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IEEE International Conference on image Processing

作者： Wang, Hui Zhang, Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (纸本)9781424479948

In this paper, we propose a novel method to adaptively apply shape prior in graph cut segmentation. By incorporating shape priors in an adaptive way, we introduce a robust way to harness shape prior in graph cut segmentation. Since traditional graph cut approaches with shape prior may fail in cases where parameters for shape prior term are not set appropriately, incorporation of shape priors adaptively within this framework mitigates these problems. To address this issue, we propose to adaptively apply shape prior based on a shape probability map, defined to reflect the need of shape prior at each location of an image. We show that the proposed method can be easily applied to existing algorithms of graph cut segmentation with shape prior, such as level set based shape prior method, and star shape prior graph cut. We validate our method in various types of images corrupted by significant noise and intensity inhomogeneities. Convincing results are obtained.

关键词： Adaptive Shape Prior Graph Cut interactive image segmentation

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Semi-Supervised Classification via Local Spline Regression

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010年第11期32卷 2039-2053页

作者： Xiang, Shiming Nie, Feiping Zhang, Changshui Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

This paper presents local spline regression for semi-supervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the data points directly to be class labels. The spline is composed of polynomials and Green's functions. It is smooth, nonlinear, and able to interpolate the scattered data points with high accuracy. Specifically, in each neighborhood, an optimal spline is estimated via regularized least squares regression. With this spline, each of the neighboring data points is mapped to be a class label. Then, the regularized loss is evaluated and further formulated in terms of class label vector. Finally, all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on the labeled and unlabeled data. To achieve the goal of semi-supervised classification, an objective function is constructed by combining together the global loss of the local spline regressions and the squared errors of the class labels of the labeled data. In this way, a transductive classification algorithm is developed in which a globally optimal classification can be finally obtained. In the semi-supervised learning setting, the proposed algorithm is analyzed and addressed into the Laplacian regularization framework. Comparative classification experiments on many public data sets and applications to interactive image segmentation and image matting illustrate the validity of our method.

关键词： Semi-supervised classification local spline regression interactive image segmentation

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image segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor

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IEEE TRANSACTIONS ON image PROCESSING 2009年第10期18卷 2289-2302页

作者： Han, Shoudong Tao, Wenbing Wang, Desheng Tai, Xue-Cheng Wu, Xianglin Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Nanyang Technol Univ Div Math Sci Singapore 63761 Singapore Nanyang Technol Univ Div Math Sci Sch Math & Phys Sci Singapore 637616 Singapore

In this paper, we propose an interactive color natural image segmentation method. The method integrates color feature with multiscale nonlinear structure tensor texture (MSNST) feature and then uses GrabCut method to obtain the segmentations. The MSNST feature is used to describe the texture feature of an image and integrated into GrabCut framework to overcome the problem of the scale difference of textured images. In addition, we extend the Gaussian Mixture Model (GMM) to MSNST feature and GMM based on MSNST is constructed to describe the energy function so that the texture feature can be suitably integrated into GrabCut framework and fused with the color feature to achieve the more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the symmetric KL divergence is chosen to produce the estimates of the tensor statistics instead of the Riemannian structure of the space of tensor. The Conjugate norm was employed using Locality Preserving Projections (LPP) technique as the distance measure in the color space for more discriminating power. An adaptive fusing strategy is presented to effectively adjust the mixing factor so that the color and MSNST texture features are efficiently integrated to achieve more robust segmentation performance. Last, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. Experiments using synthesis texture images and real natural scene images demonstrate the superior performance of our proposed method.

关键词： Adaptive fusion graph cuts interactive image segmentation multiscale nonlinear structure tensor (MSNST)

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Sub-pixel segmentation with the image Foresting Transform

Sub-pixel Segmentation with the Image Foresting Transform

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13th International Workshop on Combinatorial image Analysis

作者： Malmberg, Filip Lindblad, Joakim Nystrom, Ingela Uppsala Univ Ctr Image Anal Uppsala Sweden Swedish Univ Agr Sci S-90183 Umea Sweden

ISBN: (纸本)9783642102080

The image Foresting Transform (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned user-defined labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. In its original form, the IFT produces crisp segmentations, i.e., each image element is assigned the label of exactly one seem-point. Here, we propose a modified version of the JET that computes region boundaries with sub-pixel precision by allowing mixed labels at region boundaries. We demonstrate that the proposed sub-pixel IFT allows properties of the segmented object to be measured with higher precision.

