Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710751
G. Jacob, J. Noble, A. Blake
In this paper we present an evaluation of a robust visual image tracker on echocardiographic image sequences. We show how the tracking framework can be customised to define an appropriate shape-space that describes heart shape deformations that can be learnt from a training data set. We also investigate an energy-based temporal boundary enhancement method to improve image feature measurement. Preliminary results are presented demonstrating tracking on real normal heart motion data sequences and synthesised and real abnormal heart motion data sequences. We conclude by discussing some of our current research efforts.
{"title":"Robust contour tracking in echocardiographic sequences","authors":"G. Jacob, J. Noble, A. Blake","doi":"10.1109/ICCV.1998.710751","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710751","url":null,"abstract":"In this paper we present an evaluation of a robust visual image tracker on echocardiographic image sequences. We show how the tracking framework can be customised to define an appropriate shape-space that describes heart shape deformations that can be learnt from a training data set. We also investigate an energy-based temporal boundary enhancement method to improve image feature measurement. Preliminary results are presented demonstrating tracking on real normal heart motion data sequences and synthesised and real abnormal heart motion data sequences. We conclude by discussing some of our current research efforts.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121655062","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710816
Song-Chun Zhu, D. Mumford
Recently there have been increasing interests in using nonlinear PDEs for applications in computer vision and image processing. In this paper, we propose a general statistical framework for designing a new class of PDEs. For a given application, a Markov random field model p(I) is learned according to the minimax entropy principle so that p(I) should characterize the ensemble of images in our application. P(I) is a Gibbs distribution whose energy terms can be divided into two categories. Subsequently the partial differential equations given by gradient descent on the Gibbs potential are essentially reaction-diffusion equations, where the energy terms in one category produce anisotropic diffusion while the inverted energy terms in the second category produce reaction associated with pattern formation. We call this new class of PDEs the Gibbs Reaction And Diffusion Equations-GRADE and we demonstrate experiments where GRADE are used for texture pattern formation, denoising, image enhancement, and clutter removal.
{"title":"GRADE: Gibbs reaction and diffusion equations","authors":"Song-Chun Zhu, D. Mumford","doi":"10.1109/ICCV.1998.710816","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710816","url":null,"abstract":"Recently there have been increasing interests in using nonlinear PDEs for applications in computer vision and image processing. In this paper, we propose a general statistical framework for designing a new class of PDEs. For a given application, a Markov random field model p(I) is learned according to the minimax entropy principle so that p(I) should characterize the ensemble of images in our application. P(I) is a Gibbs distribution whose energy terms can be divided into two categories. Subsequently the partial differential equations given by gradient descent on the Gibbs potential are essentially reaction-diffusion equations, where the energy terms in one category produce anisotropic diffusion while the inverted energy terms in the second category produce reaction associated with pattern formation. We call this new class of PDEs the Gibbs Reaction And Diffusion Equations-GRADE and we demonstrate experiments where GRADE are used for texture pattern formation, denoising, image enhancement, and clutter removal.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126657362","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710701
Y. Rubner, Carlo Tomasi, L. Guibas
We introduce a new distance between two distributions that we call the Earth Mover's Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving "distribution mass" around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.
{"title":"A metric for distributions with applications to image databases","authors":"Y. Rubner, Carlo Tomasi, L. Guibas","doi":"10.1109/ICCV.1998.710701","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710701","url":null,"abstract":"We introduce a new distance between two distributions that we call the Earth Mover's Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving \"distribution mass\" around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133768000","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710839
H. Nagel, M. Haag
Model-based vehicle tracking in traffic image sequences can be made more robust by matching expected displacement rates of vehicle surface points to optical flow (OF) vectors computed from an image sequence. The capability to track vehicles uninterruptedly in this manner over extended image sequences results in the ability to investigate even small errors in OF estimation. It turns out that the OF magnitudes are systematically underestimated. The-albeit small-bias can be corrected by analyzing the influence of explicitly modeled grey value noise on the precision of OF values estimated by means of the neighborhood sampling method.
