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Image Processing, IEEE Transactions on

Issue 10 • Date Oct. 2010

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Displaying Results 1 - 25 of 29
  • Table of contents

    Page(s): C1 - C4
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  • IEEE Transactions on Image Processing publication information

    Page(s): C2
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  • Special Section on Distributed Camera Networks: Sensing, Processing, Communication, and Implementation

    Page(s): 2513 - 2515
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  • A Distributed Topological Camera Network Representation for Tracking Applications

    Page(s): 2516 - 2529
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1299 KB) |  | HTML iconHTML  

    Sensor networks have been widely used for surveillance, monitoring, and tracking. Camera networks, in particular, provide a large amount of information that has traditionally been processed in a centralized manner employing a priori knowledge of camera location and of the physical layout of the environment. Unfortunately, these conventional requirements are far too demanding for ad-hoc distributed networks. In this article, we present a simplicial representation of a camera network called the camera network complex (CN-complex), that accurately captures topological information about the visual coverage of the network. This representation provides a coordinate-free calibration of the sensor network and demands no localization of the cameras or objects in the environment. A distributed, robust algorithm, validated via two experimental setups, is presented for the construction of the representation using only binary detection information. We demonstrate the utility of this representation in capturing holes in the coverage, performing tracking of agents, and identifying homotopic paths. View full abstract»

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  • 3-D Target-Based Distributed Smart Camera Network Localization

    Page(s): 2530 - 2539
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    For distributed smart camera networks to perform vision-based tasks such as subject recognition and tracking, every camera's position and orientation relative to a single 3-D coordinate frame must be accurately determined. In this paper, we present a new camera network localization solution that requires successively showing a 3-D feature point-rich target to all cameras, then using the known geometry of a 3-D target, cameras estimate and decompose projection matrices to compute their position and orientation relative to the coordinatization of the 3-D target's feature points. As each 3-D target position establishes a distinct coordinate frame, cameras that view more than one 3-D target position compute translations and rotations relating different positions' coordinate frames and share the transform data with neighbors to facilitate realignment of all cameras to a single coordinate frame. Compared to other localization solutions that use opportunistically found visual data, our solution is more suitable to battery-powered, processing-constrained camera networks because it requires communication only to determine simultaneous target viewings and for passing transform data. Additionally, our solution requires only pairwise view overlaps of sufficient size to see the 3-D target and detect its feature points, while also giving camera positions in meaningful units. We evaluate our algorithm in both real and simulated smart camera networks. In the real network, position error is less than 1" when the 3-D target's feature points fill only 2.9% of the frame area. View full abstract»

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  • Adaptive Sensing and Optimal Power Allocation for Wireless Video Sensors With Sigma-Delta Imager

    Page(s): 2540 - 2550
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1295 KB) |  | HTML iconHTML  

    We consider optimal power allocation for wireless video sensors (WVSs), including the image sensor subsystem in the system analysis. By assigning a power-rate-distortion (P-R-D) characteristic for the image sensor, we build a comprehensive P-R-D optimization framework for WVSs. For a WVS node operating under a power budget, we propose power allocation among the image sensor, compression, and transmission modules, in order to minimize the distortion of the video reconstructed at the receiver. To demonstrate the proposed optimization method, we establish a P-R-D model for an image sensor based upon a pixel level sigma-delta ( ) image sensor design that allows investigation of the tradeoff between the bit depth of the captured images and spatio-temporal characteristics of the video sequence under the power constraint. The optimization results obtained in this setting confirm that including the image sensor in the system optimization procedure can improve the overall video quality under power constraint and prolong the lifetime of the WVSs. In particular, when the available power budget for a WVS node falls below a threshold, adaptive sensing becomes necessary to ensure that the node communicates useful information about the video content while meeting its power budget. View full abstract»

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  • Cluster-Based Distributed Face Tracking in Camera Networks

    Page(s): 2551 - 2563
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1593 KB) |  | HTML iconHTML  

