[1988 Proceedings] Second International Conference on Computer Vision最新文献

{"title":"Recognizing 3-D Objects Using Surface Descriptions","authors":"T. Fan, G. Medioni, R. Nevatia","doi":"10.1109/CCV.1988.590026","DOIUrl":"https://doi.org/10.1109/CCV.1988.590026","url":null,"abstract":"The authors provide a complete method for describing and recognizing 3-D objects, using surface information. Their system takes as input dense range date and automatically produces a symbolic description of the objects in the scene in terms of their visible surface patches. This segmented representation may be viewed as a graph whose nodes capture information about the individual surface patches and whose links represent the relationships between them, such as occlusion and connectivity. On the basis of these relations, a graph for a given scene is decomposed into subgraphs corresponding to different objects. A model is represented by a set of such descriptions from multiple viewing angles, typically four to six. Models can therefore be acquired and represented automatically. Matching between the objects in a scene and the models is performed by three modules: the screener, in which the most likely candidate views for each object are found; the graph matcher, which compares the potential matching graphs and computes the 3-D transformation between them; and the analyzer, which takes a critical look at the results and proposes to split and merge object graphs. >","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122304205","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":"Using Symmetries For Analysis Of Shape From Contour","authors":"F. Ulupinar, R. Nevatia","doi":"10.1109/CCV.1988.590018","DOIUrl":"https://doi.org/10.1109/CCV.1988.590018","url":null,"abstract":"Inference of 3-D shape from 2-D contours in a single image is an important problem in machine vision. We survey classes of techniques proposed in the past and provide a critical analysis. We propose two kinds of symmetries in figures, which we call parallel and mirror symmetries, give significant information about surface shape for a variety of objects. We show the constraints imposed by these symmetries and how to use them to infer 3-D shape. Our method is applicable to any zero-gaussian curvature surface, and also to a variety of doubly curved surfaces. One of our mathematical results is that for a cone, the surface shape can be constructed uniquely under very simple assumptions. We also show some preliminary results on extraction of symmetries from real images.","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128227211","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 Trinocular General Support Algorithm: A Three-camera Stereo Algorithm For Overcoming Binocular Matching Errors","authors":"C. Stewart, C. Dyer","doi":"10.1109/CCV.1988.589983","DOIUrl":"https://doi.org/10.1109/CCV.1988.589983","url":null,"abstract":"The combined use of binocular and new trinocular matching constraints in the Trinocular General Support Algorithm's (TGSA) parallel relaxation computation is shown to overcome many of the problems in binocular sterm matching. These problems include: (I) ambiguity in matching in periodic regions, especially when such a region is partially-occluded, (2) erroneous matches near occluded regions, and (3) missing and erronwus matches due to significant structural variations between the images. The TGSA employs cameras positioned at the vertices of an isosceles right triangle. Matching takes place between the horizontally-aligned pair of images and the vertically-aligned pair of images. Along with a variety of binocular constraints, new trinocular constraints, called trinocular uniqueness and the trinocular disparity gradient, are used to relate vertical and horizontal matches. When combined using a connectionist network relaxation algorithm, these constraints help to overcome the binocular matching problems listed above. For example, the trinocular disparity gradient provides enough information to directly resolve ambiguity in periodic regions in many cases. The TGSA has been tested on a number of image triples to demonstrate its advantages over previous binocular and trinocular stereo matching algorithms.","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128762326","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":"Pyramid Implementation Of Optimal Step Conjugate Search Algorithms For Some Computer Vision Problems","authors":"T. Simchony, R. Chellappa, Z. Lichtenstein","doi":"10.1109/CCV.1988.590038","DOIUrl":"https://doi.org/10.1109/CCV.1988.590038","url":null,"abstract":"","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133552389","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":"Synergistic Smooth Surface Stereo","authors":"T. Boult, Liang-Hua Chen","doi":"10.1109/CCV.1988.589980","DOIUrl":"https://doi.org/10.1109/CCV.1988.589980","url":null,"abstract":"This paper presents a new algorithm for stereo matching. The algo- rithm combines what are generally three processes, feature matching, surface reconstruction, and segmentation of world surfaces, in a consis- tent and synergistic way. By integrating these phases, which are usually sequential, the algorithm can make use of the current surface approxi- mation to disambiguate potential matches. This results in higher data densities, a consistency of interpretation, and greater system flexibility. Examples of the algorithm