In this paper we discuss the use of covariance methods in invariant feature extraction,texture segmentation,edge detection,and surface geometry *** covariance technique is used to compute local descriptors and to inde...
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In this paper we discuss the use of covariance methods in invariant feature extraction,texture segmentation,edge detection,and surface geometry *** covariance technique is used to compute local descriptors and to index roughness,anisotropy, or general textural *** also present a simple yet effective edge detection algorithm using a neural network which is trained by invariant features generated from covariance matrices.
Robust tracking and segmentation of faces is a prerequisite for face analysis and recognition. In this paper we describe an approach to this problem which is well suited to surveillance applications with poorly constr...
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Robust tracking and segmentation of faces is a prerequisite for face analysis and recognition. In this paper we describe an approach to this problem which is well suited to surveillance applications with poorly constrained viewing conditions. It integrates motion-based tracking with model based face detection to produce segmented face sequences from complex scenes containing several people. The motion of moving image contours was estimated using temporal convolution and a temporally consistent list of moving objects was maintained. Objects were tracked using Kalman filters. Faces were detected using a neural network. The essence of the system is that the motion tracker is able to focus attention for a face detection network whilst the latter is used to aid the tracking process.
Visual perception of faces is invariant under many transformations, perhaps the most problematic of which is pose change (face rotating in depth). We use a variation of Gabor wavelet transform (GWT) as a representatio...
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Visual perception of faces is invariant under many transformations, perhaps the most problematic of which is pose change (face rotating in depth). We use a variation of Gabor wavelet transform (GWT) as a representation framework for investigating face pose measurement. Dimensionality reduction using principal components analysis (PCA) enables pose changes to be visualised as manifolds in low-dimensional subspaces and provides a useful mechanism for investigating these changes. The effectiveness of measuring face pose with GWT representations was examined using PCA. We discuss our experimental results and draw a few preliminary conclusions.
Many researchers have turned to sensing, and in particular computervision, to create more flexible robotic systems. computervision is often required to provide data for the grasping of a target. Using a vision syste...
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Many researchers have turned to sensing, and in particular computervision, to create more flexible robotic systems. computervision is often required to provide data for the grasping of a target. Using a vision system for grasping presents several issues with respect to sensing, control, and system configuration. This paper presents some of these issues in concert with the options available to the researcher and the trade-offs to be expected when integrating a vision system with a robotic system for the purpose of grasping objects. The paper includes experimental results from a particular configuration that characterize the type and frequency of errors encountered while performing various vision-guided grasping tasks. These error classes and their frequency of occurrence lend insight into the problems encountered during visual grasping and into the possible solution of these problems.
A novel curve segmentation algorithm for determining control points for deformable-model-based target tracking is proposed. The algorithm is parameterless enabling a fully-fledged automated tracking regardless of the ...
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A novel curve segmentation algorithm for determining control points for deformable-model-based target tracking is proposed. The algorithm is parameterless enabling a fully-fledged automated tracking regardless of the shape of the object being tracked. Compared with other curve segmentation algorithms, it selects a minimal number of control points that yet deliver a superior shape description. The algorithm is comparatively tested with other curve segmentation algorithms in a variety of characteristic target outlines.
One of the major drawbacks of the current neural network generation is the inability to cope with the increase of size/complexity of classification tasks. Modular neural network classifiers attempt to solve this probl...
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One of the major drawbacks of the current neural network generation is the inability to cope with the increase of size/complexity of classification tasks. Modular neural network classifiers attempt to solve this problem through a "divide and conquer" approach. However. The performance of the modular neural network classifiers is sensitive to efficiency of the "task decomposition" technique and the "multi-module decision-making" strategy. After a brief review of previous work with emphasis on five published modular classifiers-decoupled nets, ART-BP, hierarchical network, multiple experts, and multiple identical networks (majority vote and average output decisions)-this paper introduces the cooperative modular neural network (CMNN). The CMNN classifier outperforms the surveyed nets due to its novel task decomposition and multi-module decision-making techniques.
The corners and the middle points, which are extracted as features from the line approximation of a given pattern, are overlaid on a radial grid to form the input array for training a backpropagation network for class...
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The corners and the middle points, which are extracted as features from the line approximation of a given pattern, are overlaid on a radial grid to form the input array for training a backpropagation network for classification. The proposed method is shown to be simple and robust by extensive testing of its performance on patterns both with and without noise.
In this paper, we present a new off-line word recognition system that is able to recognise unconstrained handwritten words from their grey-scale images, and is based on structural information in the handwritten word. ...
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