MEMBER, IEEE, Department of Electrical Engineering, Colorado State University, Fort Collins, CO 80523.
IEEE Trans Pattern Anal Mach Intell. 1982 Feb;4(2):167-82. doi: 10.1109/tpami.1982.4767224.
This paper presents a suboptimal boundary estimation algorithm for noisy images which is based upon an optimal maximum likelihood problem formulation. Both the maximum likelihood formulation and the resulting algorithm are described in detail, and computational results are given. In addition, the potential power of the likelihood formulation is demonstrated through the presentation of three simple but insightful analyses of algorithm performance. These analyses are based on a technique we have developed for comparing the accuracies of different boundary finding algorithms. This technique also helps in understanding the interplay of object shape and data models in the relative performances of boundary finders. Some of the algorithm design considerations resulting from the use of our analysis technique are new and, at first, surprising. Our technique appears to be the only one developed for comparing the accuracies of different boundary finding algorithms.
本文提出了一种基于最优极大似然问题公式的噪声图像次最优边界估计算法。详细描述了极大似然公式和由此产生的算法，并给出了计算结果。此外，还通过对算法性能的三个简单而有见地的分析，展示了似然公式的潜在能力。这些分析基于我们开发的一种用于比较不同边界发现算法准确性的技术。该技术还有助于理解在边界发现器的相对性能中对象形状和数据模型的相互作用。从使用我们的分析技术中得出的一些算法设计考虑因素是新的，而且起初令人惊讶。我们的技术似乎是唯一用于比较不同边界发现算法准确性的技术。
