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Image and Graphics (ICIG), 2011 Sixth International Conference on

Date 12-15 Aug. 2011

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  • [Front cover]

    Publication Year: 2011 , Page(s): C1
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  • [Title page i]

    Publication Year: 2011 , Page(s): i
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  • [Title page iii]

    Publication Year: 2011 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2011 , Page(s): iv
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  • Table of contents

    Publication Year: 2011 , Page(s): v - xvi
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  • Preface

    Publication Year: 2011 , Page(s): xvii
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  • Conference organization

    Publication Year: 2011 , Page(s): xviii - xix
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  • Program Committee Members

    Publication Year: 2011 , Page(s): xx - xxiv
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  • Keynote Abstracts

    Publication Year: 2011 , Page(s): xxv - xxix
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    These keynote speeches discuss the following: Finding it Now: Stream Mining in Real Time; Patterns of Motion: Discovery and Generalized Representation; and 3D Structure Reconstruction from Videos. View full abstract»

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  • Tutorial abstract

    Publication Year: 2011 , Page(s): xxx - xxxi
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    Summary form only given, as follows. The goal of this tutorial is to provide the ICIG community with an introduction to the quickly developing area of low-rank matrix recovery. Low-rank (or approximately low-rank) matrices arise in a great number of applications involving image and video data. A few recurrent examples in this tutorial will include aligning batches of images, super-resolution and video inpainting, and in background modeling for visual tracking and surveillance. However, in real applications our observations are never prefect: observations are always noisy, often missing, and sometimes grossly or even maliciously corrupted. The recent excitement surrounding low-rank matrix recovery is due to very recent results showing that under fairly general circumstances, the low rank recovery problem can be efficiently and exactly solved, by convex programming. These theoretical advances have inspired a flurry of algorithmic work, giving increasingly practical and scalable algorithms for solving the corresponding convex programs. The theory and algorithms described above, which the proposers have had a strong role in developing, are already beginning to influence practice in a number of areas, including collaborative filtering and computer vision. However, we believe these results are poised for even stronger impact in image processing. The purpose of this tutorial is to bring these ideas to the ICIG community, by giving a solid and unified introduction to the existing theoretical and algorithmic state of the art in the area, and then show how this theory and algorithms are already being used to solve real imaging problems. View full abstract»

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  • Workshop abstracts [3 abstracts]

    Publication Year: 2011 , Page(s): xxxii - xxxv
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    Provides an abstract for each of the three workshop presentations and a brief professional biography of each presenter. View full abstract»

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  • A Semi-automatic Method for Vascular Image Segmentation

    Publication Year: 2011 , Page(s): 3 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (768 KB) |  | HTML iconHTML  

    Vascular diseases are major public heath problem around the world. Vessel segmentation has been widely concerned because it is a key step for diagnosis and surgical planning. Among past strategies, multi-scale line filters are very popular detectors. However, multi-scale integration results in undesirable diffusion when two vessels are closely located. To avoid this problem, we use gradient vector flow as vector field and introduce a vesselness measure to detect vessel which gives high and homogeneous output for line structure so that it is more suitable for segmentation over Frangi's vesselness measure. Level set method is applied to perform vessel segmentation. Our model is tested on real images. Experimental results demonstrate that our approach can successfully separate closely adjacent vessels and address the problems of low contrast and varying vessel width. It shows better performance than multi-scale approach. Furthermore, gradient vector flow makes the contour moving into boundary concavities. View full abstract»

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  • Exemplar-Based Image Inpainting with Collaborative Filtering

    Publication Year: 2011 , Page(s): 8 - 11
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (398 KB) |  | HTML iconHTML  

    This paper proposes a novel patch synthesis approach for exemplar-based propagation in image in painting. Currently, plural non-local exemplar patches synthesis is widely adopted to fill missing pixels. It generally provides good results, but sometimes shows poor visual quality due to dissimilarity between exemplars and targets. In this paper, a collaborative filtering approach is used to enhance the exemplar-based propagation to obtain ideal in painting results. The approach works on pixel level information, while many exemplar-based propagation algorithms focus on patch level information. Object removal and stain image recovering are carried out to evaluate the proposed approach. Experiments show that our approach provides good visual quality in object removal and high PSNR in stain image recovering. View full abstract»

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  • A Unified Method Based on Wavelet Transform and C-V Model for Crack Segmentation of 3D Industrial CT Images

    Publication Year: 2011 , Page(s): 12 - 16
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (474 KB) |  | HTML iconHTML  

