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Medicine Meets Virtual Reality 16 - Parallel, Combinatorial, Convergent: NextMed by Design, MMVR 2008

作者： Quang Huy Viet, Huynh Hirai, Shinichi Shirai, Yoshiaki Yamaguchi, Satoshi Tanaka, Hiromi T. Kawasaki, Hiroshi Kawai, Yukiko Computer Vision and Graphics Laboratory Department of Information and Computer Science Saitama University Saitama Japan Laboratory for Integrated Machine Intelligence Department of Robotics Ritsumeikan University Kusatsu Japan Computer Vision Lab. Department of Human and Computer Intelligence Ritsumeikan University Kusatsu Japan Kawai Lab. Department of Computer Science Kyoto Sangyo University Kyoto Japan

ISBN: (纸本)9781586038229

On account of having real-time behavior and being easy to implement, spring meshes have been used for modeling deformable objects. Determining spring stiffness parameters for simulation of soft objects with high accuracy still remains a challenge. Allen Van Gelder derived an approximate formula for determining spring stiffness parameters based on strain analysis. Even though the experimental result showed the effectiveness, the method has not been investigated from a quantitative point of view. In this paper we propose a quantitative method for determining spring stiffness parameters. Moreover we propose a method to improve the accuracy by way of introducing torsional spring into the conventional spring mesh model. © 2008 The authors. All rights reserved.

关键词： Stiffness matrix

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Retrieving faces using adaptive subspace self-organising map

Retrieving faces using adaptive subspace self-organising map

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International Symposium on Intelligent Multimedia, Video and Speech Processing

作者： Zhi-Qiang Liu School of Creative Media City University of Hong Kong Hong Kong China Computer Vision and Machine Intelligence Lab Department of Computer Science and Software Engineerin University of Melbourne VIC Australia

We present the adaptive manifold self-organising map (AMSOM) for a face retrieval system. Our experimental results show that it has an excellent potential for face retrieval applications. As compared to the more tradi... 详细信息

ISBN: (纸本)9628576623

关键词： Face recognition Neurons Face detection Vectors Gabor filters Lattices computer vision Feature extraction Data mining Detectors

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A framework for object-based image retrieval at the semantic level 3rd

A framework for object-based image retrieval at the semantic...

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3rd International Conference on Visual Information Systems, VISUAL 1999

作者： Jia, Linhui Kitchen, Leslie Computer Vision and Machine Intelligence Lab Department of Computer Science and Software Engineering Melbourne University ParkvilleVIC3052 Australia

This paper proposes a framework with essential components and processes for object-based image retrieval based on semantically meaningful classes of objects in images. An instantiation of the framework is presented to... 详细信息

ISBN: (纸本)3540660798

关键词： Semantics

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Classification-driven object-based image retrieval

Classification-driven object-based image retrieval

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： L. Jia L. Kitchen Computer Vision and Machine Intelligence Lab Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia

This paper describes an approach for object-based image retrieval based on classes of objects in images. In this approach, contours of objects are extracted from images and are represented under a scheme which satisfies scale, rotation and translation invariance. Classifier learning techniques are used to classify objects in images into different classes. Image similarity calculation is performed based on class information of objects. Experimental results show that the method is effective and efficient.

关键词： Image retrieval Shape Image databases Image segmentation Classification tree analysis Object recognition Decision trees computer vision machine intelligence computer science

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Learning fuzzy modelling through genetic algorithm for object recognition

Learning fuzzy modelling through genetic algorithm for objec...

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IEEE International Conference on Evolutionary Computation

作者： R. Soodamani Z.Q. Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Australia

The paper proposes a genetic-algorithm-based learning strategy that models membership functions of the fuzzy attributes of surfaces in a model based machine vision system. The objective function aims at enhancing recognition performance in terms of maximizing the degree of discrimination among classes. As a result, the accuracy of recognizing known instances of objects and generalisation capability by recognizing unknown instances of known objects are greatly improved. The performance enhancement of a model based object recognition system consisting of a set of synthetic range images is established by incorporating a dynamic off-line learning mechanism using a genetic algorithm in the feedback path of the system.

