An Area-Based Measure of Directional Convexity for Grayscale Images

被引：0

作者：

Bodnar, Peter ^{[1
]}

Balazs, Peter ^{[1
]}

机构：

[1] Univ Szeged, Dept Image Proc & Comp Graph, Arpad Ter 2, H-6720 Szeged, Hungary

来源：

VIPIMAGE 2017 | 2018年 / 27卷

关键词：

Shape descriptor; Grayscale image; Directional convexity; hv-convexity; Convexity measure; RECONSTRUCTION; PROJECTIONS; POLYOMINOES; POLYGONS; HULL; 2D;

D O I：

10.1007/978-3-319-68195-5_14

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Convexity is a well-known shape descriptor that can be used for various applications of digital image processing. The concept of directional convexity of binary images has been recently studied in the field of binary tomography and image segmentation. It has been also investigated how the amount of convexity can be measured in the binary case. In this paper we present two area-based directional convexity measures for grayscale images, and study their properties (like, e.g., rotation and scale invariance) on artificial and real-life images. We also compare rankings of images provided by both measures. The aim is to reveal the advances and drawbacks of the measures affecting the quality of image processing issues they are used for.

引用

页码：131 / 139

页数：9

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