TY - GEN
T1 - Fish-Eye Lenses-Based Camera Calibration and Panoramic Image Stitching
AU - Hsu, Chao Yung
AU - Chang, Chih Ming
AU - Kang, Li Wei Kang
AU - Fu, Ru Hong
AU - Chen, Duan Yu
AU - Weng, Ming Fang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - Fish-eye lenses are common in several computer vision applications, such as four-camera surround view driver assistance, where a very wide angle (e.g., 180 degrees) of view is available. Nevertheless, their applicability is usually limited by the lack of an accurate and easy-to-use calibration procedure. In this paper, we present a camera calibration method for fish-eye lenses and a panoramic image stitching framework for calibrated surround images. To achieve the calibration of fish-eye captured images, it only requires to observe a reference planar pattern (e.g., chessboard), followed by offline estimating extrinsic and intrinsic parameters and save the related parameters. Each fish-eye distorted image can then be efficiently online corrected. Then, each calibrated image is transformed to its top-down view (or bird's-eye view) via the perspective transformation based on the estimated homography matrix. As a result, these surround bird'seye view images can be stitched to generate the final panoramic image. It is expected that the proposed framework would be applicable to AVM (around view monitoring) system or ADAS (advanced driver assistance system) of vehicles in the future.
AB - Fish-eye lenses are common in several computer vision applications, such as four-camera surround view driver assistance, where a very wide angle (e.g., 180 degrees) of view is available. Nevertheless, their applicability is usually limited by the lack of an accurate and easy-to-use calibration procedure. In this paper, we present a camera calibration method for fish-eye lenses and a panoramic image stitching framework for calibrated surround images. To achieve the calibration of fish-eye captured images, it only requires to observe a reference planar pattern (e.g., chessboard), followed by offline estimating extrinsic and intrinsic parameters and save the related parameters. Each fish-eye distorted image can then be efficiently online corrected. Then, each calibrated image is transformed to its top-down view (or bird's-eye view) via the perspective transformation based on the estimated homography matrix. As a result, these surround bird'seye view images can be stitched to generate the final panoramic image. It is expected that the proposed framework would be applicable to AVM (around view monitoring) system or ADAS (advanced driver assistance system) of vehicles in the future.
UR - http://www.scopus.com/inward/record.url?scp=85053933574&partnerID=8YFLogxK
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U2 - 10.1109/ICCE-China.2018.8448987
DO - 10.1109/ICCE-China.2018.8448987
M3 - Conference contribution
AN - SCOPUS:85053933574
SN - 9781538663011
T3 - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
BT - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
Y2 - 19 May 2018 through 21 May 2018
ER -