    Accurate segmentation of cracked body from three-dimensional (3D) industrial Computed Tomography (CT) images is an important step in the process of crack measurement and automatic recognition. In this paper we present a fast method for the segmentation of cracked body. The improved algorithm incorporates wavelet transform and Chan and Vese (C-V) model as key components. The 3D wavelet transform is applied for detecting rough edges. Then region growing is used to find a suitable region which contains cracked body. Based on the resulting volume data, 3D C-V model is used to capture the edges of cracked body. The improved method can locate rough regions by using wavelet modulus maxima, which not only reduces the amount of data C-V model processed, but also provides initial contour surface that can accelerate the convergence speed of C-V model. Experimental results illustrate our method can accurately detect the cracked surface, as well as give computational savings of segmentation which satisfy the demand of defects detection of industrial CT. View full abstract»

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  • Multi-focus Image Fusion by Nonsubsampled Shearlet Transform

    Publication Year: 2011 , Page(s): 17 - 21
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1153 KB) |  | HTML iconHTML  

    In this paper we introduce the nonsubsampled shear let transform for multi-focus image fusion. In the proposed method, source images are decomposed by nonsubsampled shear let transform firstly. Then the decomposition coefficients are merged according to the given fusion rule. Finally the fused image is reconstructed by inverse nonsubsampled shear let transform. The experimental results over five pairs of registered multi-focus images and one pair of mis-registered multi-focus images demonstrate the superiority of the proposed method. View full abstract»

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  • Saliency Modulated High Dynamic Range Image Tone Mapping

    Publication Year: 2011 , Page(s): 22 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3399 KB) |  | HTML iconHTML  

    This paper presents a new high dynamic range image tone mapping technique - saliency modulated tone mapping (SMTM). The HDR image is not directly viewable and dynamic range compression will unavoidably loose information. A saliency map analyzes the visual importance of the regions and can therefore direct the tone mapping operators to preserve the visual conspicuity of the regions that should more likely attract visual attention. In SMTM, we have developed a very fast algorithm to first compute the visual saliency map of the high dynamic range radiance map and then directly use the saliency of the local regions to control the local tone mapping curve such that highly salient regions will have their details and contrast better protected so as to remain salient and attract visual attention in the tone mapped display. We present experimental results to show that SMTM provides competitive performances to state of the art tone mapping techniques in rending visually pleasing low dynamic range displays. We also show that SMTM is better able to preserve the visual saliency of the HDR image and that SMTM renders high saliency regions to stand out to attract observers attention. View full abstract»

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  • Locally Adaptive Shearlet Denoising Based on Bayesian MAP Estimate

    Publication Year: 2011 , Page(s): 28 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (485 KB) |  | HTML iconHTML  

    A locally adaptive Bayesian estimate for image denoising is proposed by exploiting the correlation among image shear let coefficients in a sub-band. The Laplacian distribution can model a wide range of process, from heavy-tailed to less heavy-tailed processes. This paper deduces Laplacian prior distribution based the MAP estimate formula and sub-band adaptive threshold. Finally, a simulation is carried out to show the effectiveness of the new estimate. Experiment results demonstrate that compared with classical sub-band adaptive algorithms, the new denoising method has significantly increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect. View full abstract»

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  • Integrating Boundary Cue with Superpixel for Image Segmentation

    Publication Year: 2011 , Page(s): 33 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1159 KB) |  | HTML iconHTML  

    This paper researches image segmentation as a global optimization problem and proposes a new way, which is called superpixel status model, to integrate boundary and region cue. Superpixel status model is a label model which describes the joint distribution of boundary and region classification in a Bayesian framework. For organizing a boundary classifier, the contour of super pixel is decomposed into multiple line segments, and a robust line descriptor is presented to form line feature vector. Finally, an objective function is defined to assemble all super pixels statuses across the entire image for segmentation. Experiments and results show that the effectiveness of our approach. View full abstract»

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  • Integrating Low-level and Semantic Features for Object Consistent Segmentation

    Publication Year: 2011 , Page(s): 39 - 44
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (767 KB) |  | HTML iconHTML  

    The aim of semantic segmentation is to assign each pixel a semantic label. Numerous methods for semantic segmentation have been proposed in recent years and most of them chose pixel or super pixel as the processing primitives. However, as the information contained in a pixel or a super pixel is not discriminative enough, the outputs of these algorithms are usually not object consistent. To tackle this problem, we introduce the concept of object-like regions as a new and higher level processing primitive. We first experimentally showed that using object-like regions as processing primitives can boost semantic segmentation accuracy, and then proposed a novel method to produce object-like regions by integrating state-of art low-level segmentation algorithms with typical semantic segmentation algorithms through a novel semantic feature feedback mechanism. We present experimental results on the publicly available image understanding database MSRC21 and show that the new method can achieve state of the art semantic segmentation results with far fewer processing primitives. View full abstract»

