# IEEE Transactions on Image Processing

## Filter Results

Displaying Results 1 - 25 of 42
• ### [Front cover]

Publication Year: 2013, Page(s): C1
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• ### IEEE Transactions on Image Processing publication information

Publication Year: 2013, Page(s): C2
| PDF (132 KB)

Publication Year: 2013, Page(s):2923 - 2925
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• ### [Blank page]

Publication Year: 2013, Page(s): 2926
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Publication Year: 2013, Page(s):2927 - 2930
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• ### Novel True-Motion Estimation Algorithm and Its Application to Motion-Compensated Temporal Frame Interpolation

Publication Year: 2013, Page(s):2931 - 2945
Cited by:  Papers (24)
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In this paper, a new low-complexity true-motion estimation (TME) algorithm is proposed for video processing applications, such as motion-compensated temporal frame interpolation (MCTFI) or motion-compensated frame rate up-conversion (MCFRUC). Regular motion estimation, which is often used in video coding, aims to find the motion vectors (MVs) to reduce the temporal redundancy, whereas TME aims to ... View full abstract»

• ### Motion Analysis Using 3D High-Resolution Frequency Analysis

Publication Year: 2013, Page(s):2946 - 2959
Cited by:  Papers (6)
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The spatiotemporal spectra of a video that contains a moving object form a plane in the 3D frequency domain. This plane, which is described as the theoretical motion plane, reflects the velocity of the moving objects, which is calculated from the slope. However, if the resolution of the frequency analysis method is not high enough to obtain actual spectra from the object signal, the spatiotemporal... View full abstract»

• ### Segment Adaptive Gradient Angle Interpolation

Publication Year: 2013, Page(s):2960 - 2969
Cited by:  Papers (10)
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We introduce a new edge-directed interpolator based on locally defined, straight line approximations of image isophotes. Spatial derivatives of image intensity are used to describe the principal behavior of pixel-intersecting isophotes in terms of their slopes. The slopes are determined by inverting a tridiagonal matrix and are forced to vary linearly from pixel-to-pixel within segments. Image res... View full abstract»

• ### Fast Computation of Rotation-Invariant Image Features by an Approximate Radial Gradient Transform

Publication Year: 2013, Page(s):2970 - 2982
Cited by:  Papers (21)
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We present the radial gradient transform (RGT) and a fast approximation, the approximate RGT (ARGT). We analyze the effects of the approximation on gradient quantization and histogramming. The ARGT is incorporated into the rotation-invariant fast feature (RIFF) algorithm. We demonstrate that, using the ARGT, RIFF extracts features 16&times; faster than SURF while achieving a similar performanc... View full abstract»

• ### Image Completion by Diffusion Maps and Spectral Relaxation

Publication Year: 2013, Page(s):2983 - 2994
Cited by:  Papers (11)
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We present a framework for image inpainting that utilizes the diffusion framework approach to spectral dimensionality reduction. We show that on formulating the inpainting problem in the embedding domain, the domain to be inpainted is smoother in general, particularly for the textured images. Thus, the textured images can be inpainted through simple exemplar-based and variational methods. We discu... View full abstract»

• ### A Continuous Method for Reducing Interpolation Artifacts in Mutual Information-Based Rigid Image Registration

Publication Year: 2013, Page(s):2995 - 3007
Cited by:  Papers (1)
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We propose an approach for computing mutual information in rigid multimodality image registration. Images to be registered are modeled as functions defined on a continuous image domain. Analytic forms of the probability density functions for the images and the joint probability density function are first defined in 1D. We describe how the entropies of the images, the joint entropy, and mutual info... View full abstract»

• ### Image Inpainting on the Basis of Spectral Structure From 2-D Nonharmonic Analysis

Publication Year: 2013, Page(s):3008 - 3017
Cited by:  Papers (5)
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The restoration of images by digital inpainting is an active field of research and such algorithms are, in fact, now widely used. Conventional methods generally apply textures that are most similar to the areas around the missing region or use a large image database. However, this produces discontinuous textures and thus unsatisfactory results. Here, we propose a new technique to overcome this lim... View full abstract»

• ### Linear Discriminant Analysis Based on L1-Norm Maximization

Publication Year: 2013, Page(s):3018 - 3027
Cited by:  Papers (41)
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Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection v... View full abstract»

• ### Visual Tracking With Spatio-Temporal Dempster–Shafer Information Fusion

Publication Year: 2013, Page(s):3028 - 3040
Cited by:  Papers (15)
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A key problem in visual tracking is how to effectively combine spatio-temporal visual information from throughout a video to accurately estimate the state of an object. We address this problem by incorporating Dempster-Shafer (DS) information fusion into the tracking approach. To implement this fusion task, the entire image sequence is partitioned into spatially and temporally adjacent subsequence... View full abstract»

