Pub Date : 2003-10-13DOI: 10.1109/ICCV.2003.1238335
Takashi Machida, N. Yokoya, H. Takemura
In mixed reality, especially in augmented virtuality which virtualizes real objects, it is important to estimate object surface reflectance properties to render the objects under arbitrary illumination conditions. Though several methods have been explored to estimate the surface reflectance properties, it is still difficult to estimate surface reflectance parameters faithfully for complex objects which have nonuniform surface reflectance properties and exhibit interreflections. We describe a new method for densely estimating nonuniform surface reflectance properties of real objects constructed of convex and concave surfaces with interreflections. We use registered range and surface color texture images obtained by a laser rangefinder. Experiments show the usefulness of the proposed method.
{"title":"Surface reflectance modeling of real objects with interreflections","authors":"Takashi Machida, N. Yokoya, H. Takemura","doi":"10.1109/ICCV.2003.1238335","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238335","url":null,"abstract":"In mixed reality, especially in augmented virtuality which virtualizes real objects, it is important to estimate object surface reflectance properties to render the objects under arbitrary illumination conditions. Though several methods have been explored to estimate the surface reflectance properties, it is still difficult to estimate surface reflectance parameters faithfully for complex objects which have nonuniform surface reflectance properties and exhibit interreflections. We describe a new method for densely estimating nonuniform surface reflectance properties of real objects constructed of convex and concave surfaces with interreflections. We use registered range and surface color texture images obtained by a laser rangefinder. Experiments show the usefulness of the proposed method.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116000581","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238343
Yizhou Wang, Song-Chun Zhu
We present a generative model for textured motion phenomena, such as falling snow, wavy river and dancing grass, etc. Firstly, we represent an image as a linear superposition of image bases selected from a generic and over-complete dictionary. The dictionary contains Gabor bases for point/particle elements and Fourier bases for wave-elements. These bases compete to explain the input images. The transform from a raw image to a base or a token representation leads to large dimension reduction. Secondly, we introduce a unified motion equation to characterize the motion of these bases and the interactions between waves and particles, e.g. a ball floating on water. We use statistical learning algorithm to identify the structure of moving objects and their trajectories automatically. Then novel sequences can be synthesized easily from the motion and image models. Thirdly, we replace the dictionary of Gabor and Fourier bases with symbolic sketches (also bases). With the same image and motion model, we can render realistic and stylish cartoon animation. In our view, cartoon and sketch are symbolic visualization of the inner representation for visual perception. The success of the cartoon animation, in turn, suggests that our image and motion models capture the essence of visual perception of textured motion.
{"title":"Modeling textured motion : particle, wave and sketch","authors":"Yizhou Wang, Song-Chun Zhu","doi":"10.1109/ICCV.2003.1238343","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238343","url":null,"abstract":"We present a generative model for textured motion phenomena, such as falling snow, wavy river and dancing grass, etc. Firstly, we represent an image as a linear superposition of image bases selected from a generic and over-complete dictionary. The dictionary contains Gabor bases for point/particle elements and Fourier bases for wave-elements. These bases compete to explain the input images. The transform from a raw image to a base or a token representation leads to large dimension reduction. Secondly, we introduce a unified motion equation to characterize the motion of these bases and the interactions between waves and particles, e.g. a ball floating on water. We use statistical learning algorithm to identify the structure of moving objects and their trajectories automatically. Then novel sequences can be synthesized easily from the motion and image models. Thirdly, we replace the dictionary of Gabor and Fourier bases with symbolic sketches (also bases). With the same image and motion model, we can render realistic and stylish cartoon animation. In our view, cartoon and sketch are symbolic visualization of the inner representation for visual perception. The success of the cartoon animation, in turn, suggests that our image and motion models capture the essence of visual perception of textured motion.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116042288","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238329
James Davis, Xing Chen
Pan-tilt cameras are often used as components of wide-area surveillance systems. It is necessary to calibrate these cameras in relation to one another in order to obtain a consistent representation of the entire space. Existing methods for calibrating pan-tilt cameras have assumed an idealized model of camera mechanics. In addition, most methods have been calibrated using only a small range of camera motion. We present a method for calibrating pan-tilt cameras that introduces a more complete model of camera motion. Pan and tilt rotations are modeled as occurring around arbitrary axes in space. In addition, the wide area surveillance system itself is used to build a large virtual calibration object, resulting in better calibration than would be possible with a single small calibration target. Finally, the proposed enhancements are validated experimentally, with comparisons showing the improvement provided over more traditional methods.
