{"title":"Perceptual completion of occluded surfaces","authors":"L. Williams, A. Hanson","doi":"10.1109/CVPR.1994.323803","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323803","url":null,"abstract":"Researchers in computer vision have primarily studied the problem of visual reconstruction of environmental structure that is plainly visible. In this paper, the conventional goals of visual reconstruction are generalized to include both visible and occluded forward facing surfaces. This larger fraction of the environment is termed the anterior surfaces. In this paper, we show that the boundaries of the anterior surfaces can be represented in viewer-centered coordinates as a labeled knot diagram. Where boundaries are not occluded and where surface reflectance is distinct from that of the background, boundaries will be marked by image contours. However, where boundaries are occluded, or where surface reflectance matches background reflectance, there will be no detectable luminance change in the image. Deducing the complete image trace of the boundaries of the anterior surfaces under these circumstances is termed the figural completion problem. The second half of this paper describes a computational theory of figural completion. A working model is demonstrated on a variety of illusory contour displays. The experimental system employs a two stage process of completion hypothesis and combinatorial optimization. The labeling scheme is enforced by integer linear inequalities so that the optimal feasible solution of an integer linear program defines a typologically valid surface organization.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115017184","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":"Three-dimensional image registration for spiral CT angiography","authors":"M. Yeung, B. Yeo, S. Liou, A. Banihashemi","doi":"10.1109/CVPR.1994.323861","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323861","url":null,"abstract":"This paper presents a 3D image registration approach to 3D CT angiography (CTA). We extend the interest operator from 2D to 3D in order to extract 3D feature points and develop a 3D image flow algorithm to find matches of feature points between the mask and the contrast data sets. A robust 3D motion estimation algorithm is also developed which handles outliers up to 50% of all computed image flow vectors. The subtracted volume after motion correction is rendered on the display. The proposed algorithm has been successfully applied to a clinical CTA data set and the result is very promising.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115742151","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":"Principal component analysis with missing data and its application to object modeling","authors":"H. Shum, K. Ikeuchi, R. Reddy","doi":"10.1109/CVPR.1994.323882","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323882","url":null,"abstract":"Observation-based modeling can reduce the cost and effort of model constructions for tasks such as virtual reality environment. Object modeling from a sequence of range images has been formulated as a problem of principal component analysis with missing data (PCAMD), which can be generalized as a weighted least square (WLS) minimization problem. After all visible regions appeared over the whole sequence are segmented and tracked, a normal measurement matrix of surface normals and a distance measurement matrix of normal distances to the origin are constructed respectively. These two measurement matrices, with possibly many missing elements due to occlusion and mismatching, enable us to formulate multiple view merging as a combination of two WLS problems. The solution to the first WLS problem, which employs the quaternion representation of the rotation matrix, yields surface normals and rotation matrices. Subsequently the normal distances and translation vectors are computed by solving the second WLS problem. Experiments using synthetic data and real range images show that our approach is robust against noise and mismatch because it produces a statistically optimal object model by making use of redundancy from multiple views. A toy house model from a sequence of real range images is presented.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117115621","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":"Affine-invariant B-spline moments for curve matching","authors":"Zhaohui Huang, F. Cohen","doi":"10.1109/CVPR.1994.323871","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323871","url":null,"abstract":"This paper deals with the problem of matching and recognizing planar curves which are modeled as B-splines. The paper presents a fast algorithm for estimating the control points of the B-spline which is robust to nonuniform sampling and noise. Curve matching is achieved by using the similarity measure based on the knot points associated with the B-spline curves. This method, however, can not handle the affine transformation and/or occlusion between the curves. This paper presents solutions to these two problems through the use of a new class of weighted B-spline curve moments that results in (1) a closed-form solution for the affine transformation parameters; (2) the starting and ending points of the segment on the prototype curve that was affine transformed to yield the observed curve. The method is used for classifying affine transformed silhouette of aircrafts, and appears to perform well.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121058849","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 motion analysis","authors":"M. Bober, J. Kittler","doi":"10.1109/CVPR.1994.323931","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323931","url":null,"abstract":"We develop a new robust algorithm for the estimation of optic flow and extraction of other motion-relevant information. A novel combination of the Hough Transform, and robust statistical methods results in unbiased estimates for multiple motions, parallel segmentation and estimation and increased robustness to noise and changes of illumination. The algorithm is fast, due to application of multiresolution in both image and parameter space. A simple, translational motion model and a complex one coping with rotation and change of scale are applied. Also, an accuracy measure for the derived estimate is introduced. The paper includes experimental tests of this new approach and its comparison with several other widely-cited methods. The experiments were aimed at assessing the effect of noise, change of illumination and multiple motions on the algorithms performance. The results show that our approach is significantly more robust than other methods.