{"title":"Bias-corrected optical flow estimation for road vehicle tracking","authors":"H. Nagel, M. Haag","doi":"10.1109/ICCV.1998.710839","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710839","url":null,"abstract":"Model-based vehicle tracking in traffic image sequences can be made more robust by matching expected displacement rates of vehicle surface points to optical flow (OF) vectors computed from an image sequence. The capability to track vehicles uninterruptedly in this manner over extended image sequences results in the ability to investigate even small errors in OF estimation. It turns out that the OF magnitudes are systematically underestimated. The-albeit small-bias can be corrected by analyzing the influence of explicitly modeled grey value noise on the precision of OF values estimated by means of the neighborhood sampling method.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133587967","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710803
Fredrik Kahl, A. Heyden
In this paper it is shown hour corresponding conics in two images can be used to estimate the epipolar geometry in terms of the fundamental/essential matrix. The corresponding conics can, be images of either planar celtics or silhouettes of quadrics. It is shown that one conic correspondence gives two independent constraints on the fundamental matrix and a method to estimate the fundamental matrix from at least four corresponding conics is presented. Furthermore, a new type of fundamental matrix for describing conic correspondences is introduced. Finally, it is shown that the problem of estimating the fundamental matrix from 5 point correspondences and 1 conic correspondence in general has 10 different solutions. A method to calculate these solutions is also given together with an experimental validation.
{"title":"Using conic correspondences in two images to estimate the epipolar geometry","authors":"Fredrik Kahl, A. Heyden","doi":"10.1109/ICCV.1998.710803","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710803","url":null,"abstract":"In this paper it is shown hour corresponding conics in two images can be used to estimate the epipolar geometry in terms of the fundamental/essential matrix. The corresponding conics can, be images of either planar celtics or silhouettes of quadrics. It is shown that one conic correspondence gives two independent constraints on the fundamental matrix and a method to estimate the fundamental matrix from at least four corresponding conics is presented. Furthermore, a new type of fundamental matrix for describing conic correspondences is introduced. Finally, it is shown that the problem of estimating the fundamental matrix from 5 point correspondences and 1 conic correspondence in general has 10 different solutions. A method to calculate these solutions is also given together with an experimental validation.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122192238","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710695
Yakup Genç, J. Ponce
This paper addresses the problem of characterizing the space formed by all images of a rigid set of n points observed by a weak perspective or paraperspective camera. By taking explicitly into account the Euclidean constraints associated with calibrated cameras, we show that this space is a six-dimensional variety embedded in R/sup 2n/, and parameterize it using the image positions of three reference points. This parameterization is constructed via linear least squares from point correspondences established across a sequence of images, and it is used to synthesize new pictures without any explicit three-dimensional model. Degenerate scene and camera configurations are analyzed, and experiments with real image sequences are presented.
{"title":"Parameterized image varieties: a novel approach to the analysis and synthesis of image sequences","authors":"Yakup Genç, J. Ponce","doi":"10.1109/ICCV.1998.710695","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710695","url":null,"abstract":"This paper addresses the problem of characterizing the space formed by all images of a rigid set of n points observed by a weak perspective or paraperspective camera. By taking explicitly into account the Euclidean constraints associated with calibrated cameras, we show that this space is a six-dimensional variety embedded in R/sup 2n/, and parameterize it using the image positions of three reference points. This parameterization is constructed via linear least squares from point correspondences established across a sequence of images, and it is used to synthesize new pictures without any explicit three-dimensional model. Degenerate scene and camera configurations are analyzed, and experiments with real image sequences are presented.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132580164","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710727
A. Banerjee, P. Burlina, F. Alajaji
We propose a segmentation method based on Polya's urn model for contagious phenomena. Initial labeling of the pixel is obtained using a Maximum Likelihood (ML) estimate or the Nearest Mean Classifier (NMC), which are used to determine the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. Examples of the application of this scheme to the segmentation of synthetic texture images, Ultra-Wideband Synthetic Aperture Radar (UWB SAR) images and Magnetic Resonance Images (MRI) are provided.
{"title":"Contagion-driven image segmentation and labeling","authors":"A. Banerjee, P. Burlina, F. Alajaji","doi":"10.1109/ICCV.1998.710727","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710727","url":null,"abstract":"We propose a segmentation method based on Polya's urn model for contagious phenomena. Initial labeling of the pixel is obtained using a Maximum Likelihood (ML) estimate or the Nearest Mean Classifier (NMC), which are used to determine the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. Examples of the application of this scheme to the segmentation of synthetic texture images, Ultra-Wideband Synthetic Aperture Radar (UWB SAR) images and Magnetic Resonance Images (MRI) are provided.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132951689","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710724
J. Haddon, D. Forsyth
Diffuse interreflections cause effects that make current theories of shape from shading unsatisfactory. We show that distant radiating surfaces produce radiosity effects at low spatial frequencies. This means that, if a shading pattern has a small region of support, unseen surfaces in the environment can only produce effects that vary slowly over the support region. It is therefore relatively easy to construct matching processes for such patterns that are robust to interreflections. We call regions with these patterns "shading primitives". Folds and grooves on surfaces provide two examples of shading primitives; the shading pattern is relatively independent of surface shape at a fold or a groove, and the pattern is localised. We show that the pattern of shading can be predicted accurately by a simple model, and derive a matching process from this model. Both groove and fold matchers are shown to work well on images of real scenes.
