By Topic

Image Processing, IEEE Transactions on

Issue 8 • Date Aug. 2013

Filter Results

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

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (117 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Image Processing publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (132 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): 2923 - 2925
    Save to Project icon | Request Permissions | PDF file iconPDF (447 KB)  
    Freely Available from IEEE
  • [Blank page]

    Page(s): 2926
    Save to Project icon | Request Permissions | PDF file iconPDF (5 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): 2927 - 2930
    Save to Project icon | Request Permissions | PDF file iconPDF (453 KB)  
    Freely Available from IEEE
  • Novel True-Motion Estimation Algorithm and Its Application to Motion-Compensated Temporal Frame Interpolation

    Page(s): 2931 - 2945
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2622 KB) |  | HTML iconHTML  

    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 track the projected object motion as closely as possible. TME is obtained by imposing implicit and/or explicit smoothness constraints on the block-matching algorithm. To produce better quality-interpolated frames, the dense motion field at interpolation time is obtained for both forward and backward MVs; then, bidirectional motion compensation using forward and backward MVs is applied by mixing both elegantly. Finally, the performance of the proposed algorithm for MCTFI is demonstrated against recently proposed methods and smoothness constraint optical flow employed by a professional video production suite. Experimental results show that the quality of the interpolated frames using the proposed method is better when compared with the MCFRUC techniques. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Motion Analysis Using 3D High-Resolution Frequency Analysis

    Page(s): 2946 - 2959
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5279 KB) |  | HTML iconHTML  

    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 spectra disperse away from the theoretical motion plane. In this paper, we propose a high-resolution frequency analysis method, described as 3D nonharmonic analysis (NHA), which is only weakly influenced by the analysis window. In addition, we estimate the motion vectors of objects in a video using the plane-clustering method, in conjunction with the least-squares method, for 3D NHA spatiotemporal spectra. We experimentally verify the accuracy of the 3D NHA and its usefulness for a sequence containing complex motions, such as cross-over motion, through comparison with 3D fast Fourier transform. The experimental results show that increasing the frequency resolution contributes to high-accuracy estimation of a motion plane. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Segment Adaptive Gradient Angle Interpolation

    Page(s): 2960 - 2969
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1568 KB) |  | HTML iconHTML  

    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 resizing is performed by interpolating along the approximated isophotes. The proposed method can accommodate arbitrary scaling factors, provides state-of-the-art results in terms of PSNR as well as other quantitative visual quality metrics, and has the advantage of reduced computational complexity that is directly proportional to the number of pixels. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fast Computation of Rotation-Invariant Image Features by an Approximate Radial Gradient Transform

    Page(s): 2970 - 2982
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1139 KB) |  | HTML iconHTML  

    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× faster than SURF while achieving a similar performance for image matching and retrieval. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image Completion by Diffusion Maps and Spectral Relaxation

    Page(s): 2983 - 2994
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4399 KB) |  | HTML iconHTML  

    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 discuss the properties of the induced smoothness and relate it to the underlying assumptions used in contemporary inpainting schemes. As the diffusion embedding is nonlinear and noninvertible, we propose a novel computational approach to approximate the inverse mapping from the inpainted embedding space to the image domain. We formulate the mapping as a discrete optimization problem, solved through spectral relaxation. The effectiveness of the presented method is exemplified by inpainting real images, where it is shown to compare favorably with contemporary state-of-the-art schemes. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Continuous Method for Reducing Interpolation Artifacts in Mutual Information-Based Rigid Image Registration

    Page(s): 2995 - 3007
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2620 KB) |  | HTML iconHTML  

    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 information can be computed accurately by a numerical method. We then extend the method to 2D and 3D. The mutual information function generated is smooth and does not seem to have the typical interpolation artifacts that are commonly observed in other standard models. The relationship between the proposed method and the partial volume (PV) model is described. In addition, we give a theoretical analysis to explain the nonsmoothness of the mutual information function computed by the PV model. Numerical experiments in 2D and 3D are presented to illustrate the smoothness of the mutual information function, which leads to robust and accurate numerical convergence results for solving the image registration problem. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image Inpainting on the Basis of Spectral Structure From 2-D Nonharmonic Analysis

    Page(s): 3008 - 3017
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6135 KB) |  | HTML iconHTML  

