MEMBER, IEEE, Division of Engineering, Brown University, Providence, RI 02912.
IEEE Trans Pattern Anal Mach Intell. 1983 Mar;5(3):299-316. doi: 10.1109/tpami.1983.4767392.
The problem considered in this paper is the estimation of highly variable object boundaries in noisy images. Boundaries may be those of a tank in an IR image, a spinal canal in a CAT scan, a cloud in a visible light image, etc. Or they may be internal to an object such as the boundary between a spherical surface and a cylindrical surface in a manufactured object. The focus of the paper is on parallel multiple-window boundary estimation algorithms. Here the image field is parti-tioned into an array of rectangular windows, and boundary finders are run simultaneously within the windows. The boundary segments found within the windows are then seamed together to obtain meaningful global boundaries. The entire procedure is treated within a maximum likelihood estimation framework that we have developed for boundary finding. Although our multiple-window estimation approach can be used with a number of local boundary finding algorithms, we concen-trate on one which is based on dynamic programming and will produce the true maximum likelihood boundary. Some theoretical considera-tions for boundary model design and boundary-finding runtime are covered. Included is the use of a low computational cost F-test for test-ing whether a window contains a boundary, and an analytical treatment which shows that use of coarse pixels with a chi-square test or an F-test improves the probability of correctly recognizing whether a boundary is present in a window.
本文研究的问题是在噪声图像中估计高度变化的目标边界。边界可以是红外图像中的坦克、CAT 扫描中的椎管、可见光图像中的云等，也可以是物体内部的边界，如制造物体中球形表面和圆柱形表面之间的边界。本文的重点是并行多窗口边界估计算法。在此，图像域被划分为矩形窗口的数组，并在窗口内同时运行边界查找器。然后将在窗口内找到的边界段拼接在一起，以获得有意义的全局边界。整个过程是在我们为边界查找开发的最大似然估计框架内进行处理的。虽然我们的多窗口估计方法可以与许多局部边界查找算法一起使用，但我们专注于一种基于动态规划的算法，该算法将生成真实的最大似然边界。涵盖了一些边界模型设计和边界查找运行时的理论考虑因素。其中包括使用计算成本低的 F 检验来测试窗口是否包含边界，以及一种分析处理方法，该方法表明使用具有卡方检验或 F 检验的粗像素可以提高正确识别窗口中是否存在边界的概率。