{"title":"Shading primitives: finding folds and shallow grooves","authors":"J. Haddon, D. Forsyth","doi":"10.1109/ICCV.1998.710724","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710724","url":null,"abstract":"Diffuse interreflections cause effects that make current theories of shape from shading unsatisfactory. We show that distant radiating surfaces produce radiosity effects at low spatial frequencies. This means that, if a shading pattern has a small region of support, unseen surfaces in the environment can only produce effects that vary slowly over the support region. It is therefore relatively easy to construct matching processes for such patterns that are robust to interreflections. We call regions with these patterns \"shading primitives\". Folds and grooves on surfaces provide two examples of shading primitives; the shading pattern is relatively independent of surface shape at a fold or a groove, and the pattern is localised. We show that the pattern of shading can be predicted accurately by a simple model, and derive a matching process from this model. Both groove and fold matchers are shown to work well on images of real scenes.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692187","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710823
Kishore Bubna, C. Stewart
The problem of model selection is relevant to many areas of computer vision. Model selection criteria have been used in the vision literature and many more have been proposed in statistics, but the relative strengths of these criteria have not been analyzed in vision. More importantly, suitable extensions to these criteria must be made to solve problems unique to computer vision. Using the problem of surface reconstruction as our context, we analyze existing criteria using simulations and sensor data, introduce new criteria from statistics, develop novel criteria capable of handling unknown error distributions and outliers, and extend model selection criteria to apply to the surface merging problem. The new surface merging rules improve upon previous results, and work well even at small step heights (h=3/spl sigma/) and crease discontinuities. Our results show that a Bayesian criteria and its bootstrapped variant perform the best, although for time-sensitive applications, a variant of the Akaike criterion may be a better choice. Unfortunately, none of the criteria work reliably for small region sizes, implying that model selection and surface merging should be avoided unless the region size is sufficiently large.
{"title":"Model selection and surface merging in reconstruction algorithms","authors":"Kishore Bubna, C. Stewart","doi":"10.1109/ICCV.1998.710823","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710823","url":null,"abstract":"The problem of model selection is relevant to many areas of computer vision. Model selection criteria have been used in the vision literature and many more have been proposed in statistics, but the relative strengths of these criteria have not been analyzed in vision. More importantly, suitable extensions to these criteria must be made to solve problems unique to computer vision. Using the problem of surface reconstruction as our context, we analyze existing criteria using simulations and sensor data, introduce new criteria from statistics, develop novel criteria capable of handling unknown error distributions and outliers, and extend model selection criteria to apply to the surface merging problem. The new surface merging rules improve upon previous results, and work well even at small step heights (h=3/spl sigma/) and crease discontinuities. Our results show that a Bayesian criteria and its bootstrapped variant perform the best, although for time-sensitive applications, a variant of the Akaike criterion may be a better choice. Unfortunately, none of the criteria work reliably for small region sizes, implying that model selection and surface merging should be avoided unless the region size is sufficiently large.","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132244644","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}
Pub Date : 1998-01-04DOI: 10.1109/ICCV.1998.710730
Paul L. Rosin
Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very different principles. Either the noise or the signal is modelled, and the model covers either the spatial or intensity distribution characteristics. The methods are: 1) a Normal model is used for the noise intensity distribution, 2) signal intensities are tested by making local intensity distribution comparisons' in the two image frames (i.e. the difference map is not used), 3) the spatial properties of the noise are modelled by a Poisson distribution, and 4) the spatial properties of the signal are modelled as a stable number of regions (or stable Euler number).
{"title":"Thresholding for change detection","authors":"Paul L. Rosin","doi":"10.1109/ICCV.1998.710730","DOIUrl":"https://doi.org/10.1109/ICCV.1998.710730","url":null,"abstract":"Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very different principles. Either the noise or the signal is modelled, and the model covers either the spatial or intensity distribution characteristics. The methods are: 1) a Normal model is used for the noise intensity distribution, 2) signal intensities are tested by making local intensity distribution comparisons' in the two image frames (i.e. the difference map is not used), 3) the spatial properties of the noise are modelled by a Poisson distribution, and 4) the spatial properties of the signal are modelled as a stable number of regions (or stable Euler number).","PeriodicalId":270671,"journal":{"name":"Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115394459","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}
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