{"title":"Computer Vision and Image Processing: 4th International Conference, CVIP 2019, Jaipur, India, September 27–29, 2019, Revised Selected Papers, Part I","authors":"Nathan Sprague, In Numpy","doi":"10.1007/978-981-15-4015-8","DOIUrl":"https://doi.org/10.1007/978-981-15-4015-8","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84273700","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}
{"title":"Introduction to Computer Vision and Basic Concepts of Image Formation","authors":"M. Bhuyan","doi":"10.1201/9781351248396-1","DOIUrl":"https://doi.org/10.1201/9781351248396-1","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78319899","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}
Behrooz Kamgar-Parsi , Behzad Kamgar-Parsi , Harry Wechsler
{"title":"Simultaneous fitting of several planes to point sets using neural networks","authors":"Behrooz Kamgar-Parsi , Behzad Kamgar-Parsi , Harry Wechsler","doi":"10.1016/0734-189X(90)90080-F","DOIUrl":"10.1016/0734-189X(90)90080-F","url":null,"abstract":"<div><p>It is a simple problem to fit one line to a collection of points in the plane. But when the problem is generalized to two or more lines then the problem complexity becomes exponential in the number of points because we must decide on a partitioning of the points among the lines they are to fit. The same is true for fitting lines to points in three-dimensional space or hyperplanes to data points of high dimensions. We show that this problem despite its exponential complexity can be formulated as an optimization problem for which very good, but not necessarily optimal, solutions can be found by using an artificial neural network. Furthermore, we show that given a tolerance one can determine the <em>number</em> of lines (or planes) that should be fitted to a given point configuration. This problem is prototypical of a class of problems in computer vision, pattern recognition, and data fitting. For example, the method we propose can be used in reconstructing a planar world from range data or in recognizing point patterns in an image.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 341-359"},"PeriodicalIF":0.0,"publicationDate":"1990-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90080-F","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877990","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}
{"title":"Index-based object recognition in pictorial data management","authors":"William I Grosky , Rajiv Mehrotra","doi":"10.1016/0734-189X(90)90085-A","DOIUrl":"10.1016/0734-189X(90)90085-A","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 416-436"},"PeriodicalIF":0.0,"publicationDate":"1990-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90085-A","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132845938","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}
{"title":"Author index for volume 52","authors":"","doi":"10.1016/0734-189X(90)90088-D","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90088-D","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Page 460"},"PeriodicalIF":0.0,"publicationDate":"1990-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90088-D","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91632461","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}
Douglas J Hunt , Loren W Nolte, Amy R Reibman , W Howard Ruedger
{"title":"Hough transform and signal detection theory performance for images with additive noise","authors":"Douglas J Hunt , Loren W Nolte, Amy R Reibman , W Howard Ruedger","doi":"10.1016/0734-189X(90)90082-7","DOIUrl":"10.1016/0734-189X(90)90082-7","url":null,"abstract":"<div><p>The line detection performance and sensitivity to the noise distribution of the Hough transform and two signal detection theory processors are evaluated quantitatively (using receiver operating characteristics (ROC)) and compared for images corrupted by each of several types of additive noise. The types of noise distributions considered are Gaussian, uniform, and Laplacian. The two types of signal detection theory processors considered are the optimal detector for additive, Gaussian noise and the optimal detector for additive, Laplacian noise. The performances for these noise distributions are interesting to compare because they vary widely in the thickness of the tails of their probability density functions. The Gaussian processor and the Hough transform are found to be much less sensitive to noise type than the Laplacian processor.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 386-401"},"PeriodicalIF":0.0,"publicationDate":"1990-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90082-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116184108","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}