A New Fast Edge Detection Algorithm Based on NAM-structured Plane Decomposition

The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image representation because of its ability to focus on the interesting subsets of an image. In this paper, we develop a new method for gray-scale image representation based on NAM, called NAM-structured plane decomposition （NAMPD）, in which each subpattern is associated with a rectangular region in the image. The luminance function of pixels in this region is approximated by an oblique plane model. Then, we propose a new and fast edge detection algorithm based on NAMPD. The theoretical analyses and experimental results presented in this paper show that the edge detection algorithm using NAMPD performs faster than the classical ones because it permits the execution of operations on subpatterns instead of pixels.

非，对称和反收拾行李模式表示当模特儿(NAM ) ，由收拾行李的问题的概念启发了，使用一套子模式代表一个原来的模式。因为它集中于一幅图象的有趣的子集的能力， NAM 是为图象表示的一个有希望的方法。在这篇论文，我们基于 NAM 为灰阶的图象表示开发一个新方法，叫的组织 NAM 的飞机分解(NAMPD ) ，在哪个每个子模式在图象与一个矩形的区域被联系。在这个区域的象素的发光性函数被一个倾斜的飞机模型接近。然后，我们基于 NAMPD 建议一个新、快的边察觉算法。因为它在子模式上允许操作的实行而不是象素，在这篇论文介绍的理论分析和试验性的结果证明用 NAMPD 的边察觉算法比古典的快表现。