    In this paper, we present a distributed multicamera face tracking system suitable for large wired camera networks. Unlike previous multicamera face tracking systems, our system does not require a central server to coordinate the entire tracking effort. Instead, an efficient camera clustering protocol is used to dynamically form groups of cameras for in-network tracking of individual faces. The clustering protocol includes cluster propagation mechanisms that allow the computational load of face tracking to be transferred to different cameras as the target objects move. Furthermore, the dynamic election of cluster leaders provides robustness against system failures. Our experimental results show that our cluster-based distributed face tracker is capable of accurately tracking multiple faces in real-time. The overall performance of the distributed system is comparable to that of a centralized face tracker, while presenting the advantages of scalability and robustness. View full abstract»

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  • Tracking and Activity Recognition Through Consensus in Distributed Camera Networks

    Page(s): 2564 - 2579
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1779 KB) |  | HTML iconHTML  

    Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems - tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis. View full abstract»

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  • Joint Manifolds for Data Fusion

    Page(s): 2580 - 2594
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (860 KB) |  | HTML iconHTML  

    The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these networks acquire large amounts of very high-dimensional data. For example, even a relatively small network of cameras can generate massive amounts of high-dimensional image and video data. One way to cope with this data deluge is to exploit low-dimensional data models. Manifold models provide a particularly powerful theoretical and algorithmic framework for capturing the structure of data governed by a small number of parameters, as is often the case in a sensor network. However, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for a variety of signal processing algorithms for applications including classification and manifold learning. Additionally, recent results concerning random projections of manifolds enable us to formulate a scalable and universal dimensionality reduction scheme that efficiently fuses the data from all sensors. View full abstract»

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  • Activity Based Matching in Distributed Camera Networks

    Page(s): 2595 - 2613
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    In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties. The proposed method is robust to pose, illumination and geometric effects, unsupervised (does not require any calibration objects). In addition, these features are amenable to low communication bandwidth and distributed network applications. We present quantitative and qualitative results with synthetic and real life examples, and compare the proposed method with scale invariant feature transform (SIFT) based method. We show that our method significantly outperforms the SIFT method when cameras have significantly different orientations. We then describe extensions of our method in a number of directions including topology reconstruction, camera calibration, and distributed anomaly detection. View full abstract»

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  • Cooperative Object Tracking and Composite Event Detection With Wireless Embedded Smart Cameras

    Page(s): 2614 - 2633
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2070 KB) |  | HTML iconHTML  

    Embedded smart cameras have limited processing power, memory, energy, and bandwidth. Thus, many system- and algorithm-wise challenges remain to be addressed to have operational, battery-powered wireless smart-camera networks. We present a wireless embedded smart-camera system for cooperative object tracking and detection of composite events spanning multiple camera views. Each camera is a CITRIC mote consisting of a camera board and wireless mote. Lightweight and robust foreground detection and tracking algorithms are implemented on the camera boards. Cameras exchange small-sized data wirelessly in a peer-to-peer manner. Instead of transferring or saving every frame or trajectory, events of interest are detected. Simpler events are combined in a time sequence to define semantically higher-level events. Event complexity can be increased by increasing the number of primitives and/or number of camera views they span. Examples of consistently tracking objects across different cameras, updating location of occluded/lost objects from other cameras, and detecting composite events spanning two or three camera views, are presented. All the processing is performed on camera boards. Operating current plots of smart cameras, obtained when performing different tasks, are also presented. Power consumption is analyzed based upon these measurements. View full abstract»

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  • A Comprehensive Framework for Image Inpainting

    Page(s): 2634 - 2645
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2344 KB) |  | HTML iconHTML  

    Inpainting is the art of modifying an image in a form that is not detectable by an ordinary observer. There are numerous and very different approaches to tackle the inpainting problem, though as explained in this paper, the most successful algorithms are based upon one or two of the following three basic techniques: copy-and-paste texture synthesis, geometric partial differential equations (PDEs), and coherence among neighboring pixels. We combine these three building blocks in a variational model, and provide a working algorithm for image inpainting trying to approximate the minimum of the proposed energy functional. Our experiments show that the combination of all three terms of the proposed energy works better than taking each term separately, and the results obtained are within the state-of-the-art. View full abstract»

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  • User-Assisted Ink-Bleed Reduction

    Page(s): 2646 - 2658
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2597 KB) |  | HTML iconHTML  