{"title":"Calibrating pan-tilt cameras in wide-area surveillance networks","authors":"James Davis, Xing Chen","doi":"10.1109/ICCV.2003.1238329","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238329","url":null,"abstract":"Pan-tilt cameras are often used as components of wide-area surveillance systems. It is necessary to calibrate these cameras in relation to one another in order to obtain a consistent representation of the entire space. Existing methods for calibrating pan-tilt cameras have assumed an idealized model of camera mechanics. In addition, most methods have been calibrated using only a small range of camera motion. We present a method for calibrating pan-tilt cameras that introduces a more complete model of camera motion. Pan and tilt rotations are modeled as occurring around arbitrary axes in space. In addition, the wide area surveillance system itself is used to build a large virtual calibration object, resulting in better calibration than would be possible with a single small calibration target. Finally, the proposed enhancements are validated experimentally, with comparisons showing the improvement provided over more traditional methods.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125935247","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238328
Jian-Gang Wang, E. Sung, R. Venkateswarlu
We present a novel approach, called the "one-circle " algorithm, for measuring the eye gaze using a monocular image that zooms in on only one eye of a person. Observing that the iris contour is a circle, we estimate the normal direction of this iris circle, considered as the eye gaze, from its elliptical image. From basic projective geometry, an ellipse can be back-projected into space onto two circles of different orientations. However, by using an anthropometric property of the eyeball, the correct solution can be disambiguated. This allows us to obtain a higher resolution image of the iris with a zoom-in camera and thereby achieving higher accuracies in the estimation. The robustness of our gaze determination approach was verified statistically by the extensive experiments on synthetic and real image data. The two key contributions are that we show the possibility of finding the unique eye gaze direction from a single image of one eye and that one can obtain better accuracy as a consequence of this.
{"title":"Eye gaze estimation from a single image of one eye","authors":"Jian-Gang Wang, E. Sung, R. Venkateswarlu","doi":"10.1109/ICCV.2003.1238328","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238328","url":null,"abstract":"We present a novel approach, called the \"one-circle \" algorithm, for measuring the eye gaze using a monocular image that zooms in on only one eye of a person. Observing that the iris contour is a circle, we estimate the normal direction of this iris circle, considered as the eye gaze, from its elliptical image. From basic projective geometry, an ellipse can be back-projected into space onto two circles of different orientations. However, by using an anthropometric property of the eyeball, the correct solution can be disambiguated. This allows us to obtain a higher resolution image of the iris with a zoom-in camera and thereby achieving higher accuracies in the estimation. The robustness of our gaze determination approach was verified statistically by the extensive experiments on synthetic and real image data. The two key contributions are that we show the possibility of finding the unique eye gaze direction from a single image of one eye and that one can obtain better accuracy as a consequence of this.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129898673","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238358
C. Stauffer
We discuss building complex classifiers from a single labeled example and vast number of unlabeled observation sets, each derived from observation of a single process or object. When data can be measured by observation, it is often plentiful and it is often possible to make more than one observation of the state of a process or object. We discuss how to exploit the variability across such sets of observations of the same object to estimate class labels for unlabeled examples given a minimal number of labeled examples. In contrast to similar semisupervised classification procedures that define the likelihood that two observations share a label as a function of the embedded distance between the two observations, this method uses the Naive Bayes estimate of how often the two observations did result from the same observed process. Exploiting this additional source of information in an iterative estimation procedure can generalize complex classification models from single labeled observations. Some examples involving classification of tracked objects in a low-dimensional feature space given thousands of unlabeled observation sets are used to illustrate the effectiveness of this method.
