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IEEE Transactions on Haptics: Editorial

IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

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Learning Repetitive Patterns for Classifying Non-Rigidly Deforming Texture Surfaces

Learning Repetitive Patterns for Classifying Non-Rigidly Def...

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International Conference on Image Analysis and Processing

作者： Roman Filipovych Eraldo Ribeiro Computer Vision and Bio-Inspired Computing Laboratory Department of Computer Sciences Florida Institute of Technology Melbourne FL USA

In this paper, we address the relatively unexplored problem of classifying texture surfaces undergoing significant levels of non-rigid deformation. State-of-the-art texture classification methods have demonstrated to be very effective for classifying fronto-parallel texture fields. Recently, affine-invariant descriptors have been proposed as an effective way to model local perspective distortion in textures. However, if the effects of local surface curvature distortion are large, affine-invariant descriptors become unreliable. Our contribution in this paper is twofold. First, we propose a method for learning representative basic elements of non-fronto-parallel texture fields undergoing non-rigid deformations. Secondly, we demonstrate the effectiveness of our texture learning method for the classification of non-rigid deforming texture surfaces. We test our method on a set of images obtained from man-made texture surfaces.

关键词： Surface texture computer vision Nonlinear distortion Videos Learning systems Filters Laboratories Testing Geometry Deformable models

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Self-organized over-clustering avoidance in tuple-space systems

Self-organized over-clustering avoidance in tuple-space syst...

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Congress on Evolutionary Computation

作者： Matteo Casadei Ronaldo Menezes Mirko Viroli Robert Tolksdorf The Dipartimento di Elettronica Informatica e Sistemistica Alma Mater Studiorum Università di Bologna Italy The Department of Computer Sciences Laboratory for Bio-Inspired Computing Florida Institute of Technology Melbourne FL USA The Institut f ür Informatik Freie Universität Berlin Berlin Germany

When it comes to communication performance, open distributed tuple-space systems depend heavily on the proximity of tuples to processes. Researchers have proposed many approaches for storing tuples in a way that processes benefit from the organization of tuples. Although some progress has been made, most of the proposed solutions fail to address the reverse problem: if most tuples are kept near the processes, the system's robustness is affected; the over-clustering of tuples in particular nodes creates a dependence to that particular node. Hence, we have a dichotomy where it is important to organize tuples in clusters near the processes, but it is equally important to avoid over-clustering. The ideal is to have a balance where tuples are clustered but not totally concentrated in very few tuple spaces (eg. one or two). This paper presents a self- organized solution to the tuple distribution problem, in which the possibility of over-clustering is considered.

关键词： Scalability Postal services Robustness Fault tolerance Large-scale systems Standards organizations Organizing Laboratories Particle swarm optimization Entropy

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A Robust High-Capacity Watermarking Algorithm

A Robust High-Capacity Watermarking Algorithm

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IEEE International Conference on Electro-Information Technology

作者： Yi-ta Wu Frank Y. Shih Yih-tyng Wu Computer Aided Diagnosis Group Radiology Department University of Michigan Ann Arbor MI USA Computer Vision Laboratory College of Computing Sciences New Jersey Institute of Technology Newark NJ USA Department of Computer and Information Sciences Nova Southeastern University Fort Lauderdale FL USA

We present a new robust high-capacity watermarking algorithm using the block-based chaotic map and the intersection-based pixels collection. The block-based chaotic map is created to break the local spatial similarity of an image partially. The intersection-based pixels collection is utilized to generate a container and a reference register which is used to indicate the location of the container for watermark embedding and extracting. Experimental results demonstrate that our algorithm can achieve robust high-capacity performance in comparisons with the current watermarking algorithm. It can embed more than 5,050 bits of information in an image of size 202 times 202. It can achieve a low message error rate of less than 0.22 even under various attacks such as additive noise, low-pass filtering, and lossy compression

关键词： Watermarking Chaos Containers Data mining Noise robustness Error analysis Additive noise Low pass filters Filtering Image coding

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Differential Histogram Equalization Based Image Simplification

Differential Histogram Equalization Based Image Simplificati...

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IEEE International Conference on Electro-Information Technology

Image simplification is an important pre-processing task in object segmentation and recognition. The histogram-based approach is the simplest way of image simplification. However, it is difficult to find a general rule of obtaining the proper threshold for all different cases. Recently, the research on image simplification using morphology seems to achieve a better performance. Nevertheless, the quality of simplification results relies on the selection of maskers obtained from complicated morphological operations. In this paper, we present a new perspective view based on differential histogram equalization (DHE) in image simplification. It is developed to not only generate better quality results, but possess lower computational complexity

关键词： Histograms computer vision Computational complexity Pixel Image segmentation Radiology Laboratories Educational institutions Object segmentation Image recognition

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Fast indexing for shape-based image retrieval based on similarity measures of spatial features

Fast indexing for shape-based image retrieval based on simil...

