The Experts below are selected from a list of 16599 Experts worldwide ranked by ideXlab platform
Naokazu Yokoya - One of the best experts on this subject based on the ideXlab platform.
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mobile augmented reality real time and accurate extrinsic Camera Parameter estimation using feature landmark database for augmented reality
Computers & Graphics, 2011Co-Authors: Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:In the field of augmented reality (AR), many kinds of vision-based extrinsic Camera Parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based Camera Parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated Camera Parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based Camera Parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast Camera Parameter estimation by tentative Camera Parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.
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extrinsic Camera Parameter estimation using video images and gps considering gps positioning accuracy
International Conference on Pattern Recognition, 2010Co-Authors: Hideyuki Kume, Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:This paper proposes a method for estimating extrinsic Camera Parameters using video images and position data acquired by GPS. In conventional methods, the accuracy of the estimated Camera position largely depends on the accuracy of GPS positioning data because they assume that GPS position error is very small or normally distributed. However, the actual error of GPS positioning easily grows to the 10m level and the distribution of these errors is changed depending on satellite positions and conditions of the environment. In order to achieve more accurate Camera positioning in outdoor environments, in this study, we have employed a simple assumption that true GPS position exists within a certain range from the observed GPS position and the size of the range depends on the GPS positioning accuracy. Concretely, the proposed method estimates Camera Parameters by minimizing an energy function that is defined by using the reprojection error and the penalty term for GPS positioning.
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real time Camera position and posture estimation using a feature landmark database with priorities
International Conference on Pattern Recognition, 2008Co-Authors: Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:In the field of computer vision, many kinds of Camera Parameter estimation methods have been proposed. As one of these methods, an extrinsic Camera Parameter estimation method that uses pre-constructed feature landmark database has been studied. In this method, extrinsic Camera Parameters of video images are estimated from correspondences between landmarks and image features. Although this method can work in a large outdoor environment, its computational cost in matching process is expensive and it cannot work in real-time. In this paper, to achieve real-time Camera Parameter estimation, the number of matching candidates are reduced by using priorities of landmarks that are determined from previously captured video sequences.
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super resolved video mosaicing for documents based on extrinsic Camera Parameter estimation
Lecture Notes in Computer Science, 2006Co-Authors: Akihiko Iketani, Tomokazu Sato, Sei Ikeda, Masayuki Kanbara, Noboru Nakajima, Naokazu YokoyaAbstract:This paper describes a novel video mosaicing method based on extrinsic Camera Parameter estimation. With our method, a mosaic image without perspective distortion can be generated, even if none of the input image plane is parallel to the target document. Thus, users no longer have to take special care in holding the Camera so that the image plane in the reference frame is parallel to the target. First, extrinsic Camera Parameters are estimated by tracking image features. Next, by utilizing re-appearing features, estimated extrinsic Camera Parameters are globally optimized to minimize the estimation error in the whole input sequence. Finally, all the images are projected onto the mosaic image plane, and a super-resolved mosaic image is generated by applying an iterative back projection algorithm. Experiments have successfully demonstrated the feasibility of the proposed method.
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extrinsic Camera Parameter estimation based on feature tracking and gps data
Lecture Notes in Computer Science, 2006Co-Authors: Yuji Yokochi, Tomokazu Sato, Sei Ikeda, Naokazu YokoyaAbstract:This paper describes a novel method for estimating extrinsic Camera Parameters using both feature points on an image sequence and sparse position data acquired by GPS. Our method is based on a structure-from-motion technique but is enhanced by using GPS data so as to minimize accumulative estimation errors. Moreover, the position data are also used to remove mis-tracked features. The proposed method allows us to estimate extrinsic Parameters without accumulative errors even from an extremely long image sequence. The validity of the method is demonstrated through experiments of estimating extrinsic Parameters for both synthetic and real outdoor scenes.
Jean Ponce - One of the best experts on this subject based on the ideXlab platform.
