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 i...
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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.
Most data sets that describe and evolve from real-world systems are by nature semiquantitative or qualitative rather than quantitative. This can mean large variations in the significance of results that are derived fr...
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Most data sets that describe and evolve from real-world systems are by nature semiquantitative or qualitative rather than quantitative. This can mean large variations in the significance of results that are derived from this data for decision-making processes given that the original database provides training and prototypical examples that reflect systems of events in the real world. In this article we propose a structure for a Knowledge-Based System (KBS) that is derived using significance within given contextual domains. Data that would ordinarily be classified by simple attribute classification techniques are now categorized by understanding patterns and value distributions for attributes and attribute domains that exist within rich and dense databases such as in the case of census databases double dagger and Geographic Information Systems (GIS);rich by the very number of fields and interpretations, depending on the context in which the data are to be reviewed. The structure we have implemented for capturing and structuring semiquantitative information is the Fuzzy Cognitive Map (FCM). We also reduce the number of false patterns labeled ''significant'' by incorporating the knowledge used by human experts to find significance within the data. We treat this knowledge as initial background knowledge and as a minimal set for distinguishing significance for particular attribute values within a given context. (C) 1996 John Wiley & Sons, Inc.
The authors describe their system for writer independent, off-line unconstrained handwritten word recognition. First, they present a new method to automatically determine the parameters of Gabor filters to extract fea...
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ISBN:
(纸本)0780336674
The authors describe their system for writer independent, off-line unconstrained handwritten word recognition. First, they present a new method to automatically determine the parameters of Gabor filters to extract features from slant and tilt corrected images. An algorithm is also developed to translate 2D images to 1D domain. Finally, they propose a modified dynamic programming method with fuzzy inference to recognize words. Their initial experiments have shown encouraging results.
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.
In this paper, we propose a system for vision guided autonomous circumnavigation, allowing robots to navigate around objects of arbitrary pose. The system performs knowledge-based object recognition from an intensity ...
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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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An innovative and powerful method is proposed for measuring physical parameters of lines using the responses from a bank of Gabor (1946) filters. These measurements are made without resorting to an image ruler. First ...
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An innovative and powerful method is proposed for measuring physical parameters of lines using the responses from a bank of Gabor (1946) filters. These measurements are made without resorting to an image ruler. First the system is calibrated by establishing a relationship between the frequency of the Gabor filter and line length, then the length and angle of of isolated lines can be measured. A constraint on this method is that the lines in the scene need to be separated and isolated by a minimum distance. Results indicate that Gabor filters can be successfully applied to the measurement of geometric properties of objects, especially where Gabor filters are already being used for processing tasks. The best accuracies in terms of measurement error for the line length and angle measurements were 0.81% and 0.0% respectively.
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 and relational information in the handwritten word. ...
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An optimal learning scheme is proposed for a class of bidirectional associative memories (BAMs). This scheme, based on the perceptron learning algorithm, is motivated by the inadequacies/incompleteness of the weighted...
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An optimal learning scheme is proposed for a class of bidirectional associative memories (BAMs). This scheme, based on the perceptron learning algorithm, is motivated by the inadequacies/incompleteness of the weighted learning by global optimization, as derived by Wang et al. (1993). It is shown that the new scheme has superior properties: (1) Convergence to the correct solution, when it exists; and (2) A larger basin of attraction for the given set of patterns.
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