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  • A Fast Exact Euclidean Distance Transform Algorithm

    Publication Year: 2011 , Page(s): 45 - 49
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (554 KB) |  | HTML iconHTML  

    Euclidean distance transform is widely used in many applications of image analysis and processing. Traditional algorithms are time-consuming and difficult to realize. This paper proposes a novel fast distance transform algorithm. Firstly, mark each foreground's nearest background pixel's position in the row and column, and then use the marks scan the foreground area and figure out the first foreground pixel distance transform information, According to the first pixel' information, design four small regions for its 4-adjacent foreground pixel and also based on the marks search out each adjacent foreground pixel's nearest background pixel. As the region growing, iteratively process each adjacent pixel until all the foreground pixels been resolved. Our algorithm has high efficiency and is simple to implement. Experiments show that comparing to the existing boundary striping and contour tracking algorithm, our algorithm demonstrates a significant improvement in time and space consumption. View full abstract»

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  • Reverse Seam Carving

    Publication Year: 2011 , Page(s): 50 - 55
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5372 KB) |  | HTML iconHTML  

    Seam carving is an effective operator supporting content-aware resizing for both image reduction and expansion. However, repeated seam removing and inserting processes lead to excessively distortion image when imposed on seam insertion then removal operations or the other way around. With considering the relationship between seam removing and inserting processes, we present an ameliorated energy function to minimize aliasing. "Forward Energy" is an effective improvement only to image reduction. Moreover, we propose a novel "Visual Points" structure which distinguish the "Forward Energy" of seam insertion from that of seam removal, and improve seam insertion operations greatly. Qualitative and quantitative experiments show that the proposed method can achieve high quality as compared to existed methods. View full abstract»

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  • Low Bit Rate Compression for SAR Image Based on Blocks Reordering and 3D Wavelet Transform

    Publication Year: 2011 , Page(s): 56 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1203 KB) |  | HTML iconHTML  

    Due to that there are a large number of similar characteristics in surface structure of the SAR image, a novel SAR image compression algorithm was proposed based on 3D wavelet transform after block reordering. This proposed algorithm consists of four successive steps: divide the image into sub-blocks with equal size, reorder the sub-blocks according to the similarity measured by weighted Euclidean distance to form 3D array, then 3D wavelet transform and 3D-SPIHT coding are employed for encoding. Experimental results of real SAR images show that the proposed method outperforms the traditional wavelet-based SPIHT in terms of PSNR, especially at the low-bit rate. View full abstract»

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  • Distribution-Based Active Contour Model for Medical Image Segmentation

    Publication Year: 2011 , Page(s): 61 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1624 KB) |  | HTML iconHTML  

    Having being regarded as one of the classical methods in image segmentation, geodesic active contours (GAC) have the flaws of boundary leaking and expensive evolving time. In this paper, we present a distribution-based active contour model by measuring the Bhattacharyya distance between probability distributions of the object and background along with the evolution of GAC model. Due to combining the image cues of edge and statistical information which is computed by using kernel density estimation, this hybrid methodology prevents the boundary leaking as well as the under segmentation problem. Experimental results on the medical images show the improvements of our method in terms of comparisons with original GAC model, Bhattacharyya gradient flow, texture-based GAC and Li's active contour model. View full abstract»

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  • Image Interpolation Using Autoregressive Model and Gauss-Seidel Optimization

    Publication Year: 2011 , Page(s): 66 - 69
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2198 KB) |  | HTML iconHTML  

    In this paper we propose a simple yet effective image interpolation algorithm based on autoregressive model. Unlike existing algorithms which rely on low resolution pixels to estimate interpolation coefficients, we optimize the interpolation coefficients and high resolution pixel values jointly from one optimization problem. Although the two sets of variables are coupled in the cost function, the problem can be effectively solved using Gauss-Seidel method. We prove the iterations are guaranteed to converge. Experiments show that on average we have over 3dB gain compared to bicubic interpolation and over 0.1dB gain compared to SAI. View full abstract»

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  • Learning Based Adaptive Denoising Approach for Image Interpolation

    Publication Year: 2011 , Page(s): 70 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (414 KB) |  | HTML iconHTML  

    In this paper, we propose an effective image interpolation framework through learning based adaptive denoisng approach. In the local area, error pattern between original image and interpolated image is treated as stationary Gaussian distribution. Under the initial estimation, the proposed method apply the patch as the basic unit, in which Multiclass SVM classifier is used to determine iteration number and denoise parameters. There are two steps in iterative processing, including adaptive denoise and data fusion. Experiment results shown the proposed method can significantly improve the interpolated image quality both subjectively and objectively. View full abstract»

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