关键词： Genetic algorithms Object recognition Fuzzy systems Fuzzy sets Shape Feedback System testing Image segmentation computer vision machine intelligence

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Structured image retrieval

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 1997年第3期8卷 333-357页

作者： Liu, ZQ Sun, JP Computer Vision and Machine Intelligence Lab. (CVMIL) Department of Computer Science University of Melbourne Parkville Victoria 3052 Australia[f1]

Most image-retrieval systems rely on similarity measures for collecting images of similar types. Similarity measures are an integral part in the development of image management systems. In this paper, we propose frame-based similarity measures for accessing structured images, e.g. images can be understood by inferring from objects present and the relationships among them. The image content is described in the following ways: (1) adjacency blocks and/or (2) unary and binary attributes that are used to fill frame slots for representing image structures. The retrieval is based on similarity measures by comparing the contents of the query image and database image. The frame-based representation scheme is application-independent. Our similarity measures allow for images to be retrieved with different degrees of similarity and are flexible. We have developed a prototype system using the paradigms proposed. We demonstrate the usefulness of our system with some experimental results. (C) 1997 Academic Press Limited.

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Embodied computer vision for mobile robots

Embodied computer vision for mobile robots

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： N. Barnes Zhi-Qiang Liu Computer Vision and Machine Intelligence Lab Departmelit of Computer Science University of Melbourne Parkville VIC Australia

The basic idea that the perception of actual embodied beings, be they animal or robotic, is fundamentally related to their embodiment is generally referred to as purposive or animate vision. Research in this field generally emphasises low-level vision techniques. This paper outlines a philosophical basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. The aim is to facilitate the use of high-level vision through an active perception framework. We argue that the classical computer vision paradigm has problems in high-level vision due to an implicit assumption that objects in the world can be objectively subdivided into categories. Further, that through conceptual embodiment, active perception offers a way forward. We present a mobile robot navigation system based on the principles of conceptual embodiment. The system uses object recognition to guide a robot around known objects. The robot's object model is embodied, and this embodiment yields specific advantages for the robot.

关键词： computer vision Mobile robots Robot vision systems Animals Animation Navigation Object recognition

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A knowledge based approach to shape from shading

A knowledge based approach to shape from shading

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： N. Barnes Zhi-Qiang Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Parkville VIC Australia

We study the problem of recovering the approximate three-dimensional shape of an object when knowledge about the object is available. The application of knowledge-based methods to image processing tasks will help overcome problems which arise from processing images using a pixel-based approach. We show that by applying domain specific knowledge, we can fully automate the derivation of the approximate shape of an object. Further, this approach can yield specific advantages over existing approaches, both in terms of computation and processing results. This is a powerful paradigm that will have applications in object recognition, robotic navigation, and domain specific scene understanding.

关键词： Shape Image processing Pixel Object recognition Robotics and automation Navigation Layout

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Markov random field models for handwritten word recognition

Markov random field models for handwritten word recognition

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： Jinhai Cai Zhi-Qiang Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Parkville VIC Australia

ISBN: (纸本)0780342534

We propose the use of a Markov random field model for handwritten word recognition. The main advantage of Markov random field models is that they provide flexible and natural models for the interaction between spatially related random variables in their neighborhood systems via clique functions. In our scheme, Gabor filters are adopted for feature extraction. A fuzzy neighborhood system is proposed and fuzzy matching measurements are developed to cope with the variability of handwritten word shapes. A relaxation labeling algorithm is used to maximize the global compatibilities of Markov random fields. The influence of neighborhood sizes and the iteration number on recognition rates of the system is investigated. Our initial experiments have shown encouraging results.

关键词： Markov random fields Handwriting recognition Fuzzy systems Shape measurement Random variables Gabor filters Feature extraction labeling

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Improving linear separability of classes via feature projection

Improving linear separability of classes via feature project...

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： V. Chandrasekaran Zhi-Qiang Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Carlton VIC Australia

It is well known that a linearly separable set of classes is ideal for a pattern recognition task. The majority of pattern recognition research has been devoted to achieve linear separability of classes by nonlinear input-output mapping. We develop a novel idea of class label separation by projecting each element of the feature vector onto a manifold. The functional characteristics of the manifold associated with each feature type are learnt iteratively from the class label distribution under an optimization criterion. This process attempts to transform an n-dimensional nonlinearly separable feature classification task to an n-dimensional linearly separable problem. The burden of classifying features that are associated with multiple class labels is handled by projections of other discriminating features. This enables fast learning of the classification task by the second stage network which accepts the projected output as its input. If the classification task is modified by an addition of a feature element, the system requires iterative learning of the manifold associated with this new unit only and does not require learning of the whole set of features as seen in conventional neural networks. This iterative knowledge aggregation permits ease of fine tuning and selection of an optimal set of parameters for a given task. The above concept is demonstrated on a set of classification tasks.

关键词： Pattern recognition Vectors Neural networks

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