Edge detection is one of the most fundamental operations in the field of image analysis and computer vision as a critical preprocessing step for high-level tasks. It is difficult to give a generic threshold that works well on all images as the image contents are totally different. This paper presents an adaptive, robust and effective edge detector for real-time applications. According to the 2D entropy, the images can be clarified into three groups, each attached with a reference percentage value based on the edge proportion statistics. Compared with the attached points along the gradient direction, anchor points were extracted with high probability to be edge pixels. Taking the segment direction into account, these points were then jointed into different edge segments, each of which was a clean, contiguous, 1-pixel wide chain of pixels. Experimental results indicate that the proposed edge detector outperforms the traditional edge following methods in terms of detection accuracy. Besides, the detection results can be used as the input information for post-processing applications in real-time.

中文翻译：

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基于边缘比例统计的自适应鲁棒边缘检测方法

边缘检测是图像分析和计算机视觉领域中最基本的操作之一，它是高级任务的关键预处理步骤。由于图像内容完全不同，因此很难给出适用于所有图像的通用阈值。本文提出了一种适用于实时应用的自适应，鲁棒和有效的边缘检测器。根据2D熵，可以将图像分为三组，每组基于边缘比例统计信息附加一个参考百分比值。与沿着梯度方向的附着点相比，锚点极有可能被提取为边缘像素。考虑到片段方向，然后将这些点合并为不同的边缘片段，每个边缘片段都是干净，连续的1像素宽的像素链。实验结果表明，提出的边缘检测器在检测精度上优于传统的边缘跟踪方法。此外，检测结果可以用作实时后处理应用程序的输入信息。