Features of human bones are useful to establish correlation between bone structure and age, and information about age-related bone diseases. This paper presents a new approach to quantitative analysis of cross section...
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Features of human bones are useful to establish correlation between bone structure and age, and information about age-related bone diseases. This paper presents a new approach to quantitative analysis of cross sections of human bones using image processing techniques. The system uses the adaptive neighborhood algorithm, clustering, local covariance measures, and the fuzzy region growing algorithm. The system extracts various bone features with consistency and provides more reliable statistics. As a result, the authors are able to correlate bone features with age and possibly with age related bone diseases such as osteoporosis.
Traditional curvature measures for modeling 3-D surface classify surfaces into crisp sets based on the sign of the mean and Gaussian curvatures. However, descriptions based on such measures do not represent the intuit...
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Traditional curvature measures for modeling 3-D surface classify surfaces into crisp sets based on the sign of the mean and Gaussian curvatures. However, descriptions based on such measures do not represent the intuitive descriptions in a natural way, i.e. the degree to which the segment belongs to each of the surface types in the crisp set. In addition, curvature estimates are extremely sensitive to noise due to the computation of directional derivatives, which makes classification more difficult. There exists a certain level of uncertainty/ambiguity that is not taken into account while classifying the surfaces based on the existing methods. In this paper, a novel fuzzy surface description technique, that emulates the natural description of surfaces, is proposed and demonstrated on a class of range images.
In this paper, we describe a case-based system using fuzzy logic type neural networks for diagnosing electronic systems. We present a brief derivation of OR and AND neurons and the architecture of our system. To illus...
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In this paper, we describe a case-based system using fuzzy logic type neural networks for diagnosing electronic systems. We present a brief derivation of OR and AND neurons and the architecture of our system. To illustrate the effectiveness of the proposed system, we show experimental results on real data from call logs collected at the technical support centre in Ericsson Australia.
This paper discusses a technique that effectively combines conceptual graph theory with fuzzy logic for 3D object recognition. The suitability of fuzzy conceptual graphs for learning continuous valued features is brou...
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This paper discusses a technique that effectively combines conceptual graph theory with fuzzy logic for 3D object recognition. The suitability of fuzzy conceptual graphs for learning continuous valued features is brought out with a machinevision system that employs model-based object recognition. The object recognition system learns instances of segmented range objects from different views, represented in terms of fuzzy conceptual graphs as a memory aggregate. Recognition is done by deriving a conceptual graph for a query and performing graph matching. The technique is found suitable for continuous valued concepts and hence used as with a powerful capability for learning.< >
Proposes a simple and powerful approach for texture classification using the eigenfeatures of local covariance measures. A texton encoder produces a texture code which is invariant to local and global textural rotatio...
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Proposes a simple and powerful approach for texture classification using the eigenfeatures of local covariance measures. A texton encoder produces a texture code which is invariant to local and global textural rotatio...
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Proposes a simple and powerful approach for texture classification using the eigenfeatures of local covariance measures. A texton encoder produces a texture code which is invariant to local and global textural rotations. The proposed method uses six statistical features obtained from two scales of this invariant encoder to result in indices for roughness, anisotropy, and other higher-order textural features. Classification results for synthetic and natural textures are presented. The authors also discuss the effect of window sizes used at local and global scales on the performance of the classifier.< >
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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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. We use Gabor filters to extract features from the words, and then use an evidence-based approach for word classification. A solution to the Gabor filter parameter estimation problem is given, enabling the Gabor filter to be automatically tuned to the word image properties. Our experiments show that the proposed method achieves reasonably high recognition rates compared to standard classification methods.< >
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