• ### Dimensionality Reduction for Registration of High-Dimensional Data Sets

Publication Year: 2013, Page(s):3041 - 3049
Cited by:  Papers (4)
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Registration of two high-dimensional data sets often involves dimensionality reduction to yield a single-band image from each data set followed by pairwise image registration. We develop a new application-specific algorithm for dimensionality reduction of high-dimensional data sets such that the weighted harmonic mean of Cramér-Rao lower bounds for the estimation of the transformation para... View full abstract»

• ### Multiple-Kernel, Multiple-Instance Similarity Features for Efficient Visual Object Detection

Publication Year: 2013, Page(s):3050 - 3061
Cited by:  Papers (8)
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We propose to use the similarity between the sample instance and a number of exemplars as features in visual object detection. Concepts from multiple-kernel learning and multiple-instance learning are incorporated into our scheme at the feature level by properly calculating the similarity. The similarity between two instances can be measured by various metrics and by using the information from var... View full abstract»

• ### Asymmetric Correlation: A Noise Robust Similarity Measure for Template Matching

Publication Year: 2013, Page(s):3062 - 3073
Cited by:  Papers (14)
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We present an efficient and noise robust template matching method based on asymmetric correlation (ASC). The ASC similarity function is invariant to affine illumination changes and robust to extreme noise. It correlates the given non-normalized template with a normalized version of each image window in the frequency domain. We show that this asymmetric normalization is more robust to noise than ot... View full abstract»

• ### Deconvolving Images With Unknown Boundaries Using the Alternating Direction Method of Multipliers

Publication Year: 2013, Page(s):3074 - 3086
Cited by:  Papers (46)
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The alternating direction method of multipliers (ADMM) has recently sparked interest as a flexible and efficient optimization tool for inverse problems, namely, image deconvolution and reconstruction under non-smooth convex regularization. ADMM achieves state-of-the-art speed by adopting a divide and conquer strategy, wherein a hard problem is split into simpler, efficiently solvable sub-problems ... View full abstract»

• ### Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images

Publication Year: 2013, Page(s):3087 - 3096
Cited by:  Papers (25)
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In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geo... View full abstract»

• ### SparCLeS: Dynamic $ell_{1}$ Sparse Classifiers With Level Sets for Robust Beard/Moustache Detection and Segmentation

Publication Year: 2013, Page(s):3097 - 3107
Cited by:  Papers (6)
| | PDF (910 KB) | HTML

Robust facial hair detection and segmentation is a highly valued soft biometric attribute for carrying out forensic facial analysis. In this paper, we propose a novel and fully automatic system, called SparCLeS, for beard/moustache detection and segmentation in challenging facial images. SparCLeS uses the multiscale self-quotient (MSQ) algorithm to preprocess facial images and deal with illuminati... View full abstract»

• ### Cross-Domain Object Recognition Via Input-Output Kernel Analysis

Publication Year: 2013, Page(s):3108 - 3119
Cited by:  Papers (9)
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It is of great importance to investigate the domain adaptation problem of image object recognition, because now image data is available from a variety of source domains. To understand the changes in data distributions across domains, we study both the input and output kernel spaces for cross-domain learning situations, where most labeled training images are from a source domain and testing images ... View full abstract»

• ### Regularized Feature Reconstruction for Spatio-Temporal Saliency Detection

Publication Year: 2013, Page(s):3120 - 3132
Cited by:  Papers (21)
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Multimedia applications such as image or video retrieval, copy detection, and so forth can benefit from saliency detection, which is essentially a method to identify areas in images and videos that capture the attention of the human visual system. In this paper, we propose a new spatio-temporal saliency detection framework on the basis of regularized feature reconstruction. Specifically, for video... View full abstract»

• ### Texture Enhanced Histogram Equalization Using TV-${rm L}^{1}$ Image Decomposition

Publication Year: 2013, Page(s):3133 - 3144
Cited by:  Papers (6)
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Histogram transformation defines a class of image processing operations that are widely applied in the implementation of data normalization algorithms. In this paper, we present a new variational approach for image enhancement that is constructed to alleviate the intensity saturation effects that are introduced by standard contrast enhancement (CE) methods based on histogram equalization. In this ... View full abstract»

• ### Gaussian Blurring-Invariant Comparison of Signals and Images

Publication Year: 2013, Page(s):3145 - 3157
Cited by:  Papers (5)
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We present a Riemannian framework for analyzing signals and images in a manner that is invariant to their level of blurriness, under Gaussian blurring. Using a well known relation between Gaussian blurring and the heat equation, we establish an action of the blurring group on image space and define an orthogonal section of this action to represent and compare images at the same blur level. This co... View full abstract»

• ### Fast SIFT Design for Real-Time Visual Feature Extraction

Publication Year: 2013, Page(s):3158 - 3167
Cited by:  Papers (32)
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Visual feature extraction with scale invariant feature transform (SIFT) is widely used for object recognition. However, its real-time implementation suffers from long latency, heavy computation, and high memory storage because of its frame level computation with iterated Gaussian blur operations. Thus, this paper proposes a layer parallel SIFT (LPSIFT) with integral image, and its parallel hardwar... View full abstract»

## Aims & Scope

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

Full Aims & Scope

## Meet Our Editors

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