    This paper presents a novel user-assisted approach to reduce ink-bleed interference found in old manuscripts. The problem is addressed by first having the user provide simple examples of foreground ink, ink-bleed, and the manuscript's background. From this small amount of user-labeled data, likelihoods of each pixel being foreground, ink-bleed, or background are computed and used as the data costs of a dual-layer Markov random field (MRF) that simultaneously labels all pixels in both the front and back sides of the manuscript. This user-assisted approach produces better results than existing algorithms without the need for extensive parameter tuning or prior assumptions about the ink-bleed intensity characteristics. Our overall application framework is discussed along with details of the features used in the data costs, a comparison between K-nearest neighbor and support vector machine for likelihood estimation, the dual-layer MRF formulation with associated inter- and intra-layer costs, and a comparison of our approach against other ink-bleed reduction algorithms. View full abstract»

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  • Contourlet Domain Multiband Deblurring Based on Color Correlation for Fluid Lens Cameras

    Page(s): 2659 - 2668
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2021 KB) |  | HTML iconHTML  

    Due to the novel fluid optics, unique image processing challenges are presented by the fluidic lens camera system. Developed for surgical applications, unique properties, such as no moving parts while zooming and better miniaturization than traditional glass optics, are advantages of the fluid lens. Despite these abilities, sharp color planes and blurred color planes are created by the nonuniform reaction of the liquid lens to different color wavelengths. Severe axial color aberrations are caused by this reaction. In order to deblur color images without estimating a point spread function, a contourlet filter bank system is proposed. Information from sharp color planes is used by this multiband deblurring method to improve blurred color planes. Compared to traditional Lucy-Richardson and Wiener deconvolution algorithms, significantly improved sharpness and reduced ghosting artifacts are produced by a previous wavelet-based method. Directional filtering is used by the proposed contourlet-based system to adjust to the contours of the image. An image is produced by the proposed method which has a similar level of sharpness to the previous wavelet-based method and has fewer ghosting artifacts. Conditions for when this algorithm will reduce the mean squared error are analyzed. While improving the blue color plane by using information from the green color plane is the primary focus of this paper, these methods could be adjusted to improve the red color plane. Many multiband systems such as global mapping, infrared imaging, and computer assisted surgery are natural extensions of this work. This information sharing algorithm is beneficial to any image set with high edge correlation. Improved results in the areas of deblurring, noise reduction, and resolution enhancement can be produced by the proposed algorithm. View full abstract»

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  • Efficient Fourier-Wavelet Super-Resolution

    Page(s): 2669 - 2681
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3551 KB) |  | HTML iconHTML  

    Super-resolution (SR) is the process of combining multiple aliased low-quality images to produce a high-resolution high-quality image. Aside from registration and fusion of low-resolution images, a key process in SR is the restoration and denoising of the fused images. We present a novel extension of the combined Fourier-wavelet deconvolution and denoising algorithm ForWarD to the multiframe SR application. Our method first uses a fast Fourier-base multiframe image restoration to produce a sharp, yet noisy estimate of the high-resolution image. Our method then applies a space-variant nonlinear wavelet thresholding that addresses the nonstationarity inherent in resolution-enhanced fused images. We describe a computationally efficient method for implementing this space-variant processing that leverages the efficiency of the fast Fourier transform (FFT) to minimize complexity. Finally, we demonstrate the effectiveness of this algorithm for regular imagery as well as in digital mammography. View full abstract»

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  • Nonlinear Image Upsampling Method Based on Radial Basis Function Interpolation

    Page(s): 2682 - 2692
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7273 KB) |  | HTML iconHTML  

    In this paper, we present a novel edge-directed upsampling method based on radial basis function (RBF) interpolation. In order to remove artifacts such as blurred edges or blocking effects, we suggest a nonlinear method capable of taking edge information into account. The resampling evaluation is determined according to the edge orientation. The proposed scheme is as simple to implement as linear methods but it demonstrates improved visual quality by preserving the edge features better than the classical linear interpolation methods. The algorithm is compared with some well-known linear schemes as well as recently developed nonlinear schemes. The resulting images demonstrate the new algorithm's ability to magnify an image while preserving the edge features. View full abstract»

