML

    Dong et al. examined the ability of 51 computational feature sets to estimate human perceptual texture similarity; however, none performed well for this task. While it is well-known that the human visual system is extremely adept at exploiting longer-range aperiodic (and periodic) “contour” characteristics in images, none of the investigated feature sets exploit higher order statisti... View full abstract»

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  • Similarity of Scenic Bilevel Images

    Publication Year: 2016, Page(s):5063 - 5076
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4069 KB) | HTML iconHTML

    This paper presents a study of bilevel image similarity, including new objective metrics intended to quantify similarity consistent with human perception, and a subjective experiment to obtain ground truth for judging the performance of the objective similarity metrics. The focus is on scenic bilevel images, which are complex, natural or hand-drawn images, such as landscapes or portraits. The grou... View full abstract»

    Open Access
  • Compressive Estimation and Imaging Based on Autoregressive Models

    Publication Year: 2016, Page(s):5077 - 5087
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2013 KB) | HTML iconHTML

    Compressed sensing (CS) is a fast and efficient way to obtain compact signal representations. Oftentimes, one wishes to extract some information from the available compressed signal. Since CS signal recovery is typically expensive from a computational point of view, it is inconvenient to first recover the signal and then extract the information. A much more effective approach consists in estimatin... View full abstract»

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  • CU Partition Mode Decision for HEVC Hardwired Intra Encoder Using Convolution Neural Network

    Publication Year: 2016, Page(s):5088 - 5103
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3513 KB) | HTML iconHTML

    The intensive computation of High Efficiency Video Coding (HEVC) engenders challenges for the hardwired encoder in terms of the hardware overhead and the power dissipation. On the other hand, the constrains in hardwired encoder design seriously degrade the efficiency of software oriented fast coding unit (CU) partition mode decision algorithms. A fast algorithm is attributed as VLSI friendly, when... View full abstract»

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  • Light Field Multi-View Video Coding With Two-Directional Parallel Inter-View Prediction

    Publication Year: 2016, Page(s):5104 - 5117
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4764 KB) | HTML iconHTML

    Light field (LF) technology has been popularly adopted by a wide range of conventional industries. However, one problem when dealing with LFs is the sheer size of data volume. There have been many multi-view video coding (MVC)-based LF video coding methods reported in the literature, aiming at finding the best prediction structure for LF video coding. It is clear that the number of possible predic... View full abstract»

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  • Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters

    Publication Year: 2016, Page(s):5118 - 5130
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4799 KB) | HTML iconHTML

    Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA, reduced-reference (RR) IQA, and no-reference (NR) IQA according to the amount of information required from the original image. Although NR-IQA and RR-IQA are widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the fact that in many a... View full abstract»

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  • Single-Shot Dense 3D Reconstruction Using Self-Equalizing De Bruijn Sequence

    Publication Year: 2016, Page(s):5131 - 5144
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (11345 KB) | HTML iconHTML

    Single-shot dense 3D reconstruction using colored structured light is a difficult problem due to the undesired effects of ambient lighting, object albedo, non-equal channel gains, and channel cross-talk. We propose a novel single-shot dense 3D reconstruction using colored structured light. Our method combines the self-equalizing De Bruijn sequence, scale-space analysis, and bandpass complex Hilber... View full abstract»

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  • Ranking Highlights in Personal Videos by Analyzing Edited Videos

    Publication Year: 2016, Page(s):5145 - 5157
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6532 KB) | HTML iconHTML

    We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a targeted domain such as “surfing,” our system mines the YouTube database to find pairs of raw and their corresponding edited ... View full abstract»

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  • Quality Scalability Aware Watermarking for Visual Content

    Publication Year: 2016, Page(s):5158 - 5172
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (11207 KB) | HTML iconHTML

    Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not... View full abstract»

    Open Access
  • Universal Demosaicking of Color Filter Arrays

    Publication Year: 2016, Page(s):5173 - 5186
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4672 KB) | HTML iconHTML

    A large number of color filter arrays (CFAs), periodic or aperiodic, have been proposed. To reconstruct images from all different CFAs and compare their imaging quality, a universal demosaicking method is needed. This paper proposes a new universal demosaicking method based on inter-pixel chrominance capture and optimal demosaicking transformation. It skips the commonly used step to estimate the l... View full abstract»

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  • DehazeNet: An End-to-End System for Single Image Haze Removal

    Publication Year: 2016, Page(s):5187 - 5198
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5832 KB) | HTML iconHTML

    Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and ou... View full abstract»

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  • The Bitonic Filter: Linear Filtering in an Edge-Preserving Morphological Framework

    Publication Year: 2016, Page(s):5199 - 5211
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3289 KB) | HTML iconHTML

    A new filter is presented which has better edge and detail preserving properties than a median, noise reduction capability similar to a Gaussian, and is applicable to many signal and noise types. It is built on a definition of signal as bitonic, i.e., containing only one local maxima or minima within the filter range. This definition is based on data ranking rather than value; hence, the bitonic f... View full abstract»

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  • Image Inpainting Through Metric Labeling via Guided Patch Mixing

    Publication Year: 2016, Page(s):5212 - 5226
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5677 KB) | HTML iconHTML

    In this paper, we present a novel formulation of exemplar-based image inpainting as a metric labeling problem, and solve it through the simulated annealing algorithm. Due to their greedy nature, exemplar-based methods sometimes produce inpainted images, which are visually inconsistent. These methods are highly dependent upon the initialization. To solve these problems, we generate five images with... View full abstract»

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  • Structure Selective Depth Superresolution for RGB-D Cameras

    Publication Year: 2016, Page(s):5227 - 5238
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3626 KB) | HTML iconHTML Multimedia Media

    This paper describes a method for high-quality depth superresolution. The standard formulations of image-guided depth upsampling, using simple joint filtering or quadratic optimization, lead to texture copying and depth bleeding artifacts. These artifacts are caused by inherent discrepancy of structures in data from different sensors. Although there exists some correlation between depth and intens... View full abstract»

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  • A Robust Approach for the Background Subtraction Based on Multi-Layered Self-Organizing Maps

    Publication Year: 2016, Page(s):5239 - 5251
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2152 KB) | HTML iconHTML

    Motion detection in video streams is a challenging task for several computer vision applications. Indeed, segmentation of moving and static elements in the scene allows to increase the efficiency of several challenging tasks, such as human-computer interface, robot visions, and intelligent surveillance systems. In this paper, we approach motion detection through a multi-layered artificial neural n... View full abstract»

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  • A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization

    Publication Year: 2016, Page(s):5252 - 5265
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3382 KB) | HTML iconHTML

    It is of essential importance for historians to know the date and place of origin of the documents they study. It would be a huge advancement for historical scholars if it would be possible to automatically estimate the geographical and temporal provenance of a handwritten document by inferring them from the handwriting style of such a document. We propose a multiple-label guided clustering algori... View full abstract»

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Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

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Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003