Browsing by Author "Tai, Yen-Chou"

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A Fully Automatic Approach for Fisheye Camera Calibration
(2018-01-01)
Tai, Yen-Chou
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Hsieh, Yi-Yu
;
Chuang, Jen-Hui
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資訊工程學系
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Department of Computer Science
An automatic calibration procedure for a fisheye camera is presented in this paper by employing a flat panel monitor. The procedure does not require precise camera-monitor alignment, and any manual input of data or commands, making it useful for factory automation for mass production of such cameras. The fully automatic calibration procedure, which requires the generation of various test patterns on the display, and analysis of fisheye images of these patterns, consists of the following steps: (i) estimate the image center of the camera, (ii) identify the line on the monitor which intersects optical axis of the camera perpendicularly, and (iii) along the above line, obtain calibration data needed in de-warping the fisheye image. Experimental results demonstrate that the proposed approach performs satisfactorily in terms of effectiveness and accuracy.
A novel point cloud registration using 2D image features
(2017-01-07)
Lin, Chien-Chou
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Tai, Yen-Chou
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Lee, Jhong-Jin
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Chen, Yong-Sheng
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資訊工程學系
Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.
Efficient Calibration for Multi-plane Homography Using a Laser Level
(2015)
Tai, Yen-Chou
;
Liu, Chin-Wei
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Chen, Yong-Sheng
;
Chuang, Jen-Hui
;
資訊工程學系
An efficient calibration method for multi-plane homography is proposed in this paper. Two laser levels are used to cast laser lines to construct virtual poles in the environment without deploying real objects. HSV color model, Hough transform, and least squares method are applied to locate the laser lines in the captured images. Using the features of vanishing line and the view-invariant cross-ratio model, the 3-D coordinate of the camera can be estimated. The multi-plane homography between camera image and the world space can be derived based on two layers homography. The first layer homography relates the ground and the image, whereas the second layer homography can be efficiently obtained using the first layer homography and the virtual poles. Experimental results show that the virtual poles and the derived homography are both accurately estimated.