{"title":"Calibration of a stereo system with small relative angles","authors":"Behrooz Kamgar-Parsi , Roger D. Eastman","doi":"10.1016/S0734-189X(05)80059-3","DOIUrl":"https://doi.org/10.1016/S0734-189X(05)80059-3","url":null,"abstract":"<div><p>Because of their relative ease in solving the correspondence problem, stereo systems without relative rotation are popular. However, in practice, mechanical difficulties will lead to a small, unknown relative rotation between stereo cameras. In this paper we present an algorithm for the calibration of a stereo system with small relative angles in an uncontrolled environment. This algorithm has two advantages: (a) It is more accurate than the existing algorithms in the computer vision and photogrammetry literatures (b) It provides useful insight into the problem of camera calibration and relative orientation. This is done by deriving explicit analytical solutions for the relative pan, tilt, and roll angles in terms of the world pan angle (gaze angle) and the coordinates of the feature points used in their computations. These solutions allow us a better understanding of the problem of calibration in general by providing us with insight as to how errors due to quantization and uncertainty in the location of image centers affect the computation of rotation angles. It is shown that as the distance of feature points from the center of the image decreases, the error due to quantization in the relative pan angle increases quadratically, that of the relative roll angle increases linearly, while that of the tilt angle does not change appreciably. Likewise, it is shown that the errors in the locations of principal points (image centers) do not affect the computation of relative pan and roll angles appreciably, whereas the impact on the relative tilt angle is significant. These findings are likely to be of use even when the relative rotation angles are not small. All of the analytical findings have been supported by extensive simulation.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 1","pages":"Pages 1-19"},"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)80059-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134684410","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":"Abstract of papers accepted for publication","authors":"","doi":"10.1016/S0734-189X(05)80067-2","DOIUrl":"https://doi.org/10.1016/S0734-189X(05)80067-2","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"51 1","pages":"Pages 104-105"},"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)80067-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134684441","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":"Template quadtrees for representing region and line data present in binary images","authors":"M Manohar, P Sudarsana Rao, S Sitarama Iyengar","doi":"10.1016/0734-189X(90)90158-R","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90158-R","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"50 3","pages":"Page 365"},"PeriodicalIF":0.0,"publicationDate":"1990-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90158-R","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137264414","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":"Two methods of image extension","authors":"B White, D Brzakovic","doi":"10.1016/0734-189X(90)90152-L","DOIUrl":"10.1016/0734-189X(90)90152-L","url":null,"abstract":"<div><p>Most operations in image processing involve application of a mask centered at each pixel in an image. Consequently, in order to preserve image size under various operations, it is necessary to extend an image outside its borders for the number of columns and rows equal to half the mask size. This paper describes two methods of image extension so that image size may be preserved under various image processing operations. An algorithm is provided for each method and the results obtained are compared in terms of their statistical properties and aesthetic value.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"50 3","pages":"Pages 342-352"},"PeriodicalIF":0.0,"publicationDate":"1990-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90152-L","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114334733","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":"Object detection by stepwise analysis of spectral, spatial, and topographic features","authors":"Mohan M Trivedi, Chuxin Chen, Daniel H Cress","doi":"10.1016/0734-189X(90)90156-P","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90156-P","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"50 3","pages":"Pages 364-365"},"PeriodicalIF":0.0,"publicationDate":"1990-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90156-P","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137264415","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":"The algebraic basis of mathematical morphology I. Dilations and erosions","authors":"H.J.A.M Heijmans, C Ronse","doi":"10.1016/0734-189X(90)90148-O","DOIUrl":"10.1016/0734-189X(90)90148-O","url":null,"abstract":"<div><p>Mathematical morphology is a theory of image transformations and functionals deriving its tools from set theory and integral geometry. This paper deals with a general algebraic approach which both reveals the mathematical structure of morphological operations and unifies several examples into one framework. The main assumption is that the object space is a complete lattice and that the transformations of interest are invariant under a given abelian group of automorphisms on that lattice. It turns out that the basic operations called dilation and erosion are adjoints of each other in a very specific lattice sense and can be completely characterized if the automorphism group is assumed to be transitive on a sup-generating subset of the complete lattice. The abstract theory is illustrated by a large variety of examples and applications.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"50 3","pages":"Pages 245-295"},"PeriodicalIF":0.0,"publicationDate":"1990-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90148-O","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115432193","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":"Contour codes of isothetic polygons","authors":"Prabir Bhattacharya, Azriel Rosenfeld","doi":"10.1016/0734-189X(90)90153-M","DOIUrl":"10.1016/0734-189X(90)90153-M","url":null,"abstract":"<div><p>An isothetic polygonal arc is one that has all its sides oriented in two orthogonal directions, so that all its angles are right angles. Such an arc is determined (up to congruence) by specifying a “code” sequence of the form <em>α</em><sub>1</sub><em>A</em><sub>1</sub><em>α</em><sub>2</sub> … <em>α</em><sub><em>m</em>−1</sub><em>A</em><sub><em>m</em>−1</sub><em>α</em><sub><em>m</em></sub>, where the α's are positive real numbers representing side lengths, and the <em>A</em>'s are single bits that specify whether the arc turns left or right between one side and the next. In this paper we develop basic properties of this code and show how to derive various geometric properties of the arc (or the region it bounds, if it is closed) directly from the code.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"50 3","pages":"Pages 353-363"},"PeriodicalIF":0.0,"publicationDate":"1990-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90153-M","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130707191","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 50","authors":"","doi":"10.1016/0734-189X(90)90159-S","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90159-S","url":null,"abstract":"","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"50 3","pages":"Page 366"},"PeriodicalIF":0.0,"publicationDate":"1990-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90159-S","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137264817","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}