Topical Meeting On Signal Recovery and Synthesis II最新文献_第4页

Sufficient Support Information to Ensure a Unique Solution to the Phase Problem 足够的支持信息，以确保阶段问题的唯一解决方案

Topical Meeting On Signal Recovery and Synthesis II Pub Date : 1900-01-01 DOI: 10.1364/srs.1986.thc2

B. Brames

引用次数: 0

An Iterative Deconvolution Algorithm with Exponential Convergence 一种指数收敛的迭代反卷积算法

Topical Meeting On Signal Recovery and Synthesis II Pub Date : 1900-01-01 DOI: 10.1364/srs.1986.fb2

C. E. Morris, M. A. Richards, M. Hayes

引用次数: 1

Reconstruction of Axisymmetric Objects from One Silhouette 从一个剪影重建轴对称物体

Topical Meeting On Signal Recovery and Synthesis II Pub Date : 1900-01-01 DOI: 10.1364/srs.1986.thd3

Patrick Van Hove

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引用次数: 1

Recognition of 3-D Objects from Partial View Data 基于局部视图数据的三维物体识别

Topical Meeting On Signal Recovery and Synthesis II Pub Date : 1900-01-01 DOI: 10.1364/srs.1986.thd2

Ronald Wu, H. Stark

{"title":"Recognition of 3-D Objects from Partial View Data","authors":"Ronald Wu, H. Stark","doi":"10.1364/srs.1986.thd2","DOIUrl":"https://doi.org/10.1364/srs.1986.thd2","url":null,"abstract":"We consider a problem that occurs in machine vision research, namely, how to determine the class membership of an object when only partial view data about it is available. For example, suppose that a machine vision system must determine whether a physical (i.e., a three-dimensional) object belongs to class A or class B and only 90 degree view data is available. How should the data be organized into a useful feature vector? What rule should be used to determine class—membership? One way to proceed is via image recovery i.e., the restoration of missing view data using any of the several extrapolation algorithms known to the image recovery community. However if only class membership is desired, is image recovery really needed? First we note that if the partial view data is insufficient i.e, it contains no information that is not common to both classes, then image recovery will not work (now for that matter will anything else!). Second, assuming that the data is sufficient i.e., that it contains at least some data characteristic of only one class, image recovery may not be the answer for two reasons: 1) the recovery process is most-often ill-posed, thereby introducing possibly too much noise for subsequent discrimination; and 2) regularization may induce sufficient smoothing to destroy what little class-differentiability may exist in the partial data. Thus we concentrate on information recovery rather than image recovery. We are therefore led to the following problem statement: given partial view data of a 3-D object known to belong to one of M well-defined classes, what operation (linear or non-linear) will enhance the discriminating value of the data? The algorithm we have derived is based on the ful1-view algorithm described below.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115399493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Use of Singular Systems in Inverse Problems in Signal Processing 奇异系统在信号处理反问题中的应用

Topical Meeting On Signal Recovery and Synthesis II Pub Date : 1900-01-01 DOI: 10.1364/srs.1986.fa2

E. Pike, M. Bertero, C. De Mol

引用次数: 0