are presented on real and synthetic images, including a scene with a transparent surface.","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394511","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":"Optimal Computing Of Structure From Motion Using Point Correspondences In Two Frames","authors":"M. Spetsakis, Y. Aloimonos","doi":"10.1109/CCV.1988.590022","DOIUrl":"https://doi.org/10.1109/CCV.1988.590022","url":null,"abstract":"One of the problems associated with any approach to the structure from motion problem using point correspondence, i.e. recovering the structure of a moving object from its successive images, is the use of least squares on dependent variables. We formulate the problem as a quadratic minimization problem with a non-linear constraint. Then we derive the condition for i,he solution to be optimal under the assumption of Gaussian noise in the input, in the Maximum Likelihood Principle sense. This constraint minimization reduces to the solution of a nonlinear system which in the presence of modest noise is easy to approximate. We present two efficient ways to approximate it and we discuss some inherent limitations of the structure from motion problem when two frames are used that should be taken into account in robotics applications that involve dynamic imagery. In addition, our formulation introduces a framework in which previous works on the subject become special cases.","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133734686","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":"An Adaptive Clustering Algorithm For Image Segmentation","authors":"T. Pappas","doi":"10.1109/CCV.1988.590006","DOIUrl":"https://doi.org/10.1109/CCV.1988.590006","url":null,"abstract":"The problem of segmenting images of objects with smooth surfaces is considered. The algorithm we present is a generalization of the ,K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image. Spatial constraints are included by the use of a Gibbs random field model. Local intensity variations are accounted for in a n iterative procedure involving averaging over a sliding window whose size decreases as the algorithm progresses. Results with an eight-neighbor Gibbs random field model applied to pictures of industrial objects, buildings, aerial photographs, optical characters, and faces, show that the algorithm performs better than the K-means algorithm and its nonadaptive extensions that incorporate spatial constraints by the use of Gibbs random fields. A hierarchical implementation is also presented and results in better performance and faster speed of execution. The segmented images are caricatures of the originals which preserve the most significant features, while removing unimportant details. They can be used in image recognition and as crude representations of the image. The caricatures are easy to display or print using a few grey levels and can be coded very efficiently. In particular, segmentation of faces results in binary sketches which preserve the main characteristics of the face, so that it is easily recognizable. I. INTRODUCTION E present a technique for segmenting a grey-scale image (typically 2.56 levels) into regions of uniform or slowly varying intensity. The segmented image consists of very few levels (typically 2-4). each denoting a different region, as shown in Fig. 1. It is a sketch, or caricature, of the original image which preserves its most significant features, while removing unimportant details. It can thus be the first stage of an image recognition system. However, assuming that the segmented image retains the intelligibility of the original, it can also be used as a crude representation of the image. The caricature has the advantage that it is easy to display or print with very few grey levels. The number of levels is crucial for special display media like paper, cloth, and binary computer screens. Also, the caricature can be coded very efficiently , since we only have to code the transitions between a few grey levels. We develop an algorithm that separates the pixels in the image into clusters based on both their intensity and their","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127540689","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":"Morphological Feature Detection","authors":"J. Noble","doi":"10.5244/C.2.32","DOIUrl":"https://doi.org/10.5244/C.2.32","url":null,"abstract":"We describe investigations applying grey-scale mathematical morphology to the problem of feature detection. We show how a combination of morphological operators can be interpreted in terms of the differential geometrical characteristics of the intensity surface. This is significant in that it provides insight into how morphological operators manipulate image data in a manner that has no parallel in traditional convolutionbased image processing. Results using a simple morphological boundary detector compare favourably with the output of a normal edge detector 3uch as the Canny operator. However, boundary detection differs in two important respects; the performance is generally better in regions of high image curvature and image junction information remains explicit. We provide experimental evidence to support these claims. An image description is only of use if it is an aid to image understanding. We conclude with a brief discussion of a morphologically derived scheme based on boundary surface features and indicate how such a description