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Detection and classification of edges in color images

机译:彩色图像边缘的检测和分类

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摘要

Up to now, most of the color edge detection methods are monochromatic-based techniques, which produce, in general, better than when traditional gray-value techniques are applied. In this overview, we focus mainly on vector-valued techniques because it is easy to understand how to apply common edge detection schemes to every color component. Opposed to this, vector-valued techniques are new and different. The second part of the article addresses the topic of edge classification. While edges are often classified into step edges and ramp edges, we address the topic of physical edge classification based on their origin into shadow edges, reflectance edges, orientation edges, occlusion edges, and specular edges. In the rest of this article we discuss various vector-valued techniques for detecting discontinuities in color images. Then operators are presented based on vector order statistics, followed by presentation by examples of a couple of results of color edge detection. We then discuss different approaches to a physical classification of edges by their origin.
机译:到目前为止,大多数颜色边缘检测方法都是基于单色的技术,通常产生的效果比应用传统灰度值技术时要好。在本概述中,我们主要关注矢量值技术,因为它很容易理解如何将通用的边缘检测方案应用于每个颜色分量。与此相反,矢量值技术是新的和不同的。本文的第二部分讨论了边缘分类的主题。虽然通常将边缘分为阶梯边缘和斜坡边缘,但我们还是根据物理边缘的来源将它们分类为阴影边缘,反射边缘,方向边缘,遮挡边缘和镜面边缘。在本文的其余部分中,我们讨论了用于检测彩色图像中的不连续性的各种矢量值技术。然后,根据矢量顺序统计信息显示算子,然后给出几个颜色边缘检测结果示例的算子。然后,我们讨论根据边缘的起源对边缘进行物理分类的不同方法。

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