Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops最新文献
{"title":"The World in an Eye","authors":"K. Nishino, S. Nayar","doi":"10.1109/CVPR.2004.248","DOIUrl":"https://doi.org/10.1109/CVPR.2004.248","url":null,"abstract":"This paper provides a comprehensive analysis of exactly what visual information about the world is embedded within a single image of an eye. It turns out that the cornea of an eye and a camera viewing the eye form a catadioptric imaging system. We refer to this as a corneal imaging system. Unlike a typical catadioptric system, a corneal one is flexible in that the reflector (cornea) is not rigidly attached to the camera. Using a geometric model of the cornea based on anatomical studies, its 3D location and orientation can be estimated from a single image of the eye. Once this is done, a wide-angle view of the environment of the person can be obtained from the image. In addition, we can compute the projection of the environment onto the retina with its center aligned with the gaze direction. This foveated retinal image reveals what the person is looking at. We present a detailed analysis of the characteristics of the corneal imaging system including field of view, resolution and locus of viewpoints. When both eyes of a person are captured in an image, we have a stereo corneal imaging system. We analyze the epipolar geometry of this stereo system and show how it can be used to compute 3D structure. The framework we present in this paper for interpreting eye images is passive and non-invasive. It has direct implications for several fields including visual recognition, human-machine interfaces, computer graphics and human affect studies. 1. What do Eyes Reveal?","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"40 1","pages":"444-451"},"PeriodicalIF":0.0,"publicationDate":"2004-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86334728","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":"2D-Shape Analysis Using Conformal Mapping","authors":"Eitan Sharon, D. Mumford","doi":"10.1109/CVPR.2004.2","DOIUrl":"https://doi.org/10.1109/CVPR.2004.2","url":null,"abstract":". The study of 2D shapes and their similarities is a central problem in the field of vision. It arises in particular from the task of classifying and recognizing objects from their observed silhouette. Defining natural distances between 2D shapes creates a metric space of shapes, whose mathematical structure is inherently relevant to the classification task. One intriguing metric space comes from using conformal mappings of 2D shapes into each other, via the theory of Teichm¨uller spaces. In this space every simple closed curve in the plane (a “shape”) is represented by a ‘fingerprint’ which is a diffeomorphism of the unit circle to itself (a differentiable and invertible, periodic function). More precisely, every shape defines to a unique equivalence class of such diffeomorphisms up to right multiplication by a M¨obius map. The fingerprint does not change if the shape is varied by translations and scaling and any such equivalence class comes from some shape. This coset space, equipped with the infinitesimal Weil-Petersson (WP) Riemannian norm is a metric space. In this space, the shortest path between each two shapes is unique, and is given by a geodesic connecting them. Their distance from each other is given by integrating the WP-norm along that geodesic. In this paper we concentrate on solving the “welding\" problem of “sewing\" together conformally the interior and exterior of the unit circle, glued on the unit circle by a given diffeomorphism, to obtain the unique 2D shape associated with this diffeomorphism. This will allow us to go back and forth between 2D shapes and their representing diffeomorphisms in this “space of shapes”. We then present an efficient method for computing the unique shortest path, the geodesic of shape morphing between each two end-point shapes. The group of diffeomorphisms of S 1 acts as a group of isometries on the space of shapes and we show how this can be used to define shape transformations, like for instance ‘adding a protruding limb’ to any shape.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"7 1","pages":"350-357"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84666763","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":"L-8Minimization in Geometric Reconstruction Problems","authors":"Richard I. Hartley, F. Schaffalitzky","doi":"10.1109/CVPR.2004.140","DOIUrl":"https://doi.org/10.1109/CVPR.2004.140","url":null,"abstract":"","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"70 1","pages":"504-509"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76529402","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}
N. Chawla, Thomas E. Moore, K. Bowyer, L. Hall, C. Springer, W. Kegelmeyer
{"title":"Bagging Is a Small-Data-Set Phenomenon","authors":"N. Chawla, Thomas E. Moore, K. Bowyer, L. Hall, C. Springer, W. Kegelmeyer","doi":"10.1109/CVPR.2001.991030","DOIUrl":"https://doi.org/10.1109/CVPR.2001.991030","url":null,"abstract":"Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, disjoint partitions result in better performance than bootstrap aggregates (bags). Many applications (e.g., protein structure prediction) involve the use of datasets that are too large to handle in the memory of a typical computer. Our results indicate that, in such applications, the simple approach of creating a committee of classifiers from disjoint partitions is preferred over the more complex approach of bagging.