Browsing by Author "Shih, SW"

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A 3D predictive visual tracker for tracking multiple moving objects with a stereo vision system
(1995)
Hung, YP
;
Tang, CY
;
Shih, SW
;
Chen, Z
;
Lin, WS
This paper presents a 3D feature-based visual tracker for tracking multiple moving objects by using a predictor that first partitions 3D features into different common-motion clusters and then predicts the motion of each cluster with Kalman filters. The 3D features are computed from a sequence of stereo images by combining two 2D temporal matching modules and one stereo correspondence module. To partition the 3D features into different common-motion clusters, we propose a RANSAC-based clustering method by using rigid body consensus which assumes that all the extracted 3D features on a rigid body have the same 3D motion. By using the motion estimates obtained with the RANSAC-based method as the measurements, we are able to use linear Kalman filters to predict the motion of each cluster, and then, to predict the next position of each 3D feature. Preliminary experiments showed that the proposed 3D predictive visual tracker can serve as a robust 3D feature tracker for an active stereo vision system.
A method for calibrating a motorized object rig
(2006)
Huang, PH
;
Tsai, YP
;
Lo, WY
;
Shih, SW
;
Chen, CS
Object Movies (OMs) have been successfully used in many applications. However, the techniques for acquiring OMs still need to be improved if high-quality and efficient OMs are desired. In this paper, we present a method for calibrating a motorized object rig to facilitate the acquisition of OMs. We first apply the CPC kinematic model to formulate the 3D configuration of the device, and then propose a method to estimate the parameters of the CPC model of the device. Furthermore, a visual tool is provided for users to adjust the controllable axes of the rig according to the estimated results. After this calibration, more accurate camera parameters can be obtained and then be used for different purposes. In this work, we use the parameters to reconstruct, from an OM, the 3D model of the object, and then adjust the OM according to the center of the 3D model so that a high-quality OM can be obtained for rendering.