The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform
Rinichiro Taniguchi - One of the best experts on this subject based on the ideXlab platform.
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shape and Pose Parameter estimation of 3d multi part objects
Asian Conference on Computer Vision, 1998Co-Authors: Satoshi Yonemoto, Naoyuki Tsuruta, Rinichiro TaniguchiAbstract:This paper presents an analysis-by-image-synthesis framework of shape and Pose estimation of 3D multi-part. objects, whose purPose is to map objects in the real world into virtual environments. In general, complex 3D multi-part objects cause serious self-occlusion and non-rigid motion. To deal with the occlusion among them, we employ both multiple calibrated cameras and time-varying sequences, since there is enough information to estimate the Parameters in the sensory data. In our framework, to minimize the error between the selected measurements and the estimated model Parameters, we proceed model fitting process based on proper gradient-based minimization.
Satoshi Yonemoto - One of the best experts on this subject based on the ideXlab platform.
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shape and Pose Parameter estimation of 3d multi part objects
Asian Conference on Computer Vision, 1998Co-Authors: Satoshi Yonemoto, Naoyuki Tsuruta, Rinichiro TaniguchiAbstract:This paper presents an analysis-by-image-synthesis framework of shape and Pose estimation of 3D multi-part. objects, whose purPose is to map objects in the real world into virtual environments. In general, complex 3D multi-part objects cause serious self-occlusion and non-rigid motion. To deal with the occlusion among them, we employ both multiple calibrated cameras and time-varying sequences, since there is enough information to estimate the Parameters in the sensory data. In our framework, to minimize the error between the selected measurements and the estimated model Parameters, we proceed model fitting process based on proper gradient-based minimization.
Naoyuki Tsuruta - One of the best experts on this subject based on the ideXlab platform.
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shape and Pose Parameter estimation of 3d multi part objects
Asian Conference on Computer Vision, 1998Co-Authors: Satoshi Yonemoto, Naoyuki Tsuruta, Rinichiro TaniguchiAbstract:This paper presents an analysis-by-image-synthesis framework of shape and Pose estimation of 3D multi-part. objects, whose purPose is to map objects in the real world into virtual environments. In general, complex 3D multi-part objects cause serious self-occlusion and non-rigid motion. To deal with the occlusion among them, we employ both multiple calibrated cameras and time-varying sequences, since there is enough information to estimate the Parameters in the sensory data. In our framework, to minimize the error between the selected measurements and the estimated model Parameters, we proceed model fitting process based on proper gradient-based minimization.
Christoph Von Der Malsburg - One of the best experts on this subject based on the ideXlab platform.
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Analysis and Synthesis of Pose Variations of Human Faces by a Linear PCMAP Model and its Application for Pose-Invariant Face Recognition System
2000Co-Authors: Kazunori Okada, Shigeru Akamatsu, Christoph Von Der MalsburgAbstract:A method of manifold representation for human faces with Pose variations is proPosed. Our model consists of mappings between 3D head angles and facial images separately represented in shape and texture, via sub-space models spanned by principal components (PCs). Explicit mappings to and from 3D head angles are used as processes of Pose estimation and transformation, respectively. Generalization capability to unknown head Poses enables our model to continuously cover Pose Parameter space, providing high approximation accuracy. The feasibility of this model is evaluated in a number of experiments. We also proPose a novel Pose-invariant face recognition system using our model as the entry format for a gallery of known persons. Experimental results with 3D facial models recorded by a Cyberware scanner show that our model provides a superior recognition performance against Pose variations, and that texture synthesis process is carried out correctly.
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Analysis and synthesis of Pose variations of human faces by a linear pcmap model and its application for Pose–invariant face recognition system
2000Co-Authors: Kazunori Okada, Shigeru Akamatsu, Christoph Von Der MalsburgAbstract:A method of manifold representation for human faces with Pose variations is proPosed. Our model consists of mappings between 3D head angles and facial images sepa-rately represented in shape and texture, via sub-space mod-els spanned by principal components (PCs). Explicit map-pings to and from 3D head angles are used as processes of Pose estimation and transformation, respectively. Gen-eralization capability to unknown head Poses enables our model to continuously cover Pose Parameter space, pro-viding high approximation accuracy. The feasibility of this model is evaluated in a number of experiments. We also pro-Pose a novel Pose-invariant face recognition system using our model as the entry format for a gallery of known per-sons. Experimental results with 3D facial models recorded by a Cyberware scanner show that our model provides a superior recognition performance against Pose variations, and that texture synthesis process is carried out correctly.
Shengcai Liao - One of the best experts on this subject based on the ideXlab platform.
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coarse to fine statistical shape model by bayesian inference
Asian Conference on Computer Vision, 2007Co-Authors: Ran He, Stan Z Li, Shengcai LiaoAbstract:In this paper, we take a predefined geometry shape as a constraint for accurate shape alignment. A shape model is divided in two parts: fixed shape and active shape. The fixed shape is a user-predefined simple shape with only a few landmarks which can be easily and accurately located by machine or human. The active one is comPosed of many landmarks with complex shape contour. When searching an active shape, Pose Parameter is calculated by the fixed shape. Bayesian inference is introduced to make the whole shape more robust to local noise generated by the active shape, which leads to a compensation factor and a smooth factor for a coarse-to-fine shape search. This method provides a simple and stable means for online and offline shape analysis. Experiments on cheek and face contour demonstrate the effectiveness of our proPosed approach.