{"title":"A comparative performance study of several global thresholding techniques for segmentation","authors":"Sang Uk Lee, Seok Yoon Chung, Rae Hong Park","doi":"10.1016/0734-189X(90)90036-U","DOIUrl":"10.1016/0734-189X(90)90036-U","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 2","pages":"Page 218"},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90036-U","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90181288","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":"k × k thinning","authors":"Lawrence O'Gorman","doi":"10.1016/0734-189X(90)90030-Y","DOIUrl":"10.1016/0734-189X(90)90030-Y","url":null,"abstract":"<div><p>A commonly used method for thinning regions in binary images consists of examining windows of 3 × 3 pixels throughout an image, and erasing the center pixel if the thinning criteria are met. The<em>k × k</em> thinning method is a generalization of the 3 × 3 method, where<em>k × k</em> sized windows are examined and a center core of<em>(k − 2) × (k − 2)</em> pixels is erased if the criteria are met. The advantage of<em>k × k</em> thinning is that, by peeling thicker layers from the boundaries of image regions, fewer iterations are required to reach the thinned result. For larger<em>k</em>, this is often at the cost of an increase in the coarseness of the result. Criteria are given by which the<em>k × k</em> method thins to minimally 8-connected lines while retaining connectivity and endpoints. Sequential and parallel algorithms are given. A procedure to obtain line widths in the course of thinning is described. Examples are shown illustrating the reduction in iterations with increase of<em>k</em>, and the trade-off between size of<em>k</em> and the coarseness of the result. Because of the highly repetitive, and local operations of the algorithm, it is straightforwardly mapped into VLSI hardware, and an example of this is given.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 2","pages":"Pages 195-215"},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90030-Y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125827463","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}
Tony P. Pridmore, John E.W. Mayhew, John P. Frisby
{"title":"Exploiting image-plane data in the interpretation of edge-based binocular disparity","authors":"Tony P. Pridmore, John E.W. Mayhew, John P. Frisby","doi":"10.1016/0734-189X(90)90034-S","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90034-S","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 2","pages":"Pages 217-218"},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90034-S","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137435598","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":"Representation and recognition of surface shapes in range images: A differential geometry approach","authors":"Ping Liang, John S. Todhunter","doi":"10.1016/0734-189X(90)90032-Q","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90032-Q","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 2","pages":"Page 217"},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90032-Q","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136864587","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":"Dynamic strip algorithm in curve fitting","authors":"Maylor K. Leung, Yee-Hong Yang","doi":"10.1016/0734-189X(90)90028-T","DOIUrl":"10.1016/0734-189X(90)90028-T","url":null,"abstract":"<div><p>In this paper, a new technique for fitting a curve with lines employing strips is presented. An interesting feature of the proposed algorithm is its ability to dynamically adjust the direction of the strip to increase the number of contour points that can be enclosed within the strip. This translates to minimizing the number of the generated line segments with little added cost.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 2","pages":"Pages 146-165"},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90028-T","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121892061","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":"Automatic fixing of ship position by simulation-and-matching","authors":"Nobumichi Ishimura, Takeshi Hashimoto, Shuichi Tsujimoto, Suguru Arimoto","doi":"10.1016/S0734-189X(05)80061-1","DOIUrl":"10.1016/S0734-189X(05)80061-1","url":null,"abstract":"<div><p>In this paper, we propose an automatic method, named simulation-and-matching, for fixing ship position both from a radar echo image and from a synthetic echo image which is synthesized from three-dimensional topographic data, and we show that simulation-and-matching is a more accurate and robust method than those methods that are based on coastline matching. For bulky three-dimensional data, data compression by spline approximation is also considered.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 1","pages":"Pages 38-53"},"PeriodicalIF":0.0,"publicationDate":"1990-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0734-189X(05)80061-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131250560","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":"Direct construction of the perspective projection aspect graph of convex polyhedra","authors":"John H. Stewman, Kevin W. Bowyer","doi":"10.1016/S0734-189X(05)80060-X","DOIUrl":"10.1016/S0734-189X(05)80060-X","url":null,"abstract":"<div><p>The <em>aspect graph</em> concept was first described by Koenderink and van Doorn as a possible mechanism in human vision and has subsequently become an active research topic in computer vision. This paper describes an algorithm for constructing the <em>perspective projection aspect graph</em> of convex polyhedra. In the perspective projection aspect graph, viewpoint space is modeled as all of 3D space surrounding the object. This makes the perspective projection aspect graph a more realistic representation than the <em>orthographic projection aspect graph</em>, in which viewpoint space is modeled by the Gaussian sphere. The algorithm uses an intermediate data structure which represents a complete <em>parcellation</em> of 3D space derived from the geometric definition of the object. All information necessary for identifying object <em>aspects</em> and corresponding <em>viewing cells</em> is obtained as a result of this parcellation. The resulting aspect graph structure has a node for each distinct aspect/viewing cell. The upper bounds on the time complexity of the algorithm and the space complexity of the resulting data structure are Θ(<em>N</em><sup>4</sup>), where <em>N</em> is the number of faces of the polyhedron. The algorithm has been implemented in C, runs on a SUN workstation, and can use PADL-2 files for its input description of objects.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 1","pages":"Pages 20-37"},"PeriodicalIF":0.0,"publicationDate":"1990-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0734-189X(05)80060-X","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114812802","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":"Texture descriptors based on co-occurrence matrices","authors":"Calvin C. Gotlieb, Herbert E. Kreyszig","doi":"10.1016/S0734-189X(05)80063-5","DOIUrl":"10.1016/S0734-189X(05)80063-5","url":null,"abstract":"<div><p>This paper focuses on the problem of texture classification using statistical descriptors based on the co-occurrence matrices. A major part of the paper is dedicated to the derivation of a general model for analysis and interpretation of experimental results in texture analysis when individual and groups of classifiers are being used, and a technique for evaluating their performance. Using six representative classifiers; that is, <em>second angular moment f1, contrast f2, inverse difference moment f5, entropy f9</em>, and <em>information measures of correlation I and II, f12 and f13</em>, we give a systematic study of the discrimination power of all 63 combination of these classifiers on 13 samples of Brodatz textures. The conclusion that can be drawn from our study is that it is useful to combine classifiers up to a certain order. Here it turned out that groups of four classifiers are optimal.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 1","pages":"Pages 70-86"},"PeriodicalIF":0.0,"publicationDate":"1990-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0734-189X(05)80063-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115430241","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}