Development of New Back-Propagation Morphological Algorithmsand Architectures

近几十年来，数学形态学已经成为图像处理和机器视觉应用中一种日益重要和常用的技术。然而，形态运算的迭代在流水线并行体系结构上的实现存在瓶颈。该项目的目标是提出一组新的形态算子，称为反向传播形态，它不同于传统定义的用于求解耗时迭代问题的形态，并为各种视觉应用开发其基本的定理、算法和体系结构。第一阶段实验表明，反向传播形态具有只需两次扫描，无需多次迭代即可求出信号根的优点，适用于并行结构。预计拟议的研究计划将在新的反向传播形态运算的理论理解方面取得根本性进展。该项目开发的算法和体系结构以及产生的研究成果也将在推动机器视觉检测和识别技术的发展方面对工业具有重要的实用价值。

Mathematical morphology has been becoming an increasingly important and often-used technique in image processing and machine vision applications during recent decades. Nevertheless, the iterations of morphological operations exist a bottleneck in implementation on a pipelined parallel architecture. The objectives of this project are to propose a set of new morphological operators, called back-propagation morphology, which is different from the traditionally defined morphology for solving time-consuming iteration problems, and to develop its underlying theorems, algorithms and architectures for various vision applications. First-stage experiments show that the back-propagation morphology has the advantages of deriving a root of a signal which only requires two scans without numerous iterations and being suited for parallel architectures. It is anticipated that the proposed research program will lead to fundamental advances in the theoretical understanding of the new back-propagation morphological operations. The algorithms and architectures developed and the research findings produced by the project will also have substantial utility for industry in advancing machine vision inspection and recognition technologies.