{"title":"Minimally-supervised classification using multiple observation sets","authors":"C. Stauffer","doi":"10.1109/ICCV.2003.1238358","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238358","url":null,"abstract":"We discuss building complex classifiers from a single labeled example and vast number of unlabeled observation sets, each derived from observation of a single process or object. When data can be measured by observation, it is often plentiful and it is often possible to make more than one observation of the state of a process or object. We discuss how to exploit the variability across such sets of observations of the same object to estimate class labels for unlabeled examples given a minimal number of labeled examples. In contrast to similar semisupervised classification procedures that define the likelihood that two observations share a label as a function of the embedded distance between the two observations, this method uses the Naive Bayes estimate of how often the two observations did result from the same observed process. Exploiting this additional source of information in an iterative estimation procedure can generalize complex classification models from single labeled observations. Some examples involving classification of tracked objects in a low-dimensional feature space given thousands of unlabeled observation sets are used to illustrate the effectiveness of this method.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127244228","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238669
Margarita Osadchy, D. Jacobs, R. Ramamoorthi
Recognition systems have generally treated specular highlights as noise. We show how to use these highlights as a positive source of information that improves recognition of shiny objects. This also enables us to recognize very challenging shiny transparent objects, such as wine glasses. Specifically, we show how to find highlights that are consistent with a hypothesized pose of an object of known 3D shape. We do this using only a qualitative description of highlight formation that is consistent with most models of specular reflection, so no specific knowledge of an object's reflectance properties is needed. We first present a method that finds highlights produced by a dominant compact light source, whose position is roughly known. We then show how to estimate the lighting automatically for objects whose reflection is part specular and part Lambertian. We demonstrate this method for two classes of objects. First, we show that specular information alone can suffice to identify objects with no Lambertian reflectance, such as transparent wine glasses. Second, we use our complete system to recognize shiny objects, such as pottery.
{"title":"Using specularities for recognition","authors":"Margarita Osadchy, D. Jacobs, R. Ramamoorthi","doi":"10.1109/ICCV.2003.1238669","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238669","url":null,"abstract":"Recognition systems have generally treated specular highlights as noise. We show how to use these highlights as a positive source of information that improves recognition of shiny objects. This also enables us to recognize very challenging shiny transparent objects, such as wine glasses. Specifically, we show how to find highlights that are consistent with a hypothesized pose of an object of known 3D shape. We do this using only a qualitative description of highlight formation that is consistent with most models of specular reflection, so no specific knowledge of an object's reflectance properties is needed. We first present a method that finds highlights produced by a dominant compact light source, whose position is roughly known. We then show how to estimate the lighting automatically for objects whose reflection is part specular and part Lambertian. We demonstrate this method for two classes of objects. First, we show that specular information alone can suffice to identify objects with no Lambertian reflectance, such as transparent wine glasses. Second, we use our complete system to recognize shiny objects, such as pottery.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130058059","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238659
Carlo Tomasi, Slav Petrov, A. Sastry
Hand gestures are examples of fast and complex motions. Computers fail to track these in fast video, but sleight of hand fools humans as well: what happens too quickly we just cannot see. We show a 3D tracker for these types of motions that relies on the recognition of familiar configurations in 2D images (classification), and fills the gaps in-between (interpolation). We illustrate this idea with experiments on hand motions similar to finger spelling. The penalty for a recognition failure is often small: if two configurations are confused, they are often similar to each other, and the illusion works well enough, for instance, to drive a graphics animation of the moving hand. We contribute advances in both feature design and classifier training: our image features are invariant to image scale, translation, and rotation, and we propose a classification method that combines VQPCA with discrimination trees.
{"title":"3D tracking = classification + interpolation","authors":"Carlo Tomasi, Slav Petrov, A. Sastry","doi":"10.1109/ICCV.2003.1238659","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238659","url":null,"abstract":"Hand gestures are examples of fast and complex motions. Computers fail to track these in fast video, but sleight of hand fools humans as well: what happens too quickly we just cannot see. We show a 3D tracker for these types of motions that relies on the recognition of familiar configurations in 2D images (classification), and fills the gaps in-between (interpolation). We illustrate this idea with experiments on hand motions similar to finger spelling. The penalty for a recognition failure is often small: if two configurations are confused, they are often similar to each other, and the illusion works well enough, for instance, to drive a graphics animation of the moving hand. We contribute advances in both feature design and classifier training: our image features are invariant to image scale, translation, and rotation, and we propose a classification method that combines VQPCA with discrimination trees.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132979841","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238473
J. Vermaak, A. Doucet, P. Pérez
In recent years particle filters have become a tremendously popular tool to perform tracking for nonlinear and/or nonGaussian models. This is due to their simplicity, generality and success over a wide range of challenging applications. Particle filters, and Monte Carlo methods in general, are however poor at consistently maintaining the multimodality of the target distributions that may arise due to ambiguity or the presence of multiple objects. To address this shortcoming this paper proposes to model the target distribution as a nonparametric mixture model, and presents the general tracking recursion in this case. It is shown how a Monte Carlo implementation of the general recursion leads to a mixture of particle filters that interact only in the computation of the mixture weights, thus leading to an efficient numerical algorithm, where all the results pertaining to standard particle filters apply. The ability of the new method to maintain posterior multimodality is illustrated on a synthetic example and a real world tracking problem involving the tracking of football players in a video sequence.