    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 limitation by using signal prediction based on the nonharmonic analysis (NHA) technique proposed by the authors. NHA can be used to extract accurate spectra, irrespective of the window function, and its frequency resolution is less than that of the discrete Fourier transform. The proposed method sequentially generates new textures on the basis of the spectrum obtained by NHA. Missing regions from the spectrum are repaired using an improved cost function for 2D NHA. The proposed method is evaluated using the standard images Lena, Barbara, Airplane, Pepper, and Mandrill. The results show an improvement in MSE of about 10-20 compared with the examplar-based method and good subjective quality. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Linear Discriminant Analysis Based on L1-Norm Maximization

    Page(s): 3018 - 3027
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (598 KB) |  | HTML iconHTML  

    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 vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Visual Tracking With Spatio-Temporal Dempster–Shafer Information Fusion

    Page(s): 3028 - 3040
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2214 KB) |  | HTML iconHTML  

    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 subsequences. A support vector machine (SVM) classifier is trained for object/nonobject classification on each of these subsequences, the outputs of which act as separate data sources. To combine the discriminative information from these classifiers, we further present a spatio-temporal weighted DS (STWDS) scheme. In addition, temporally adjacent sources are likely to share discriminative information on object/nonobject classification. To use such information, an adaptive SVM learning scheme is designed to transfer discriminative information across sources. Finally, the corresponding DS belief function of the STWDS scheme is embedded into a Bayesian tracking model. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracking approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dimensionality Reduction for Registration of High-Dimensional Data Sets

    Page(s): 3041 - 3049
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (666 KB) |  | HTML iconHTML  

    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 parameters for registration is minimized. The performance of the proposed dimensionality reduction algorithm is evaluated using three remotes sensing data sets. The experimental results using mutual information-based pairwise registration technique demonstrate that our proposed dimensionality reduction algorithm combines the original data sets to obtain the image pair with more texture, resulting in improved image registration. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multiple-Kernel, Multiple-Instance Similarity Features for Efficient Visual Object Detection

    Page(s): 3050 - 3061
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1783 KB) |  | HTML iconHTML  

    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 various sources, which mimics the use of multiple kernels for kernel machines. Pooling of the similarity values from multiple instances of an object part is introduced to cope with alignment inaccuracy between object instances. To deal with the high dimensionality of the multiple-kernel multiple-instance similarity feature, we propose a forward feature-selection technique and a coarse-to-fine learning scheme to find a set of good exemplars, hence we can produce an efficient classifier while maintaining a good performance. Both the feature and the learning technique have interesting properties. We demonstrate the performance of our method using both synthetic data and real-world visual object detection data sets. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Asymmetric Correlation: A Noise Robust Similarity Measure for Template Matching

    Page(s): 3062 - 3073
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1160 KB) |  | HTML iconHTML  

    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 other cross correlation variants, such as the correlation coefficient. Direct computation of ASC is very slow, as a DFT needs to be calculated for each image window independently. To make the template matching efficient, we develop a much faster algorithm, which carries out a prediction step in linear time and then computes DFTs for only a few promising candidate windows. We extend the proposed template matching scheme to deal with partial occlusion and spatially varying light change. Experimental results demonstrate the robustness of the proposed ASC similarity measure compared to state-of-the-art template matching methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Deconvolving Images With Unknown Boundaries Using the Alternating Direction Method of Multipliers

    Page(s): 3074 - 3086
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1064 KB) |  | HTML iconHTML  

    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 (e.g., using fast Fourier or wavelet transforms, or simple proximity operators). In deconvolution, one of these sub-problems involves a matrix inversion (i.e., solving a linear system), which can be done efficiently (in the discrete Fourier domain) if the observation operator is circulant, i.e., under periodic boundary conditions. This paper extends ADMM-based image deconvolution to the more realistic scenario of unknown boundary, where the observation operator is modeled as the composition of a convolution (with arbitrary boundary conditions) with a spatial mask that keeps only pixels that do not depend on the unknown boundary. The proposed approach also handles, at no extra cost, problems that combine the recovery of missing pixels (i.e., inpainting) with deconvolution. We show that the resulting algorithms inherit the convergence guarantees of ADMM and illustrate its performance on non-periodic deblurring (with and without inpainting of interior pixels) under total-variation and frame-based regularization. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images

    Page(s): 3087 - 3096
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1627 KB) |  | HTML iconHTML  

