The Experts below are selected from a list of 223050 Experts worldwide ranked by ideXlab platform
Katsushi Ikeuchi - One of the best experts on this subject based on the ideXlab platform.
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acquiring a radiance distribution to superimpose virtual objects onto a real scene
Modelling from reality, 2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a real scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a real scene automatically and use it for superimposing virtual objects appropriately onto a real scene. First, a Geometric Model of the scene is constructed from a pair of omni-directional images by using an omni-directional stereo algorithm. Then radiance of the scene is computed from a sequence of omni-directional images taken with different shutter speeds and mapped onto the constructed Geometric Model. The radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the real scene. We successfully tested the proposed method by using real images to show its effectiveness.
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acquiring a radiance distribution to superimpose virtual objects onto a real scene
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a real scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a real scene automatically and use it for superimposing virtual objects appropriately onto a real scene. First, a Geometric Model of the scene is constructed from a pair of omnidirectional images by using an omnidirectional stereo algorithm. Then, radiance of the scene is computed from a sequence of omnidirectional images taken with different shutter speeds and mapped onto the constructed Geometric Model. The radiance distribution mapped onto the Geometric Model is used for rendering virtual objects superimposed onto the scene image. As a result, even for a complex radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the real scene. We successfully tested the proposed method by using real images to show its effectiveness.
Imari Sato - One of the best experts on this subject based on the ideXlab platform.
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acquiring a radiance distribution to superimpose virtual objects onto a real scene
Modelling from reality, 2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a real scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a real scene automatically and use it for superimposing virtual objects appropriately onto a real scene. First, a Geometric Model of the scene is constructed from a pair of omni-directional images by using an omni-directional stereo algorithm. Then radiance of the scene is computed from a sequence of omni-directional images taken with different shutter speeds and mapped onto the constructed Geometric Model. The radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the real scene. We successfully tested the proposed method by using real images to show its effectiveness.
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acquiring a radiance distribution to superimpose virtual objects onto a real scene
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a real scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a real scene automatically and use it for superimposing virtual objects appropriately onto a real scene. First, a Geometric Model of the scene is constructed from a pair of omnidirectional images by using an omnidirectional stereo algorithm. Then, radiance of the scene is computed from a sequence of omnidirectional images taken with different shutter speeds and mapped onto the constructed Geometric Model. The radiance distribution mapped onto the Geometric Model is used for rendering virtual objects superimposed onto the scene image. As a result, even for a complex radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the real scene. We successfully tested the proposed method by using real images to show its effectiveness.
Yoichi Sato - One of the best experts on this subject based on the ideXlab platform.
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acquiring a radiance distribution to superimpose virtual objects onto a real scene
Modelling from reality, 2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a real scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a real scene automatically and use it for superimposing virtual objects appropriately onto a real scene. First, a Geometric Model of the scene is constructed from a pair of omni-directional images by using an omni-directional stereo algorithm. Then radiance of the scene is computed from a sequence of omni-directional images taken with different shutter speeds and mapped onto the constructed Geometric Model. The radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the real scene. We successfully tested the proposed method by using real images to show its effectiveness.
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acquiring a radiance distribution to superimpose virtual objects onto a real scene
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a real scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a real scene automatically and use it for superimposing virtual objects appropriately onto a real scene. First, a Geometric Model of the scene is constructed from a pair of omnidirectional images by using an omnidirectional stereo algorithm. Then, radiance of the scene is computed from a sequence of omnidirectional images taken with different shutter speeds and mapped onto the constructed Geometric Model. The radiance distribution mapped onto the Geometric Model is used for rendering virtual objects superimposed onto the scene image. As a result, even for a complex radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the real scene. We successfully tested the proposed method by using real images to show its effectiveness.
Richard M Eustice - One of the best experts on this subject based on the ideXlab platform.
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Pose-graph visual SLAM with Geometric Model selection for autonomous underwater ship hull inspection
2009 IEEE RSJ International Conference on Intelligent Robots and Systems IROS 2009, 2009Co-Authors: Ayoung Kim, Richard M EusticeAbstract:This paper reports the application of vision based simultaneous localization and mapping (SLAM) to the problem of autonomous ship hull inspection by an underwater vehicle. The goal of this work is to automatically map and navigate the underwater surface area of a ship hull for foreign object detection and maintenance inspection tasks. For this purpose we employ a pose-graph SLAM algorithm using an extended information filter for inference. For perception, we use a calibrated monocular camera system mounted on a tilt actuator so that the camera approximately maintains a nadir view to the hull. A combination of SIFT and Harris features detectors are used within a pairwise image registration framework to provide camera-derived relative-pose constraints (modulo scale). Because the ship hull surface can vary from being locally planar to highly three-dimensional (e.g., screws, rudder), we employ a Geometric Model selection framework to appropriately choose either an essential matrix or homography registration Model during image registration. This allows the image registration engine to exploit geometry information at the early stages of estimation, which results in better navigation and structure reconstruction via more accurate and robust camera-constraints. Preliminary results are reported for mapping a 1,300 image data set covering a 30 m by 5 m section of the hull of a USS aircraft carrier. The post-processed result validates the algorithm's potential to provide in-situ navigation in the underwater environment for trajectory control, while generating a texture-mapped 3D Model of the ship hull as a byproduct for inspection.
Hanzi Wang - One of the best experts on this subject based on the ideXlab platform.
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conceptual space based gross outlier removal for Geometric Model fitting
International Conference on Control Automation Robotics and Vision, 2016Co-Authors: Xing Wang, Jin Zheng, Guobao Xiao, Yan Yan, Hanzi WangAbstract:In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for Geometric Model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" Model hypotheses. In the conceptual space, the distributions of inliers and gross outliers are significantly different. Specifically, inliers of each Model instance are distributed in a subspace and they are far away from the origin of the conceptual space, while gross outliers are distributed near the origin. In this manner, the problem of densely gross outlier removal is formulated as a binary classification problem. The main advantage of the proposed method is that it can handle data with a large proportion of outliers and effectively remove gross outliers in data. Experimental results on both synthetic and real data have demonstrated the efficiency and effectiveness of the proposed method.
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ICARCV - Conceptual space based gross outlier removal for Geometric Model fitting
2016 14th International Conference on Control Automation Robotics and Vision (ICARCV), 2016Co-Authors: Xing Wang, Jin Zheng, Guobao Xiao, Yan Yan, Hanzi WangAbstract:In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for Geometric Model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" Model hypotheses. In the conceptual space, the distributions of inliers and gross outliers are significantly different. Specifically, inliers of each Model instance are distributed in a subspace and they are far away from the origin of the conceptual space, while gross outliers are distributed near the origin. In this manner, the problem of densely gross outlier removal is formulated as a binary classification problem. The main advantage of the proposed method is that it can handle data with a large proportion of outliers and effectively remove gross outliers in data. Experimental results on both synthetic and real data have demonstrated the efficiency and effectiveness of the proposed method.
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an outlier removal method by statistically analyzing hypotheses for Geometric Model fitting
International Conference on Image and Graphics, 2015Co-Authors: Guobao Xiao, Hanzi WangAbstract:In this paper, we propose an outlier removal method which utilizes the information of hypotheses for Model fitting. The proposed method statistically analyzes the properties of data points in two groups of hypotheses, i.e., “good hypotheses” and “bad hypotheses”. We show that the bad hypotheses, whose parameters are far from the parameters of Model instances in data, also contain the correlation information between data points. The information can be used to effectively remove outliers from the data. Experimental results show the proposed method can effectively remove outliers on real datasets.