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IEE International Conference on Visual Information Engineering, VIE 2005

作者： Wong, Wai-Tak Shih, Frank Y. Su, Te-Feng Department of Information Management Chung-Hua University No. 707 WuFu Road Hsinchu 30067 Taiwan Computer Vision Laboratory College of Computing Sciences New Jersey Institute of Technology Newark NJ 07102

In this paper, a new method for shape-based image retrieval is presented. We first identify the dominant points of an object. Then, the geometric moment and perimeter (relative to a referenced dominant point) for each dominant point are computed. A synthetic spectrum plotted from the normalized geometric moments vs. normalized distances is obtained. The area covered by and the perimeter of the spectrum are computed. These two values are used as similarity measures for fast indexing in a shape-based image database. The fine-grained comparisons, based on cross-sectional area and perimeter distribution, are performed on the candidates to select the best matching category. Our method can satisfy necessary requirements of cognitively similarity measures from visual perception, such as rotation, scaling and shearing invariance.

关键词： Image retrieval

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Fast indexing for shape-based image retrieval based on similarity measures of spatial features

Fast indexing for shape-based image retrieval based on simil...

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IEE International Conference on Visual Information Engineering (VIE 2005)

作者： Wai-Tak Wong Frank Y. Shih Te-Feng Su Department of Information Management Chung-Hua University No. 707 Sec. 2 WuFu Road HsinChu Taiwan ROC 30067 Computer Vision Laboratory College of Computing Sciences New Jersey Institute of Technology Newark NJ 07102

In this paper, a new method for shape-based image retrieval is presented. We first identify the dominant points of an object. Then, the geometric moment and perimeter (relative to a referenced dominant point) for each dominant point are computed. A synthetic spectrum plotted from the normalized geometric moments vs. normalized distances is obtained. The area covered by and the perimeter of the spectrum are computed. These two values are used as similarity measures for fast indexing in a shape-based image database. The finegrained comparisons, based on cross-sectional area and perimeter distribution, are performed on the candidates to select the best matching category. Our method can satisfy necessary requirements of cognitively similarity measures from visual perception, such as rotation, scaling and shearing invariance.

关键词： Shape Shape measurement Distortion measurement Image databases Encoding Shearing Testing

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Shape metamorphism using p-Laplacian equation

Shape metamorphism using p-Laplacian equation

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International Conference on Pattern Recognition

作者： Ge Cong M. Esser B. Parvin G. Bebis Imaging and Informatics Computing Sciences Lawrence Berkeley National Laboratory Berkeley CA USA Computer Vision Laboratory Department of Computer Sciences and Engineering University of Nevada Reno NV USA

We present a new approach for shape metamorphism, which is a process of gradually changing a source shape (known) through intermediate shapes (unknown) into a target shape (known). The problem, when represented with implicit scalar function, is under-constrained, and regularization is needed. Using the p-Laplacian equation (PLE), we generalize a series of regularization terms based on the gradient of the implicit function, and we show that the present methods lack additional constraints for a more stable solution. The novelty of our approach is in the deployment of a new regularization term when p /spl rarr/ /spl infin/ which leads to the infinite Laplacian equation (ILE). We show that ILE minimizes the supremum of the gradient and prove that it is optimal for metamorphism since intermediate solutions are equally distributed along their normal direction. Applications of the proposed algorithm for 2D and 3D objects are demonstrated.

关键词： Shape Equations Pattern recognition

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Robust detection of road segments in noisy aerial images

Robust detection of road segments in noisy aerial images

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13th International Conference on Pattern Recognition, ICPR 1996

作者： Netanyahu, Nathan S. Philomin, Vasanth Rosenfeld, Azriel Stromberg, Arnold J. Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD 20742-3275 United States Center of Excellence in Space Data and Information Sciences Space Data and Computing Division NASA Goddard Space Flight Center Greenbelt MD 20771 United States Department of Statistics University of Kentucky Lexington KY 40506-0027 United States

ISBN: (纸本)081867282X

This paper treats the problem of detecting straight or circular pieces of road in noisy aerial images. It first uses a local nonlinear operator to detect pixels whose neighborhoods are line-like, and then applies (robust) estimation techniques to find sets of such pixels that lie on, or near straight or circular loci. An (unbiased) ordinary least squares estimator cannot handle outlying data;on the other hand, conventional robust techniques for fitting circular arcs are severely affected by digitization effects and the fact that road circular segments are typically short and shallow. We therefore introduce an estimator that is both robust and statistically efficient. © 1996 IEEE.

关键词： Pixels

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Robust detection of road segments in noisy aerial images

Robust detection of road segments in noisy aerial images

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International Conference on Pattern Recognition

作者： N.S. Netanyahu V. Philomin A. Rosenfeld A.J. Stromberg Center of Excellence in Space Data Information Sciences Space Data and Computing Division NASA Goddard Space Flight Center Greenbelt MD USA Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD USA Department of Statistics University of Kentucky Lexington KY USA

This paper treats the problem of detecting straight or circular pieces of road in noisy aerial images. It first uses a local nonlinear operator to detect pixels whose neighborhoods are line-like, and then applies (robust) estimation techniques to find sets of such pixels that lie on, or near straight or circular loci. An (unbiased) ordinary least squares estimator cannot handle outlying data; on the other hand, conventional robust techniques for fitting circular arcs are severely affected by digitization effects and the fact that road circular segments are typically short and shallow. We therefore introduce an estimator that is both robust and statistically efficient.

关键词： Image segmentation Roads computer vision Laboratories Least squares approximation Automation Educational institutions Noise robustness Satellites Shape

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