关键词： image foresting transform interactive image segmentation Sub-pixel precision

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SEED image SELECTION IN interactive COsegmentation

SEED IMAGE SELECTION IN INTERACTIVE COSEGMENTATION

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16th IEEE International Conference on image Processing

作者： Batra, Dhruv Parikh, Devi Kowdle, Adarsh Chen, Tsuhan Luo, Jiebo Carnegie Mellon Univ Pittsburgh PA 15213 USA Cornell Univ Ithaca NY 14853 USA Eastman Kodak Co Rochester NY 14650 USA

ISBN: (纸本)9781424456536

interactive image segmentation is a powerful paradigm that allows users to direct the segmentation algorithm towards a desired output. However, marking scribbles on multiple images is a cumbersome process. Recent works show that statistics collected from user input in a single image can be shared among a group of related images to perform interactive cosegmentation. Most works use a naive heuristic of requesting the user input on a random image from the group. We show that in practice, selecting the right image to scribble on is critical to the resulting segmentation quality. In this paper, we address the problem of Seed image Selection, i.e., deciding which image among a group of related images should be presented to the user for scribbling. We formulate our approach as a classification problem and show that our approach outperforms the naive heuristic used by other works.

关键词： interactive Cosegmentation interactive image segmentation Object Cutout

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Learning a Mahalanobis distance metric for data clustering and classification

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PATTERN RECOGNITION 2008年第12期41卷 3600-3612页

作者： Xiang, Shiming Nie, Feiping Zhang, Changshui Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links. As one kind of side information, a must-link indicates the pair of the two data points must be in a same class, while a cannot-link indicates that the two data points must be in two different classes. Given must-link and cannot-link information, our goal is to learn a Mahalanobis distance metric. Under this metric, we hope the distances of point pairs in must-links are as small as possible and those of point pairs in cannot-links are as large as possible. This task is formulated as a constrained optimization problem, in which the global optimum can be obtained effectively and efficiently. Finally, some applications in data clustering, interactive natural image segmentation and face pose estimation are given in this paper. Experimental results illustrate the effectiveness of our algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： distance metric learning Mahalanobis distance global optimization data clustering interactive image segmentation face pose estimation

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3D visualization to assist iterative object definition from medical images

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2006年第4期30卷 217-230页

作者： Audigier, Romaric Lotufo, Roberto Falcao, Alexandre Univ Estadual Campinas UNICAMP Inst Comp BR-13084971 Campinas SP Brazil Univ Estadual Campinas UNICAMP Sch Elect & Comp Engn BR-13083852 Campinas SP Brazil

In medical imaging, many applications require visualization and/or analysis of three-dimensional (3D) objects (e.g. organs). At same time, object definition often requires considerable user assistance. In this process, objects are usually defined in an iterative way and their visualization during the process is very important to guide the user's actions for the next iteration. The usual procedure provides slice visualization during object definition (segmentation) and 3D visualization afterward. In this paper, we propose and evaluate efficient methods to provide 3D visualization during iterative object definition. The methods combine the differential image foresting transform for segmentation with voxel splatting/ray casting for visualization. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： 3D visualization interactive image segmentation watershed transform image foresting transform voxel splatting raycasting rendering of medical images

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Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

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Journal of Systems Engineering and Electronics 2005年第4期16卷 750-755页

作者： Tian Chunna Gao Xinbo School of Electronic Engineering Xidian Univ. Xi'an 710071 P. R. China

A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

关键词： fuzzy connectedness interactive image segmentation image-sequences segmentation multiple objects segmentation fast algorithm.

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Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2005年第10期27卷 1549-1554页

作者： Tian, Chun-Na Gao, Xin-Bo Halidan, A. School of Electronic Engineering Xidian University Xi'an 710071 China School of Electrical Engineering Xinjiang University Urumqi 830008 China

A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the existing algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex background and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

关键词： Fuzzy connectedness image-sequences segmentation interactive image segmentation Multiple objects segmentation

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Random walks for interactive alpha-matting

Random walks for interactive alpha-matting

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5th IASTED International Conference on Visualization, Imaging, and image Processing

作者： Grady, L Schiwietz, T Aharon, S Westermann, R Siemens Corp Res Imaging & Visualizat Princeton NJ USA

ISBN: (纸本)0889865280

interactive, efficient, methods of foreground extraction and alpha-matting are of increasing practical importance for digital image editing. Although several new approaches to this problem have recently been developed, many challenges remain. We propose a new technique based on random walks that has the following advantages: First, by leveraging a recent technique from manifold learning theory, we effectively use RGB values to set boundaries for the random walker, even in fuzzy or low-contrast images. Second, the algorithm is straightforward to implement, requires specification of only a single free parameter (set the same for all images), and performs the segmentation and alpha-matting in a single step. Third, the user may locally fine tune the results by interactively manipulating the foreground[background maps. Finally, the algorithm has an inherit parallelism that leads to a particularly efficient implementation via the graphics processing unit (GPU). Our method. processes a 1024 x 1024 image at the interactive speed of 0.5 seconds and, most importantly, produces high-quality results. We show that our algorithm can generate good segmentation and matting results at an interactive rate with minimal user interaction.

关键词： interactive image segmentation alpha matting random walks general purpose GPU image editing object extraction

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