An approach to machine recognition and understanding of pictures via textural feature extraction is presented. Gradient distribution matrices are used to obtain numerous textural measurements. Based upon Karhunen-Loev...
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An approach to machine recognition and understanding of pictures via textural feature extraction is presented. Gradient distribution matrices are used to obtain numerous textural measurements. Based upon Karhunen-Loeve expansion and normalization, textural features are extracted by computer from these measurements. The textural features serve as the basis for machine recognition and understanding of pictures.
An algorithm to find linear representations of images is developed such that the obtained straight lines are optimal in the Hausdorff metric. This algorithm avoids the computational difficulties associated with direct...
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An algorithm to find linear representations of images is developed such that the obtained straight lines are optimal in the Hausdorff metric. This algorithm avoids the computational difficulties associated with direct application of the definition of the Hausdorff distance between point sets.
This paper discusses a structural patternrecognition methodology which combines some ideas about relation homomorphisms and theory of covers. Features from any arrangement are determined by calculating to which basis...
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This paper discusses a structural patternrecognition methodology which combines some ideas about relation homomorphisms and theory of covers. Features from any arrangement are determined by calculating to which basis arrangements the given arrangement is a homomorphism and calculating which basis arrangements are isomorphic to some part of the given arrangement. A decision rule then decides which class the given arrangement is assigned using the theory of covers. The methodology suggested in the paper provides an alternative to syntactic patternrecognition.
A key element in patternrecognition is the description of shape. The description of shape is facilitated by segmenting the boundary line at so-called critical points - corners (discontinuities in curvature), points o...
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A key element in patternrecognition is the description of shape. The description of shape is facilitated by segmenting the boundary line at so-called critical points - corners (discontinuities in curvature), points of inflection, and curvature maxima. Additional critical points are intersections and points of tangency. Algorithms are described for extracting such critical points in the presence of noise. An illustration is given showing how the critical points may be used in the development of a shape description system.
This paper explores several approaches to outlining point clusters in the plane in a visually acceptable way consistent with capturing shape attributes which may have application in patternrecognition and scene analy...
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This paper explores several approaches to outlining point clusters in the plane in a visually acceptable way consistent with capturing shape attributes which may have application in patternrecognition and scene analysis. Both line and region oriented methods are discussed and the strengths and weaknesses of each explored. General comments are made on the nature of the problem and the duality relationship between line and region representations. A variety of examples are offered for subjective evaluation.
Several methods for estimating a sample-based discriminant's probability of correct classification are compared with respect to bias, variance, and robustness. ″Smooth″ modification of the counting method, or sa...
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Several methods for estimating a sample-based discriminant's probability of correct classification are compared with respect to bias, variance, and robustness. ″Smooth″ modification of the counting method, or sample success proportion, is recommended to reduce bias while retaining stability and robustness. In contrast, the popular ″leave-one-out″ technique is a counting method whose bias reduction is vitiated by corresponding increase in variance.
New and useful results on multistage multiclass statistical classification and on model-driven, data-directed structural pattern analysis have been obtained by developing and exploiting connections between state-space...
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New and useful results on multistage multiclass statistical classification and on model-driven, data-directed structural pattern analysis have been obtained by developing and exploiting connections between state-space and AND/OR graph models, formal grammars, heuristic search and probabilistic decision making. This paper highlights the concepts and connections which have led to these results.
Performance of Hough-like transforms in the presence of inaccurate feature point estimates is examined comparing predicted and simulated results. Cases where there are several curve families are treated when correct a...
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Performance of Hough-like transforms in the presence of inaccurate feature point estimates is examined comparing predicted and simulated results. Cases where there are several curve families are treated when correct and incorrect transforms are applied. Results are given for texture inputs to the transforms. Scatter diagrams in the transformed space are given.
This paper presents a simple algorithm to detect and label homogeneous areas in an image without sequential region growing or edge boundary tracing. The scheme constructs directed trees with the image points as nodes,...
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This paper presents a simple algorithm to detect and label homogeneous areas in an image without sequential region growing or edge boundary tracing. The scheme constructs directed trees with the image points as nodes, guided by an edge value computed at every point. These directed trees segment the image into disjoint regions. The properties of the resultant segments are stated in terms of the edge image. The algorithm is shown to be simple, efficient and effective for detecting homogeneous segments in the presence of noise. Results of application of the algorithm to segment a LANDSAT multispectral scene of an agricultural area are included.
The approach to contour extraction described here formulates the contour extraction problem as one of minimum cost tree searching. Branch costs or metrics are defined which are indicative of the likelihood that a part...
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The approach to contour extraction described here formulates the contour extraction problem as one of minimum cost tree searching. Branch costs or metrics are defined which are indicative of the likelihood that a particular branch lies on the true contour. The branch metrics incorporate both local and global or contextual information. The most likely path or contour is then extracted by application of a heuristic tree searching algorithm. The approach has been successfully applied to a number of biomedical imageprocessing problems.
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