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"143 1","pages":"684-689"},"PeriodicalIF":0.0,"publicationDate":"2001-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77514810","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":"Robust Visual Recognition of Color Images Rozenn Dahyot, Pierre","authors":"P. Charbonnier, F. Heitz","doi":"10.1109/CVPR.2000.855886","DOIUrl":"https://doi.org/10.1109/CVPR.2000.855886","url":null,"abstract":"","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"25 1","pages":"1685-1690"},"PeriodicalIF":0.0,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73058791","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":"Comparison of Edge Detectors Using an Object Recognition Task","authors":"M. Shin, Dmitry Goldgof, K. Bowyer","doi":"10.1109/CVPR.1999.786964","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786964","url":null,"abstract":"This paper presents a methodology and results of evaluating edge detection algorithms using an object recognition task. A dataset consisting of 37 real images with 5 different jeep-like vehicles is used. Five edge detectors are compared using ROC curve analysis. The Heitger detector gives the best results. The work is being extended to include more images and a train-and-test style evaluation.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"18 1","pages":"1360-"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74249844","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":"Evaluation of Texture Segmentation Algorithms","authors":"K. Chang, K. Bowyer, Munish Sivagurunath","doi":"10.1109/CVPR.1999.786954","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786954","url":null,"abstract":"This paper presents a method of evaluating unsupervised texture segmentation algorithms. The control scheme of texture segmentation has been conceptualized as two modular processes: (1) feature computation and (2) segmentation of homogeneous regions based on the feature values. Three feature extraction methods are considered: gray level co-occurrence matrix, Laws' texture energy and Gabor multi-channel filtering. Three segmentation algorithms are considered: fuzzy c-means clustering, square-error clustering and split-and-merge. A set of 35 real scene images with manually-specified ground truth was compiled. Performance is measured against ground truth on real images using region-based and pixel-based performance metrics.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"99 4 1","pages":"1294-"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89572541","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 Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task","authors":"M. Shin, Dmitry Goldgof, K. Bowyer","doi":"10.1109/CVPR.1998.698608","DOIUrl":"https://doi.org/10.1109/CVPR.1998.698608","url":null,"abstract":"This paper presents a task-oriented evaluation methodology for edge detectors. Performance is measured based on the task of structure from motion. Eighteen real image sequences from 2 different scenes varying in the complexity and scenery types are used. The task-level ground truth for each image sequence is manually specified in terms of the 3D motion and structure. An automated tool computes the accuracy of the motion and structure achieved using the set of edge maps. Parameter sensitivity and execution speed are also analyzed. Four edge detectors are compared. All implementations and data sets are publicly available.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"56 1","pages":"190-195"},"PeriodicalIF":0.0,"publicationDate":"1998-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79677655","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":"Recognizing object function through reasoning about 3-D shape and dynamic physical properties","authors":"L. Stark","doi":"10.1109/CVPR.1994.323880","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323880","url":null,"abstract":"Recent work in computer vision has demonstrated positive results in reasoning about possible object function based on analysis of only the object shape. While shape properties are important, verification of actual functionality generally requires consideration of properties beyond pure static shape. In particular, dynamic physical properties such as the degree of deformation or rigidity under applied force are essential to the function of many objects. The work described in this paper combines reasoning about object shape with reasoning about deformation under applied forces for recognizing (categorizing) an object according to its function. Initial reasoning about the static 3-D object shape provides a function verification plan. This plan is a sequence of interactions that lead to confirming or rejecting the suggested object function. The interactions involve applying test forces to elements of the object structure as identified in the reasoning about static 3-D shape. >","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"43 1","pages":"546-553"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79931826","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":"Realistic range rendering","authors":"P. Flynn","doi":"10.1109/CVPR.1994.323911","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323911","url":null,"abstract":"In many model-based object recognition systems, a synthesize-and-verify technique is used to evaluate the quality of hypotheses. This technique synthesises images of hypothesized objects in hypothesized poses, and compares them against the input imagery, producing a matching score. In this paper, we examine the image synthesis process in the context of triangulation-based range finding. We motivate the use of synthetically shadowed range data, for verification, present a simple and efficient algorithm for generation of shadowed range imagery, and demonstrate its usefulness in a set of real imagery. >","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"75 1","pages":"848-851"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86027463","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}