27 research outputs found

    Graph-based features for texture discrimination

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    Graph-based features for texture discrimination

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    Improved Contour Detection by Non-Classical Receptive Field Inhibition

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    Improved Contour Detection by Non-Classical Receptive Field Inhibition

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    Improved Contour Detection by Non-Classical Receptive Field Inhibition

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    Contour detection and shape recognition in image analysis

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    De onderwerpen behandeld in deze dissertatie kunnen het best worden weergegeven door wat Leonard Da Vinci gekarakteriseerd heeft als een inleidende vorm van artistieke uitdrukking: ''Het eerste beeld bestond uit een omtreklijn die de schaduw van een man omsingeld zoals de zon het op een wand heeft afgebeeld''. Hoewel over de artistieke eigenschappen van deze kunstvorm zullen veel critice in debat treden, wordt een discussie daarover aan het overgelaten. ... Zie: Samenvatting.

    Graph-based features for texture discrimination

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    Graph-based features, such as the number of connected components, edges of a given orientation and vertices per unit area, and the number of vertices and pixels per connected component, are proposed for the analysis of textures which consist of structural elements. The proposed set of features is compared with features obtained by a typical filter-based scheme which makes use of Gabor filters. The discrimination properties of the two types of features are assessed by evaluating the separability of sets of feature vectors which are derived from different types of texture using the Mahalanobis distance. The graph-based features are shown to be superior to the filter-based features for the class of concerned textures. They are particularly suited for discrimination between textures which have the same spatial and orientation regularity but consist of elements of different form.
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