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127293236","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":"Recovering parametric geons from multiview range data","authors":"Kenong Wu, M. Levine","doi":"10.1109/CVPR.1994.323824","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323824","url":null,"abstract":"Focuses on approximating object part shapes by distinctive types of volumetric primitives. Shape approximation is accomplished by fitting volumetric models called 'parametric geons' to multiview range data of single-part objects and classifying the fitting residuals. Parametric geons are seven qualitative shape types defined by parameterized equations which control the size and degree of tapering and bending. Model fitting is performed by minimizing an objective function which measures the similarity in both size and shape between models and objects. Multiple view data, global shape constraints and global optimization are employed to obtain unique models and to compensate for noise and minor variations in object shape. This approach has been studied in experiments with both synthetic 3D data and actual rangefinder data of perfect and imperfect geon-like objects.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127471013","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}
Tanuja Joshi, B. Vijayakumar, D. Kriegman, J. Ponce
{"title":"HOT curves for modelling and recognition of smooth curved 3D objects","authors":"Tanuja Joshi, B. Vijayakumar, D. Kriegman, J. Ponce","doi":"10.1109/CVPR.1994.323917","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323917","url":null,"abstract":"Arbitrary smooth curved 3D shapes are represented by a discrete set of high-order tangent (HOT) curves, where a surface admits HOTs. These curves determine the structure of the image contours and its catastrophic changes, and there is a natural correspondence between some of them and monocular contour features such as inflections and bitangents. We present a method for automatically constructing the HOT curves from continuous sequences of video images and describe an approach to object recognition using viewpoint-dependent monocular image features as indices into a database of models and as a basis for pose estimation. We have implemented both of the methods, and present results obtained from real images.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125512051","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":"Orientation-based representations of 3-D shape","authors":"Y. Li, R. Woodham","doi":"10.1109/CVPR.1994.323827","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323827","url":null,"abstract":"Orientation-based representations are well-suited to vision tasks including viewpoint independent object recognition and 3D attitude determination. The key property that orientation-based representations share is that they rotate in the same way as the object rotates. Combinations of orientation-based representations of a model and of a sensed object determine inequalities that become equalities if and only if the object and model match both in identity and in attitude. This results in optimization problems that can be solved by standard numerical methods. The paper unifies and extends previous work based on the Extended Gaussian Image (EGI) representation. It provides the theoretical basis for new approaches to object recognition and attitude determination using dense surface data. It extends results on convex polyhedra to the domain of smooth, strictly convex surfaces. The class of shapes covered also is extended to include starshaped sets. The theoretical results lead to feasible algorithms that are both accurate and robust. A proof-of-concept system has been implemented and experiments conducted both on synthesized data and on data obtained from real objects.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115332191","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 new closed-form solution for absolute orientation","authors":"Zhengyan Wang, A. Jepson","doi":"10.1109/CVPR.1994.323819","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323819","url":null,"abstract":"Presents a closed-form solution for the determination of 3D displacement and rotation parameters, given a set of 3D point correspondences. The method applies to the general case of arbitrary displacement and rotation with respect to an arbitrary stationary scene. The approach is based on the linear subspace method, which allows separate linear equations to be extracted for the displacement and rotation. This leads to a simple algorithm, suitable for real-time implementations, for the determination of both the 3D transformation and error estimates for the estimated 3D displacement. Preliminary experimental results with range data are presented.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122378547","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":"Localized Radon transform-based detection of linear features in noisy images","authors":"A. Copeland, G. Ravichandran, M. Trivedi","doi":"10.1109/CVPR.1994.323782","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323782","url":null,"abstract":"This paper presents a Radon transform-based approach to the detection of linear features in images characterized by high noise levels. This approach is based on the localized Radon transform where the intensity integration is performed over short line segments rather than across the entire image. The algorithm, referred to as the feature space line detector (FSLD) algorithm, is tested on synthetic images of linear features with very high noise levels. The results of this testing demonstrate the algorithm's robustness in the presence of noise, as well as its ability to detect and localize linear features that are significantly shorter than the image dimensions or that display some curvature.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"389 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122781924","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}