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  • Joint Decoding of Unequally Protected JPEG2000 Bitstreams and Reed-Solomon Codes

    Page(s): 2693 - 2704
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    In this paper we present joint decoding of JPEG2000 bitstreams and Reed-Solomon codes in the context of unequal loss protection. Using error resilience features of JPEG2000 bitstreams, the joint decoder helps to restore the erased symbols when the Reed-Solomon decoder fails to retrieve them on its own. However, the joint decoding process might become time-consuming due to a search through the set of possible erased symbols. We propose the use of smaller codeblocks and transmission of a relatively small amount of side information with high reliability as two approaches to accelerate the joint decoding process. The accelerated joint decoder can deliver essentially the same quality enhancement as the nonaccelerated one, while operating several times faster. View full abstract»

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  • Sampling Optimization for Printer Characterization by Greedy Search

    Page(s): 2705 - 2711
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (957 KB) |  | HTML iconHTML  

    Printer color characterization, e.g., in the form of an ICC output profile or other proprietary mechanism linking printer RGB/CMYK inputs to resulting colorimetry, is fundamental to a printing system delivering output that is acceptable to its recipients. Due to the inherently nonlinear and complex relationship between a printing system's inputs and the resulting color output, color characterization typically requires a large sample of printer inputs (e.g., RGB/CMYK) and corresponding color measurements of printed output. Simple sampling techniques here lead to inefficiency and a low return for increases in sampling density. While effective solutions have been proposed to this problem very recently, they either do not exploit the full possibilities of the 3-D/4-D space being sampled or they make assumptions about the underlying relationship being sampled. The approach presented here does not make assumptions beyond those inherent in the subsequent tessellation and interpolation applied to the resulting samples. Instead, the tradeoff here is the great computational cost of the initial optimization, which, however, only needs to be performed during the printing system's engineering and is transparent to its end users. Results show a significant reduction in the number of samples needed to match a given level of color accuracy. View full abstract»

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  • Scanned Compound Document Encoding Using Multiscale Recurrent Patterns

    Page(s): 2712 - 2724
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    In this paper, we propose a new encoder for scanned compound documents, based upon a recently introduced coding paradigm called multidimensional multiscale parser (MMP). MMP uses approximate pattern matching, with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. These features give MMP the ability to adjust to the input image's characteristics, resulting in high coding efficiencies for a wide range of image types. This versatility makes MMP a good candidate for compound digital document encoding. The proposed algorithm first classifies the image blocks as smooth (texture) and nonsmooth (text and graphics). Smooth and nonsmooth blocks are then compressed using different MMP-based encoders, adapted for encoding either type of blocks. The adaptive use of these two types of encoders resulted in performance gains over the original MMP algorithm, further increasing the performance advantage over the current state-of-the-art image encoders for scanned compound images, without compromising the performance for other image types. View full abstract»

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  • Backscatter-Contour-Attenuation Joint Estimation Model for Attenuation Compensation in Ultrasound Imagery

    Page(s): 2725 - 2736
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2364 KB) |  | HTML iconHTML  

    Ultrasound B-scan exhibits shadowing and enhancement artifacts due to acoustic wave propagation and spatially varying scatter attenuation across layers of tissues. These artifacts hide underlying echo signals that are truly clinically indicative of diseases. Attenuation compensation estimates and corrects for shadowing and enhancement artifacts, which improves the quality of ultrasound imaging. Block-based attenuation compensation methods, widely employed in commercial scanners, produce results with resolutions limited by the block size. To obtain higher spatial resolution (as desired for quantitative analysis), we present a backscatter-contour-attenuation (BCA) joint estimation model for attenuation compensation in pulse-echo imaging using a set of self-consistent partial differential equations and a contour evolution model. The problem is posed as reconstructing sources of information from observations. We derive the joint estimation model from minimizing a cost functional of separated attributes with region-based isotropic regularizations. A three-step alternating minimization method is adopted towards a tractable numerical solution. Detailed numerical methods are described. The efficacy of the proposed approach is demonstrated using simulated and real images. View full abstract»

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  • A Novel Rotationally Invariant Region-Based Hidden Markov Model for Efficient 3-D Image Segmentation

