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检索条件"机构=Computer Vision and Machine Intelligence Laboratory Department of Computer Science"

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A knowledge based approach to shape from shading

A knowledge based approach to shape from shading

引用

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

引用

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...

引用

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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Tracking facial feature points with Gabor wavelets and shape models 1st

Tracking facial feature points with Gabor wavelets and shape...

引用

Proceedings of the 1997 1st International Conference on Audio- and Video-based Biometric Person Authentication

作者： McKenna, S.J. Gong, S. Wurtz, R.P. Tanner, J. Banin, D. Machine Vision Laboratory Department of Computer Science Queen Mary and Westfield College Mile End Road London United Kingdom Institute for Mathematics and Computing Science University of Groningen P.O. Box 800 NL-9700 AV Netherlands

ISBN: (纸本)3540626603

A feature-based approach to tracking rigid and non-rigid facial motion is described. Feature points are characterised using Gabor wavelets and can be individually tracked by phase-based displacement estimation. In order to achieve robust tracking a flexible shape model is used to impose global constraints upon the local feature points and to constrain the tracker. While there are many applications in facial analysis, the approach can be used for tracking other textured objects.

关键词： Face recognition

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Recognition of 2D shapes through contour metamorphosis

Recognition of 2D shapes through contour metamorphosis

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： R. Singh I. Pavlidis N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA

A novel method for 2D shape recognition is proposed. The method employs as a dissimilarity measure the degree of morphing between a test shape and a reference shape. A physics-based approach substantiates the degree of morphing as a deformation energy and casts the problem as an energy minimization problem. The method operates upon key segmentation points that are provided by an appropriate segmentation algorithm. The recognition paradigm is invariant to translation, rotation, and scaling. It can handle both convex and non-convex shapes. The proposed system exhibits robust recognition behavior and real-time performance in a series of experiments. The experiments also highlight the ability of the method to recognize deformable shapes.

关键词： Shape Wire Testing Defense industry Image recognition Optical character recognition software Character recognition Animation Artificial intelligence Intelligent robots

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Optimal neural network realizations of nonlinear FIR and IIR filters

Optimal neural network realizations of nonlinear FIR and IIR...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： R.J.P. de Figueiredo Laboratory for Machine Intelligence Vision and. Neural Computing Department of Electrical and Computer Engineering University of California Irvine CA USA

A rigorous framework is presented for the design of nonlinear digital and analog filters. The approach followed is based on a Generalized Fock (GF) space framework developed by the Principal Investigator and T.A.W. Dwyer, III. A GF space is a reproducing kernel Hilbert space of discrete or continuous Volterra series with a problem-dependent weighted inner product. The optimal nonlinear filter structure is obtained by an orthogonal projection of the desired filter into the subspace spanned by the representers of interpolating, smoothing, and other design constraint functionals in the appropriate GF space. One of the attractive features of this approach is that the solutions to the filter design problem appear naturally as feedforward (FIR) or recurrent (IIR) artificial neural networks. These results are derived for a GF space F(E/sup N/) on a finite-dimensional Euclidian space E/sup N/. Generalization to functional FIR and IIR nonlinear filters follows immediately from replacing F(E/sup N/) by F(L/sub 2/(I)), where L/sub 2/(I) is the space of square integrable functions on an interval I of the real line.

关键词： Neural networks Finite impulse response filter IIR filters Nonlinear filters Digital filters Kernel Hilbert space Smoothing methods Subspace constraints Artificial neural networks

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Robust pedestrian tracking using a model-based approach

Robust pedestrian tracking using a model-based approach

引用

International Conference on Intelligent Transportation

作者： O. Masoud N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA

This paper presents a real-time system for pedestrian tracking in sequences of grayscale images acquired by a stationary CCD camera. The objective is to integrate this system with a pedestrian control scheme for intersections. The system outputs the spatio-temporal coordinates of each pedestrian during the period the pedestrian is in the scene. Processing is done at three levels: raw images, blobs, and pedestrians. Our method models pedestrians as rectangular patches with a certain dynamic behavior. Kalman filtering is used to estimate pedestrian parameters. The system was implemented on a Datacube MaxVideo 20 equipped with a Datacube Max860 and was able to achieve a peak performance of over 20 frames per second. Experimental results based on indoor and outdoor scenes demonstrated the system's robustness under many difficult situations such as partial and full occlusions of pedestrians.

关键词： Robustness Layout Real time systems Gray-scale Charge coupled devices Charge-coupled image sensors Control systems Kalman filters Filtering Parameter estimation

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Modelling active contours using neural networks isomorphic to boundaries

Modelling active contours using neural networks isomorphic t...

引用

International Conference on Neural Networks

作者： Y.V. Venkatesh N. Rishikesh Computer Vision and Artificial Intelligence Laboratory Department of Electrical Engineering Indian Institute of Science Bangalore India

We propose a new technique, based on self-organization, for localizing salient contours in an image, with applications to, for instance, object and character recognition, stereopsis and motion tracking. A neuronal network which is isomorphic to the template/initial contour is created. This network acts as an active contour, which, using self-organization, undergoes deformation in an attempt to cling on to the nearest salient contour in the test image. The application areas of the model proposed are similar to "snake", but distinct from it both in the underlying mathematics and implementation. The new technique is illustrated with some examples.

关键词： Active contours Neural networks Humans Image edge detection Artificial intelligence Shape Character recognition Object recognition Prototypes Spline

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An invariant pulse-coder for 2-D shape recognition

An invariant pulse-coder for 2-D shape recognition

引用

International Conference on Information, Communications and Signal Processing

作者： N. Rishikesh Y.V. Venkatesh Computer Vision and Artificial Intelligence Laboratory Department of Electrical Engineering Indian Institute of Science Bangalore India

We present some novel schemes for (i) pulse coding for invariant representation of shape; and (ii) a neural architecture for recognizing the encoded shape. The proposed shape encoder utilizes the properties of complex logarithmic mapping (CLM) which transforms rotation and scaling (in its domain) to shifts (in its range). In order to handle this shift, in an attempt to provide increased speed of operation, the encoder converts the CLM output to a sequence of pulses. These pulses are fed to a novel multilayered neural recognizer which (i) invokes template matching with a distinct architecture; and (ii) achieves robustness (to noise and shape deformation) by virtue of its overlapping strategy for code classification. The proposed encoder-recognizer system, which is hardware implementable by a high-speed electronic switching circuit, and can add new shapes on-line to the existing ones, is illustrated by examples.

关键词： Pulse shaping methods Shape Pattern recognition Encoding Fires computer vision Artificial intelligence Laboratories computer architecture Noise robustness

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A metamorphosis-based shape recognition method

A metamorphosis-based shape recognition method

引用

International Conference on Digital Signal Processing (DSP)

作者： R. Singh J. Pavlidis N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA

We present a framework for matching and recognition of planar shapes based on a method from computer graphics based animation, called "shape metamorphosis". In our approach, the "degree of morphing" between two shapes is employed as a dissimilarity measure. A physics-based energy minimization approach is used for optimally computing the "degree of morphing". This measure is shown to have metric properties and invariance to translation, rotation, scaling and mirror symmetry. Experimentations in the recognition of planar shapes, hand-drawn figures, and online cursive words indicate the robustness of the recognition paradigm.

关键词： Shape measurement Optical character recognition software Hidden Markov models Artificial intelligence Intelligent robots Robot vision systems Laboratories computer science Mirrors Robustness

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