{"title":"PM4—A reconfigurable multiprocessor system for pattern recognition and image processing","authors":"F. Briggs, K. Fu, K. Hwang, J. Patel","doi":"10.1109/MARK.1979.8817082","DOIUrl":null,"url":null,"abstract":"Pictorial information is often described by digitized arrays, syntactic (and semantic) strings and high-dimensional trees or graphs. The analysis and extraction of meaningful information for pictorial patterns by digital computers is called pictorial pattern analysis. Pattern analysis tasks require a wide variety of processing techniques and mathematical tools. In most machine intelligence systems, large computers are employed to process pictorial information. Because most image processing tasks require only repetitive Boolean operations or simple arithmetic operations defined over extremely large arrays of picture elements (pixels), 1 the use of large computers with rigidly structured sequential or parallel processors may result in intolerable waste of resources. 2 For example, the array-structured ILLIAC IV 3 and STARAN 4 are efficient for processing fixed-length vectors, but are very inefficient for mixed scalar and vector operations, due to the fact that multiple instruction streams do not exist simultaneously in these supercomputers.","PeriodicalId":341008,"journal":{"name":"1979 International Workshop on Managing Requirements Knowledge (MARK)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1979-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1979 International Workshop on Managing Requirements Knowledge (MARK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MARK.1979.8817082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38
Abstract
Pictorial information is often described by digitized arrays, syntactic (and semantic) strings and high-dimensional trees or graphs. The analysis and extraction of meaningful information for pictorial patterns by digital computers is called pictorial pattern analysis. Pattern analysis tasks require a wide variety of processing techniques and mathematical tools. In most machine intelligence systems, large computers are employed to process pictorial information. Because most image processing tasks require only repetitive Boolean operations or simple arithmetic operations defined over extremely large arrays of picture elements (pixels), 1 the use of large computers with rigidly structured sequential or parallel processors may result in intolerable waste of resources. 2 For example, the array-structured ILLIAC IV 3 and STARAN 4 are efficient for processing fixed-length vectors, but are very inefficient for mixed scalar and vector operations, due to the fact that multiple instruction streams do not exist simultaneously in these supercomputers.
图形信息通常由数字化数组、语法(和语义)字符串和高维树或图来描述。利用数字计算机对图形图形进行分析和提取有意义的信息称为图形图形分析。模式分析任务需要各种各样的处理技术和数学工具。在大多数机器智能系统中,大型计算机被用来处理图像信息。因为大多数图像处理任务只需要重复的布尔运算或在极大的图像元素(像素)数组上定义的简单算术运算,所以使用具有严格结构的顺序或并行处理器的大型计算机可能会导致无法忍受的资源浪费。2例如,数组结构的ILLIAC IV 3和STARAN 4对于处理固定长度的向量是有效的,但是对于混合标量和向量操作是非常低效的,因为在这些超级计算机中不存在多个指令流同时存在。