A Graph Representation for Silhouette Based on Multiscale Analysis

被引：0

作者：

Zhang, MingMing ^{[1
]}

Omachi, Shinichiro ^{[1
]}

Aso, Hirotomo ^{[1
]}

机构：

[1] Tohoku Univ, Grad Sch Engn, Sendai, Miyagi 9808579, Japan

来源：

PROCEEDINGS OF THE 2009 CHINESE CONFERENCE ON PATTERN RECOGNITION AND THE FIRST CJK JOINT WORKSHOP ON PATTERN RECOGNITION, VOLS 1 AND 2 | 2009年

关键词：

silhouette image; graph recognition; graph representation; medial axis; multiscale analysis; Fourier Descriptor;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Graph descriptor is usually focused on in computer vision for its flexibility and richness. However, in object recognition, it is difficult to catch the feature of an object completely with a straightforward way by a graph. In this paper, from a multiscale viewpoint, we propose a method to construct a vertex-labeled graph for image recognition where the label represents the importance of a vertex to the graph. By using the Fourier Descriptor, when we adjust the cut-off frequency we can select how much detailed feature can be used to represent a contour. In this process, we found the medial axis of shape also evolves from a simple graph to a detailed graph. Focusing on this evolution, for each vertex in the graph we assign a label representing its importance in this graph.

引用

页码：837 / 839

页数：3

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