增强曲率尺度空间特征,实现鲁棒形状分类

S. Kopf, T. Haenselmann, W. Effelsberg
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引用次数: 30

摘要

曲率尺度空间(CSS)技术是描述复杂形状的一种鲁棒方法,也是MPEG-7标准的一部分。其核心思想是分析形状的曲率,并从拐点导出特征。CSS方法的一个主要缺点是它对凸段的表示很差:由于缺少拐点,凸对象根本无法表示。我们扩展了CSS方法来为形状的凹段和凸段生成特征点。这种通用方法适用于任意对象。在实验结果中,我们对图像和视频中字符的自动识别进行了综合评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing curvature scale space features for robust shape classification
The curvature scale space (CSS) technique, which is also part of the MPEG-7 standard, is a robust method to describe complex shapes. The central idea is to analyze the curvature of a shape and derive features from inflection points. A major drawback of the CSS method is its poor representation of convex segments: Convex objects cannot be represented at all due to missing inflection points. We have extended the CSS approach to generate feature points for concave and convex segments of a shape. This generic approach is applicable to arbitrary objects. In the experimental results, we evaluate as a comprehensive example the automatic recognition of characters in images and videos.
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