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Accurate Camera calibration from multi-view stereo and bundle adjustment
International Journal of Computer Vision, 2009Co-Authors: Yasutaka Furukawa, Jean PonceAbstract:The advent of high-resolution digital Cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on Camera calibration to fulfill these promises. This paper presents a novel approach to Camera calibration where top-down information from rough Camera Parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve Camera calibration Parameters using a standard bundle ad-justment algorithm (Lourakis and Argyros 2008). The pro-posed method has been tested on six real datasets including objects without salient features for which image correspon-dences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in Camera Parameters. Three different methods have been used to qual-itatively assess the improvements of the Camera Parameters. The implementation of the proposed algorithm is publicly available at Furukawa and Ponce (2008b).
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accurate Camera calibration from multi view stereo and bundle adjustment
Computer Vision and Pattern Recognition, 2008Co-Authors: Yasutaka Furukawa, Jean PonceAbstract:The advent of high-resolution digital Cameras and sophisticated multi-view stereo algorithms offers the promises of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on Camera calibration to fulfill these promises. This paper presents a novel approach to Camera calibration where top-down information from rough Camera Parameter estimates and the output of a publicly available multiview-stereo system (Furukawa et al.) on scaled-down input images are used to effectively guide the search for additional image correspondences and significantly improve Camera calibration Parameters using a standard bundle adjustment algorithm (Lourakis et al.). The proposed method has been tested on several real datasets-including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and image-based modeling tasks-including the construction of visual hulls where thin structures are lost without our calibration procedure.
Tomokazu Sato - One of the best experts on this subject based on the ideXlab platform.
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mobile augmented reality real time and accurate extrinsic Camera Parameter estimation using feature landmark database for augmented reality
Computers & Graphics, 2011Co-Authors: Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:In the field of augmented reality (AR), many kinds of vision-based extrinsic Camera Parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based Camera Parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated Camera Parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based Camera Parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast Camera Parameter estimation by tentative Camera Parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.
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extrinsic Camera Parameter estimation using video images and gps considering gps positioning accuracy
International Conference on Pattern Recognition, 2010Co-Authors: Hideyuki Kume, Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:This paper proposes a method for estimating extrinsic Camera Parameters using video images and position data acquired by GPS. In conventional methods, the accuracy of the estimated Camera position largely depends on the accuracy of GPS positioning data because they assume that GPS position error is very small or normally distributed. However, the actual error of GPS positioning easily grows to the 10m level and the distribution of these errors is changed depending on satellite positions and conditions of the environment. In order to achieve more accurate Camera positioning in outdoor environments, in this study, we have employed a simple assumption that true GPS position exists within a certain range from the observed GPS position and the size of the range depends on the GPS positioning accuracy. Concretely, the proposed method estimates Camera Parameters by minimizing an energy function that is defined by using the reprojection error and the penalty term for GPS positioning.
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real time Camera position and posture estimation using a feature landmark database with priorities
International Conference on Pattern Recognition, 2008Co-Authors: Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:In the field of computer vision, many kinds of Camera Parameter estimation methods have been proposed. As one of these methods, an extrinsic Camera Parameter estimation method that uses pre-constructed feature landmark database has been studied. In this method, extrinsic Camera Parameters of video images are estimated from correspondences between landmarks and image features. Although this method can work in a large outdoor environment, its computational cost in matching process is expensive and it cannot work in real-time. In this paper, to achieve real-time Camera Parameter estimation, the number of matching candidates are reduced by using priorities of landmarks that are determined from previously captured video sequences.
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super resolved video mosaicing for documents based on extrinsic Camera Parameter estimation
Lecture Notes in Computer Science, 2006Co-Authors: Akihiko Iketani, Tomokazu Sato, Sei Ikeda, Masayuki Kanbara, Noboru Nakajima, Naokazu YokoyaAbstract:This paper describes a novel video mosaicing method based on extrinsic Camera Parameter estimation. With our method, a mosaic image without perspective distortion can be generated, even if none of the input image plane is parallel to the target document. Thus, users no longer have to take special care in holding the Camera so that the image plane in the reference frame is parallel to the target. First, extrinsic Camera Parameters are estimated by tracking image features. Next, by utilizing re-appearing features, estimated extrinsic Camera Parameters are globally optimized to minimize the estimation error in the whole input sequence. Finally, all the images are projected onto the mosaic image plane, and a super-resolved mosaic image is generated by applying an iterative back projection algorithm. Experiments have successfully demonstrated the feasibility of the proposed method.
