{"title":"Symbolic reasoning in object extraction","authors":"Amnon Meisels, Doron Mintz","doi":"10.1016/0734-189X(90)90087-C","DOIUrl":"10.1016/0734-189X(90)90087-C","url":null,"abstract":"<div><p>A realization of the top-down use of knowledge in the process of extraction of simple man-made objects from aerial photographs is presented. The finding of objects is performed by a reasoning rule-based program written in prolog. The program is purely symbolic and has no access to digital data, yet it produces the needed objects by controlling three other modules of the system through a purely symbolic interface. The program was run as a road finder on aerial images and the experiment is described in detail. We demonstrate the simple “programmability architecture” of the program by presenting examples of simple additions that make it find different kinds of simple objects. Our paradigm makes it possible to understand much better, via the reasoning mechanism, the process of object extraction.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 447-459"},"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)90087-C","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674585","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":"On topology as applied to image analysis","authors":"Gabor T Herman","doi":"10.1016/0734-189X(90)90084-9","DOIUrl":"10.1016/0734-189X(90)90084-9","url":null,"abstract":"<div><p>We discuss the recently published claim of V. A. Kovalevsky that the topology of cellular complexes is the only appropriate topology for image analysis. In some sense we confirm this claim and even generalize it from the finite domain to an infinite one. We prove some results which can be interpreted to show that the class of partially ordered sets is strictly equivalent to a class of topological spaces which is certainly powerful enough to handle all of image analysis. However, such equivalence does not carry over when the partially ordered sets are complemented with a dimension function so as to form cellular complexes. In fact, it remains unclear whether the <em>subclass</em> of cellular complexes which use the assignment of dimension which is standard in image analysis is indeed powerful enough to encompass all problems of image analysis.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 409-415"},"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)90084-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121916090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On characterizing ribbons and finding skewed symmetries","authors":"Jean Ponce","doi":"10.1016/0734-189X(90)90079-B","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90079-B","url":null,"abstract":"<div><p>Following Rosenfeld, we compare Blum, Brooks, and Brady ribbons. We prove that Blum and Brady ribbons are not, in general, Brooks ribbons. Conversely, we prove that Brooks ribbons are, in general, neither Blum nor Brady ribbons. For Blum and Brady ribbons, it is in principle trivial to decide whether two contour points may form a ribbon pair: they have to form a local symmetry. This property is not true for Brooks ribbons. It is possible to characterize locally the pairs of contour points which form a Brooks ribbon pair? Using the curvature of the contour of a Brooks ribbon, we show that the answer to this question is yes for some classes of Brooks ribbons, including skewed symmetries. This result is used in an implemented algorithm for finding skewed symmetries in an image, and examples of segmentation of real images are given.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 328-340"},"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)90079-B","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137438521","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":"Systolic implementation of the adaptive solution to normal equations","authors":"P Comon, Y Robert, D Trystram","doi":"10.1016/0734-189X(90)90083-8","DOIUrl":"10.1016/0734-189X(90)90083-8","url":null,"abstract":"<div><p>We are interested in the systolic computation of projection operators entering digital signal processing, or more precisely, solution of the so-called normal equations involved in adaptive systems. The systolic array proposed achieves a real-time adaptive solution, i.e., updates the left- and right-hand sides of the linear equation and computes its solution at each time step.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 402-408"},"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)90083-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116321313","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":"Vector field restoration by the method of convex projections","authors":"Patrice Y Simard , Guy E Mailloux","doi":"10.1016/0734-189X(90)90081-6","DOIUrl":"10.1016/0734-189X(90)90081-6","url":null,"abstract":"<div><p>In this paper, the theory of image restoration by projections onto closed convex sets is applied to the restoration of vector fields. A set of useful projection operators is presented together with a linear time numerical implementation. These projection operators can be used to restore from partial information the velocity or deformation fields computed between successive views of a scene. They also find applications in the restoration of vector fields of physical quantities as those encountered in mechanics, hydrodynamics, or electromagnetism. The method is compared with the variational approach and illustrated by restoring simulated vector fields.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 360-385"},"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)90081-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126464945","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":"Finding point correspondence and determining motion of a rigid object from two weak perspective views","authors":"Chia-Hoang Lee, Thomas Huang","doi":"10.1016/0734-189X(90)90078-A","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90078-A","url":null,"abstract":"<div><p>Given two images of an <em>n</em>-point configuration which undergoes 3D rotation, translation, and scaling, our problems are (i) How can we match the corresponding points in the two images? Can all the possible mapping be found? (ii) What underlying motions and associated depth components of these points could account for the two images? (iii) Can the object be recovered uniquely? This formulation of the <em>n</em>-point problem is in the most general setting and does not assume attributes or features. A natural question to ask is whether an <em>n</em>-point problem is equivalent to a set of fewer-point problems. This paper presents a method which reduces an <em>n</em>-point problem to a set of 4-point problems. The effort of reduction takes <em>O(n)</em> steps and it also takes <em>O(n)</em> steps to construct all possible mappings of an <em>n</em>-point set from the solution to a 4-point problem. Other results include (1) coplanarity condition of four points in two views, (2) recovering the tilt direction of the rotational axis using four points in two views, (3) recovering the scaling factor.