    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 geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation-maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • SparCLeS: Dynamic \ell _{1} Sparse Classifiers With Level Sets for Robust Beard/Moustache Detection and Segmentation

    Page(s): 3097 - 3107
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (910 KB) |  | HTML iconHTML  

    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 illumination variation. Histogram of oriented gradients (HOG) features are extracted from the preprocessed images and a dynamic sparse classifier is built using these features to classify a facial region as either containing skin or facial hair. A level set based approach, which makes use of the advantages of both global and local information, is then used to segment the regions of a face containing facial hair. Experimental results demonstrate the effectiveness of our proposed system in detecting and segmenting facial hair regions in images drawn from three databases, i.e., the NIST Multiple Biometric Grand Challenge (MBGC) still face database, the NIST Color Facial Recognition Technology FERET database, and the Labeled Faces in the Wild (LFW) database. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Cross-Domain Object Recognition Via Input-Output Kernel Analysis

    Page(s): 3108 - 3119
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (703 KB) |  | HTML iconHTML  

    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 are from a different target domain. To address the feature distribution change issue in the reproducing kernel Hilbert space induced by vector-valued functions, we propose a domain adaptive input-output kernel learning (DA-IOKL) algorithm, which simultaneously learns both the input and output kernels with a discriminative vector-valued decision function by reducing the data mismatch and minimizing the structural error. We also extend the proposed method to the cases of having multiple source domains. We examine two cross-domain object recognition benchmark data sets, and the proposed method consistently outperforms the state-of-the-art domain adaptation and multiple kernel learning methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Regularized Feature Reconstruction for Spatio-Temporal Saliency Detection

    Page(s): 3120 - 3132
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1850 KB) |  | HTML iconHTML  

    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 saliency detection, both the temporal and spatial saliency detection are considered. For temporal saliency, we model the movement of the target patch as a reconstruction process using the patches in neighboring frames. A Laplacian smoothing term is introduced to model the coherent motion trajectories. With psychological findings that abrupt stimulus could cause a rapid and involuntary deployment of attention, our temporal model combines the reconstruction error, regularizer, and local trajectory contrast to measure the temporal saliency. For spatial saliency, a similar sparse reconstruction process is adopted to capture the regions with high center-surround contrast. Finally, the temporal saliency and spatial saliency are combined together to favor salient regions with high confidence for video saliency detection. We also apply the spatial saliency part of the spatio-temporal model to image saliency detection. Experimental results on a human fixation video dataset and an image saliency detection dataset show that our method achieves the best performance over several state-of-the-art approaches. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Texture Enhanced Histogram Equalization Using TV- {\rm L}^{1} Image Decomposition

    Page(s): 3133 - 3144
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3406 KB) |  | HTML iconHTML  

    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 paper, we initially apply total variation (TV) minimization with a L1 fidelity term to decompose the input image with respect to cartoon and texture components. Contrary to previous papers that rely solely on the information encompassed in the distribution of the intensity information, in this paper, the texture information is also employed to emphasize the contribution of the local textural features in the CE process. This is achieved by implementing a nonlinear histogram warping CE strategy that is able to maximize the information content in the transformed image. Our experimental study addresses the CE of a wide variety of image data and comparative evaluations are provided to illustrate that our method produces better results than conventional CE strategies. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Gaussian Blurring-Invariant Comparison of Signals and Images

    Page(s): 3145 - 3157
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4326 KB) |  | HTML iconHTML  

    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 comparison is based on geodesic distances on the section manifold which, in turn, are computed using a path-straightening algorithm. The actual implementations use coefficients of images under a truncated orthonormal basis and the blurring action corresponds to exponential decays of these coefficients. We demonstrate this framework using a number of experimental results, involving 1D signals and 2D images. As a specific application, we study the effect of blurring on the recognition performance when 2D facial images are used for recognizing people. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fast SIFT Design for Real-Time Visual Feature Extraction

    Page(s): 3158 - 3167
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1247 KB) |  | HTML iconHTML  

    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 hardware design with an on-the-fly feature extraction flow for real-time application needs. Compared with the original SIFT algorithm, the proposed approach reduces the computational amount by 90% and memory usage by 95%. The final implementation uses 580-K gate count with 90-nm CMOS technology, and offers 6000 feature points/frame for VGA images at 30 frames/s and ~ 2000 feature points/frame for 1920 × 1080 images at 30 frames/s at the clock rate of 100 MHz. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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