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extrinsic Camera Parameter estimation based on feature tracking and gps data
Lecture Notes in Computer Science, 2006Co-Authors: Yuji Yokochi, Tomokazu Sato, Sei Ikeda, Naokazu YokoyaAbstract:This paper describes a novel method for estimating extrinsic Camera Parameters using both feature points on an image sequence and sparse position data acquired by GPS. Our method is based on a structure-from-motion technique but is enhanced by using GPS data so as to minimize accumulative estimation errors. Moreover, the position data are also used to remove mis-tracked features. The proposed method allows us to estimate extrinsic Parameters without accumulative errors even from an extremely long image sequence. The validity of the method is demonstrated through experiments of estimating extrinsic Parameters for both synthetic and real outdoor scenes.
Takafumi Taketomi - One of the best experts on this subject based on the ideXlab platform.
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Camera pose estimation under dynamic intrinsic Parameter change for augmented reality
Computers & Graphics, 2014Co-Authors: Takafumi Taketomi, Kazuya Okada, Goshiro Yamamoto, Jun Miyazaki, Hirokazu KatoAbstract:In this paper, we propose a method for estimating the Camera pose for an environment in which the intrinsic Camera Parameters change dynamically. In video see-through augmented reality (AR) technology, image-based methods for estimating the Camera pose are used to superimpose virtual objects onto the real environment. In general, video see-through-based AR cannot change the image magnification that results from a change in the Camera's field-of-view because of the difficulty of dealing with changes in the intrinsic Camera Parameters. To remove this limitation, we propose a novel method for simultaneously estimating the intrinsic and extrinsic Camera Parameters based on an energy minimization framework. Our method is composed of both online and offline stages. An intrinsic Camera Parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic Camera Parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based method to estimate the Camera Parameters: reprojection errors based on the epipolar constraint and the constraint of the continuity of zoom values. By using a novel energy function, our method can accurately estimate intrinsic and extrinsic Camera Parameters. We confirmed experimentally that the proposed method can achieve accurate Camera Parameter estimation during Camera zooming. Graphical abstractDisplay Omitted HighlightsWe propose an intrinsic and extrinsic Camera Parameter estimation method.We extended a conventional marker-based method by adding two energy terms.Camera Parameters are estimated accurately using new energy function.The effectiveness of our method is shown in simulated and real environments.
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geometric registration for zoomable Camera using epipolar constraint and pre calibrated intrinsic Camera Parameter change
International Symposium on Mixed and Augmented Reality, 2013Co-Authors: Takafumi Taketomi, Kazuya Okada, Goshiro Yamamoto, Jun Miyazaki, Hirokazu KatoAbstract:In general, video see-through based augmented reality (AR) cannot change the magnification of Camera zooming Parameter due to the difficulty of dealing with changes in intrinsic Camera Parameters. To realize the usage of Camera zooming in AR, we propose a novel simultaneous intrinsic and extrinsic Camera Parameter estimation method based on an energy minimization framework. Our method is composed of the online and offline stages. An intrinsic Camera Parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic Camera Parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based Camera Parameter estimation method. One is reprojection errors based on the epipolar constraint. The other is the constraint of continuity of zoom values. By using a novel energy function, our method can estimate accurate intrinsic and extrinsic Camera Parameters. In an experiment, we confirmed that the proposed method can achieve accurate Camera Parameter estimation during Camera zooming.
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mobile augmented reality real time and accurate extrinsic Camera Parameter estimation using feature landmark database for augmented reality
Computers & Graphics, 2011Co-Authors: Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:In the field of augmented reality (AR), many kinds of vision-based extrinsic Camera Parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based Camera Parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated Camera Parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based Camera Parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast Camera Parameter estimation by tentative Camera Parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.