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 309-327"},"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)90078-A","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137438167","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":"Synchronous multiprocessor implementation of the Hough transform","authors":"D Ben-Tzvi, A Naqvi, M Sandler","doi":"10.1016/0734-189X(90)90086-B","DOIUrl":"10.1016/0734-189X(90)90086-B","url":null,"abstract":"<div><p>Normally, in parallel implementations of the Hough transform either the transform space or the set of image features can be distributed among the processing elements. A method is proposed to link parallel access to feature points in the image, and parallel access to the transform space. A synchronous processing sequence is suggested such that both can be distributed. Real-time performance has been obtained on a MIMD distributed memory architecture.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 3","pages":"Pages 437-446"},"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)90086-B","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128603114","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":"Polygonal representation: A maximum likelihood approach","authors":"Thomas L Hemminger, Carlos A Pomalaza-Ráez","doi":"10.1016/0734-189X(90)90056-2","DOIUrl":"10.1016/0734-189X(90)90056-2","url":null,"abstract":"<div><p>Image boundaries are frequently approximated with polygons. The polygonal approximation technique described here constructs straight lines from digital line segments having the same angular orientation. The procedure sequentially analyzes the segmented contour while producing a diminishing set of candidate lines. When particular conditions prevail, a straight line may be recognized. These operations are performed on integer and set variables only, obviating the need for floating point calculations.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 2","pages":"Pages 239-247"},"PeriodicalIF":0.0,"publicationDate":"1990-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90056-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134203995","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":"Shape from texture using the Wigner distribution","authors":"Jack Y Jau , Roland T Chin","doi":"10.1016/0734-189X(90)90057-3","DOIUrl":"https://doi.org/10.1016/0734-189X(90)90057-3","url":null,"abstract":"<div><p>This paper presents a method for estimating the orientation of a textured surface as a descriptor of surface shape. It is based on the analysis of local spectral information of the texture in an image. The local spectrum representation is computed by the two-dimensional Wigner distribution, which gives the spatial-frequency information as a function of location. The change in texture density, or the so-called <em>texture gradient</em>, caused by the perspective projection of a surface in the three-dimensional world onto the two-dimensional image plane, is computed from this space-frequency representation by measuring the high frequency energy distribution at each location of the image. The surface orientation is then estimated from the texture gradient. This method was implemented for the limited case of planar surfaces. Simulations were performed and results were analyzed to address issues related to the method's estimation accuracy, implementation, and limitations.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 2","pages":"Pages 248-263"},"PeriodicalIF":0.0,"publicationDate":"1990-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90057-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137281692","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":"Automated stereophotogrammetry","authors":"Greg Brookshire, Morton Nadler, Choon Lee","doi":"10.1016/0734-189X(90)90059-5","DOIUrl":"10.1016/0734-189X(90)90059-5","url":null,"abstract":"<div><p>In this paper we outline a structural pattern recognition approach to the stereo matching problem of automated stereophotogrammetry. Oriented-edge graphs are obtained with the edge vectors and filtered to obtained feature points for matching purposes. A resolution pyramid is based to aid the accurate matching of the feature points. At each stage of the pyramid, pseudo-hexagonal gray scale arrays are used to bypass the four-eight connectivity paradox in the implementation of association of oblique vectors in Bowker's association filter. The Fisher-<span><math><mtext>z</mtext></math></span> transform is used to determine the correlation function threshold in matching. To fill the gaps between the matched nodes, a local interpolation method that linearly weights the disparity values in a window was developed. The interpolated points are not used for matching, but give the initial approximation at the next lower stage of the resolution pyramid. Finally, a modified normalized gray-scale correlation is used to refine the parallax found at the lowest level of the pseudo-hex resolution pyramid using the original 2D raster. The correlation scheme used here works in both directions: from left to right and from right to left, to obtain reliable matching. We have tried to use as simple operations as possible in each stage to allow the algorithm to run in real-time, possibly leading to an economical hardware implementation.</p></div>","PeriodicalId":100319,"journal":{"name":"Computer Vision, Graphics, and Image Processing","volume":"52 2","pages":"Pages 276-296"},"PeriodicalIF":0.0,"publicationDate":"1990-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0734-189X(90)90059-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115704927","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}