provides potentially powerful constraints for correspondence algorithms.","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129924693","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":"A Color Reflectance Model And Its Use For Segmentation","authors":"G. Healey","doi":"10.1109/CCV.1988.590024","DOIUrl":"https://doi.org/10.1109/CCV.1988.590024","url":null,"abstract":"This paper presents a color reflectance model and demonstrates its usefulness for segn~entation. I adopt general physical models which describe the interaction of light with matter. These models apply to both metal and dielectric materials. The models indicate that, in general, reflectance is a complicated function of waveleiigth and geometry. An analysis of the general reflectance models, however, shows that approximate reAectance models exist which preserve much of the structure of the more detailed models. The approximate color reflectance model is the basis of an algorithm which is used during segmentation. This algorithm uses normalized color to classify surfaces according to milr terial composition. Experimen ta1,results are presented. electrics, ACRM is equivalent to the dichromatic reflection model suggested by Shafer [14]. In this paper, I use the Reichman body scattering model [13] to show that the dichromatic reflection model is a reasonable approximation for a large class of inhomogeneous dielectrics. I also show from the Torrance-Sparrow [17] specular reflection model and the Fresnel equations [2] that a unichromatic reflection model is a reasonable approximation for metals. Thus, ACRM combines the dichromatic reflection model for inhomogeneous dielectrics with a unichromatic reflection model for metals. The analysis includes an estimate of the accuracy of ACRM for various materials. An algorithm is derived from ACRM which is used to classify image regions based on the material of the corresponding object surfaces.","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132314275","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":"Evolution Properties Of Space Curves","authors":"F. Mokhtarian","doi":"10.1109/CCV.1988.589977","DOIUrl":"https://doi.org/10.1109/CCV.1988.589977","url":null,"abstract":"An evolved version I?, of a space curve l7 is obtained by convolving a parametric representation of r with a Gaussian function of variance 0'. The process of generating the ordered sequence of curves {I',o>O} is referred to as the evolution of r. Evolved space curves arise when computing the Torsion and Curvature Scale Space representation of a space curve. A number of evolution properties of space curves are investigated in this paper. It is shown that the evolution of space curves is invariant under rotation, uniform scaling and translation of those curves. This is an essential propert for any reliable shape representation. It is also shown tiat properties such as connectedness and closedness of a space curve are preserved during evolution of the curve and that the center of mass of a s ace curve remains the same as the curve evolves. AnotRer result is that a space curve remains inside its convex hull during evolution. The two main theorems of the paper examine a s ace curve during its evolution just before and just after tie formation of a cusp point. It is shown that strong constraints on the shape of the curve in the neighborhood of the cusp point exist just before and just after the formation of that point. Final1 it is argued that each one of the results obtained in tiis paper is important and useful for practical applications. I. Introduction A multi-scale representation for one-dimensional functions and signals was first proposed by Stansfield [1980] and later developed by Witkin [1983]. The signal fp) is convolved with a Gaussian function as its variance 0 varies from a small to a large value. The aero-crossings of the second derivative of each convolved signal are extracted and marked in the z-o space. The result is the Scale Space Image of the signal. Mokhtarian and Mackworth [1986] generalized that concept to planar curves. A planar curve r is parametrized by arc length U and represented using its coordinate functions. An evolved version of I' is computed by convolving each of its coordinate functions with a Gaussian function of variance 0' and denoted by I',,. The process of generating the ordered sequence of curven {r,lo>O} is referred to as the evolution of r. The curvature of each Fa can be expressed in terms of the first and second derivatives of convolved versions of functions z( U) and y(u). It is therefore possible to extract the curvature zero-crossings of each I', as 5 varies from a small to a large value and mark them in the U-o space. The result is referred to as the Curvature Scale Space Image of the curve. Mokhtarian [1988b] generalized the above concept further to space curves. The parametrization of a space curve can be expressed as: I' = (z(u),y(u),z(u)). Curvature and torsion of an evolved space curve can be expressed in terms of the first three derivatives of convolved versions of functions z(u), y(~) and n(u). A scale space representation for space curves consists of the Torsion and Curvature Scale Space Images wh","PeriodicalId":229545,"journal":{"name":"[1988 Proceedings] Second International Conference on Computer Vision","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114266730","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}