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extrinsic Camera Parameter estimation using video images and gps considering gps positioning accuracy
International Conference on Pattern Recognition, 2010Co-Authors: Hideyuki Kume, Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:This paper proposes a method for estimating extrinsic Camera Parameters using video images and position data acquired by GPS. In conventional methods, the accuracy of the estimated Camera position largely depends on the accuracy of GPS positioning data because they assume that GPS position error is very small or normally distributed. However, the actual error of GPS positioning easily grows to the 10m level and the distribution of these errors is changed depending on satellite positions and conditions of the environment. In order to achieve more accurate Camera positioning in outdoor environments, in this study, we have employed a simple assumption that true GPS position exists within a certain range from the observed GPS position and the size of the range depends on the GPS positioning accuracy. Concretely, the proposed method estimates Camera Parameters by minimizing an energy function that is defined by using the reprojection error and the penalty term for GPS positioning.
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real time Camera position and posture estimation using a feature landmark database with priorities
International Conference on Pattern Recognition, 2008Co-Authors: Takafumi Taketomi, Tomokazu Sato, Naokazu YokoyaAbstract:In the field of computer vision, many kinds of Camera Parameter estimation methods have been proposed. As one of these methods, an extrinsic Camera Parameter estimation method that uses pre-constructed feature landmark database has been studied. In this method, extrinsic Camera Parameters of video images are estimated from correspondences between landmarks and image features. Although this method can work in a large outdoor environment, its computational cost in matching process is expensive and it cannot work in real-time. In this paper, to achieve real-time Camera Parameter estimation, the number of matching candidates are reduced by using priorities of landmarks that are determined from previously captured video sequences.
Hirokazu Kato - One of the best experts on this subject based on the ideXlab platform.
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Camera pose estimation under dynamic intrinsic Parameter change for augmented reality
Computers & Graphics, 2014Co-Authors: Takafumi Taketomi, Kazuya Okada, Goshiro Yamamoto, Jun Miyazaki, Hirokazu KatoAbstract:In this paper, we propose a method for estimating the Camera pose for an environment in which the intrinsic Camera Parameters change dynamically. In video see-through augmented reality (AR) technology, image-based methods for estimating the Camera pose are used to superimpose virtual objects onto the real environment. In general, video see-through-based AR cannot change the image magnification that results from a change in the Camera's field-of-view because of the difficulty of dealing with changes in the intrinsic Camera Parameters. To remove this limitation, we propose a novel method for simultaneously estimating the intrinsic and extrinsic Camera Parameters based on an energy minimization framework. Our method is composed of both online and offline stages. An intrinsic Camera Parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic Camera Parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based method to estimate the Camera Parameters: reprojection errors based on the epipolar constraint and the constraint of the continuity of zoom values. By using a novel energy function, our method can accurately estimate intrinsic and extrinsic Camera Parameters. We confirmed experimentally that the proposed method can achieve accurate Camera Parameter estimation during Camera zooming. Graphical abstractDisplay Omitted HighlightsWe propose an intrinsic and extrinsic Camera Parameter estimation method.We extended a conventional marker-based method by adding two energy terms.Camera Parameters are estimated accurately using new energy function.The effectiveness of our method is shown in simulated and real environments.
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geometric registration for zoomable Camera using epipolar constraint and pre calibrated intrinsic Camera Parameter change
International Symposium on Mixed and Augmented Reality, 2013Co-Authors: Takafumi Taketomi, Kazuya Okada, Goshiro Yamamoto, Jun Miyazaki, Hirokazu KatoAbstract:In general, video see-through based augmented reality (AR) cannot change the magnification of Camera zooming Parameter due to the difficulty of dealing with changes in intrinsic Camera Parameters. To realize the usage of Camera zooming in AR, we propose a novel simultaneous intrinsic and extrinsic Camera Parameter estimation method based on an energy minimization framework. Our method is composed of the online and offline stages. An intrinsic Camera Parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic Camera Parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based Camera Parameter estimation method. One is reprojection errors based on the epipolar constraint. The other is the constraint of continuity of zoom values. By using a novel energy function, our method can estimate accurate intrinsic and extrinsic Camera Parameters. In an experiment, we confirmed that the proposed method can achieve accurate Camera Parameter estimation during Camera zooming.