Development of New Back-Propagation Morphological Algorithmsand Architectures

新的反向传播形态学算法和架构的开发

• 批准号: 9109138
• 负责人: Frank Shih
• 金额: $ 6.37万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-01 至 1993-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9109138&HistoricalAwards=false
• 关键词: Development; New; Back; Propagation; Morphological

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.

数学形态学已经成为一个越来越重要的 图像处理中重要和常用技术， 机器视觉在近几十年的应用。 然而，尽管如此， 形态学运算的迭代存在瓶颈， 在流水线并行架构上实现。 的 该项目的目标是提出一套新的 形态学算子，称为反向传播形态学， 其不同于传统定义的形态， 解决耗时的迭代问题，并开发其 基础定理，算法和各种架构 视觉应用 第一阶段的实验表明，反向传播 形态学具有导出信号的根的优点 其仅需要两次扫描而无需多次迭代， 适合并行架构。 预计各国 拟议的研究计划将导致根本性的进展， 在理论上理解新的反向传播 形态学运算 算法和架构 该项目的研究成果将 在推进机器的工业上也有很大的用途 视觉检测和识别技术。