Browsing by Author "Lai, CC"

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A Bayesian approach to video object segmentation via merging 3-D watershed volumes
(2005-01-01)
Tsai, YP
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Lai, CC
;
Hung, YP
;
Shih, ZC
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資訊工程學系
In this letter, we propose a Bayesian approach to video object segmentation. Our method consists of two stages. In the first stage, we partition the video data into a set of three-dimensional (3-D) watershed volumes, where each watershed volume is a series of corresponding two-dimensional (2-D) image regions. These 2-D image regions are obtained by applying to each image frame the marker-controlled watershed segmentation, where the markers are extracted by first generating a set of initial markers via temporal tracking and then refining the markers with two shrinking schemes: the iterative adaptive erosion and the verification against a presimplified watershed segmentation. Next, in the second stage, we use a Markov random field to model the spatio-temporal relationship among the 3-D watershed volumes that are obtained from the first stage. Then, the desired video objects can be extracted by merging watershed volumes having similar motion characteristics within a Bayesian framework. A major advantage of this method is that it can take into account the global motion information contained in each watershed volume. Our experiments have shown that the proposed method has potential for extracting moving objects from a video sequence.
Estimation of moving vehicle locations using wheel shape information in single 2-D lateral vehicle images by 3-D computer vision techniques
(1999-04-01)
Lai, CC
;
Tsai, WH
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資訊工程學系
;
Department of Computer Science
an approach to the estimation of moving lateral vehicle locations for driving assistance using wheel shape information in single 2-D vehicle images by 3-D computer vision techniques is proposed. The location scheme is supposed to be performed on a vehicle with a camera mounted on the front bumper. An analytical solution is applied to estimate locations of the lateral vehicle. Firstly, the rear wheel shape of a lateral vehicle moving in a nearby lane is imaged. By using the Hough transform, the projected wheel shape, which is an ellipse, is detected. Secondly. the equation of the detected ellipse is used to infer the orientation angle of the lateral vehicle with respect to the camera view direction. Finally, the center of the ellipse shape is used to determine the relative position of the lateral vehicle with respect to the camera lens center, Moreover, an edge-point verification algorithm is utilized to extract the ellipse shape more precisely in the image processing stage. Both computer simulated and real images are tested and good experimental results show the effectiveness of the proposed approach for estimating lateral vehicle locations. The results are useful for driving assistance and vehicle collision avoidance and are discussed in detail. (C) 1999 Elsevier Science Ltd. All rights reserved.
Location estimation and trajectory prediction of moving lateral vehicle using two wheel shapes information in 2-D lateral vehicle images by 3-D computer vision techniques
(2003)
Lai, CC
;
Tsai, WH
;
資訊工程學系
;
Department of Computer Science
A novel approach to location estimation and trajectory prediction of moving lateral vehicle locations for driving assistance using wheels shape information in single 2-D vehicle images by 3-D computer vision techniques is proposed. The location scheme is supposed to be performed on a vehicle with a camera mounted on the front bumper. An analytical solution is applied to estimate the location of a lateral vehicle. Firstly, the normal vectors of the car wheels in the camera coordinate system as well as the projections of the wheel centers in the image plane are derived. Then orientation angle of the lateral vehicle with respect to the driving vehicle is derived from the two vectors. Next, the image projections of the contact points of the wheels with the ground are detected. Finally, the image positions of the contact points are used to determine the 3D relative position of the moving vehicle with respect to the camera coordinate system by the back-projection principle. After the location of a moving lateral vehicle is obtained, a new approach is proposed to predict the trajectory of a lateral vehicle by using the spatial information of the two wheels in a single image. Real images are tested and experimental results show the effectiveness of the proposed approaches. The results are useful for car driving assistance and vehicle collision avoidance.