    Page(s): 2737 - 2748
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1882 KB) |  | HTML iconHTML  

    We present a novel 3-D region-based hidden Markov model (rbHMM) for efficient unsupervised 3-D image segmentation. Our contribution is twofold. First, rbHMM employs a more efficient representation of the image data than current state-of-the-art HMM-based approaches that are based on either voxels or rectangular lattices/grids, thus resulting in a faster optimization process. Second, our proposed novel tree-structured parameter estimation algorithm for the rbHMM provides a locally optimal data labeling that is invariant to object rotation, which is a highly valuable property in segmentation tasks, especially in medical imaging where the segmentation results need to be independent of patient positioning in scanners in order to minimize methodological variability in data analysis. We demonstrate the advantages of our proposed technique over grid-based HMMs by validating on synthetic images of geometric shapes as well as both simulated and clinical brain MRI scans. For the geometric shapes data, our method produced consistently accurate segmentation results that were also invariant to object rotation. For the brain MRI data, our white matter and gray matter segmentation resulted in substantially higher robustness and accuracy levels with improved Dice similarity indices of 4.60% (p=0.0022) and 7.71% (p<;0.0001) , respectively. View full abstract»

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  • A Three-Stage Approach to Shadow Field Estimation From Partial Boundary Information

    Page(s): 2749 - 2760
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2647 KB) |  | HTML iconHTML  

    In this paper, we present a system for estimating the shadow field from a single natural image. Unlike previous works that require extensive user assistance, our system only needs the user to roughly specify the shadow boundary with a broad brush. As the user finishes drawing a stroke, the system starts to estimate the shadow field around the stroke and generates pretty accurate result even if the underlying surface is highly textured. We also propose an optimization scheme to propagate the estimated shadow field to the entire image, achieving a further reduction of user effort required for the system. The shadow field estimated by our system can be used to seamlessly remove the shadow from the image. It is also useful for many shadow editing tasks such as pasting an object's shadow from one image to another. Experimental results on a variety of photos are provided to show the effectiveness of the proposed system. View full abstract»

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  • Image Clustering Using Local Discriminant Models and Global Integration

    Page(s): 2761 - 2773
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (995 KB) |  | HTML iconHTML  

    In this paper, we propose a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI). To deal with the data points sampled from a nonlinear manifold, for each data point, we construct a local clique comprising this data point and its neighboring data points. Inspired by the Fisher criterion, we use a local discriminant model for each local clique to evaluate the clustering performance of samples within the local clique. To obtain the clustering result, we further propose a unified objective function to globally integrate the local models of all the local cliques. With the unified objective function, spectral relaxation and spectral rotation are used to obtain the binary cluster indicator matrix for all the samples. We show that LDMGI shares a similar objective function with the spectral clustering (SC) algorithms, e.g., normalized cut (NCut). In contrast to NCut in which the Laplacian matrix is directly calculated based upon a Gaussian function, a new Laplacian matrix is learnt in LDMGI by exploiting both manifold structure and local discriminant information. We also prove that K-means and discriminative K-means (DisKmeans) are both special cases of LDMGI. Extensive experiments on several benchmark image datasets demonstrate the effectiveness of LDMGI. We observe in the experiments that LDMGI is more robust to algorithmic parameter, when compared with NCut. Thus, LDMGI is more appealing for the real image clustering applications in which the ground truth is generally not available for tuning algorithmic parameters. View full abstract»

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  • Generalized Probabilistic Scale Space for Image Restoration

    Page(s): 2774 - 2780
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4509 KB) |  | HTML iconHTML  

    A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation. View full abstract»

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  • An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation

    Page(s): 2781 - 2787
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2832 KB) |  | HTML iconHTML  

    This correspondence proposes an edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. Two improvement techniques are proposed for the two key steps of maker extraction and pixel labeling, respectively, to make it more effective and efficient for high spatial resolution image segmentation. Moreover, the edge information, detected by the edge detector embedded with confidence, is used to direct the two key steps for detecting objects with weak boundary and improving the positional accuracy of the objects boundary. Experiments on different images show that the proposed method has a good generality in producing good segmentation results. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation. 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