{"title":"Maintaining multimodality through mixture tracking","authors":"J. Vermaak, A. Doucet, P. Pérez","doi":"10.1109/ICCV.2003.1238473","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238473","url":null,"abstract":"In recent years particle filters have become a tremendously popular tool to perform tracking for nonlinear and/or nonGaussian models. This is due to their simplicity, generality and success over a wide range of challenging applications. Particle filters, and Monte Carlo methods in general, are however poor at consistently maintaining the multimodality of the target distributions that may arise due to ambiguity or the presence of multiple objects. To address this shortcoming this paper proposes to model the target distribution as a nonparametric mixture model, and presents the general tracking recursion in this case. It is shown how a Monte Carlo implementation of the general recursion leads to a mixture of particle filters that interact only in the computation of the mixture weights, thus leading to an efficient numerical algorithm, where all the results pertaining to standard particle filters apply. The ability of the new method to maintain posterior multimodality is illustrated on a synthetic example and a real world tracking problem involving the tracking of football players in a video sequence.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130507287","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238470
J. Hoey, J. Little
We present a method for unsupervised learning of classes of motions in video. We project optical flow fields to a complete, orthogonal, a-priori set of basis functions in a probabilistic fashion, which improves the estimation of the projections by incorporating uncertainties in the flows. We then cluster the projections using a mixture of feature-weighted Gaussians over optical flow fields. The resulting model extracts a concise probabilistic description of the major classes of optical flow present. The method is demonstrated on a video of a person's facial expressions.
{"title":"Bayesian clustering of optical flow fields","authors":"J. Hoey, J. Little","doi":"10.1109/ICCV.2003.1238470","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238470","url":null,"abstract":"We present a method for unsupervised learning of classes of motions in video. We project optical flow fields to a complete, orthogonal, a-priori set of basis functions in a probabilistic fashion, which improves the estimation of the projections by incorporating uncertainties in the flows. We then cluster the projections using a mixture of feature-weighted Gaussians over optical flow fields. The resulting model extracts a concise probabilistic description of the major classes of optical flow present. The method is demonstrated on a video of a person's facial expressions.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131703087","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 : 2003-10-13DOI: 10.1109/ICCV.2003.1238637
Paul Smith, T. Drummond, K. Roussopoulos
This paper addresses the problem of computing the trajectory of a camera from sparse positional measurements that have been obtained from visual localisation, and dense differential measurements from odometry or inertial sensors. A fast method is presented for fusing these two sources of information to obtain the maximum a posteriori estimate of the trajectory. A formalism is introduced for representing probability density functions over Euclidean transformations, and it is shown how these density functions can be propagated along the data sequence and how multiple estimates of a transformation can be combined. A three-pass algorithm is described which makes use of these results to yield the trajectory of the camera. Simulation results are presented which are validated against a physical analogue of the vision problem, and results are then shown from sequences of approximately 1,800 frames captured from a video camera mounted on a go-kart. Several of these frames are processed using computer vision to obtain estimates of the position of the go-kart. The algorithm fuses these estimates with odometry from the entire sequence in 150 ms to obtain the trajectory of the kart.
{"title":"Computing MAP trajectories by representing, propagating and combining PDFs over groups","authors":"Paul Smith, T. Drummond, K. Roussopoulos","doi":"10.1109/ICCV.2003.1238637","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238637","url":null,"abstract":"This paper addresses the problem of computing the trajectory of a camera from sparse positional measurements that have been obtained from visual localisation, and dense differential measurements from odometry or inertial sensors. A fast method is presented for fusing these two sources of information to obtain the maximum a posteriori estimate of the trajectory. A formalism is introduced for representing probability density functions over Euclidean transformations, and it is shown how these density functions can be propagated along the data sequence and how multiple estimates of a transformation can be combined. A three-pass algorithm is described which makes use of these results to yield the trajectory of the camera. Simulation results are presented which are validated against a physical analogue of the vision problem, and results are then shown from sequences of approximately 1,800 frames captured from a video camera mounted on a go-kart. Several of these frames are processed using computer vision to obtain estimates of the position of the go-kart. The algorithm fuses these estimates with odometry from the entire sequence in 150 ms to obtain the trajectory of the kart.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129176104","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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