A COMPUTATIONAL APPROACH TO EDGE-DETECTION
A COMPUTATIONAL APPROACH TO EDGE-DETECTION
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DOI：
10.1109/tpami.1986.4767851
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发表时间：
1986-11-01
影响因子：
23.6
通讯作者：
CANNY, J
中科院分区：
文献类型：
--
作者：
CANNY, J
本文介绍了一种边缘检测的计算方法。该方法的成功取决于定义一套全面的目标计算的边缘点。这些目标必须足够精确，以界定检测器的期望行为，同时对解决方案的形式做出最小的假设。我们定义了一类边缘的检测和定位标准，并提出了这些标准作为算子脉冲响应泛函的数学形式。然后添加第三个标准，以确保检测器对单个边缘只有一个响应。我们使用数值优化中的标准来推导出几种常见图像特征（包括阶梯边缘）的检测器。通过对阶跃边缘的分析，我们发现在检测和定位性能这两个主要目标之间存在一个自然的不确定性原理。根据这一原则，我们得到一个单一的运营商的形状，这是在任何规模的最佳。最佳检测器有一个简单的近似实现，其中边缘标记在高斯平滑图像的梯度幅度的最大值。我们扩展了这个简单的检测器使用运营商的几个宽度，以科普不同的信号-噪声比的图像。我们提出了一种通用的方法，称为功能合成，从运营商在不同尺度的信息从细到粗的整合。最后，我们表明，步骤的边缘检测器的性能大大提高了算子点扩展函数沿沿着边缘。
This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.