Abstract

Existing feature-based methods for homography estimation require several point correspondences in two images of a planar scene captured from different perspectives. These methods are sensitive to outliers, and their effectiveness depends strongly on the number and accuracy of the specified points. This work presents an iterative method for homography estimation that requires only a single-point correspondence. The homography parameters are estimated by solving a search problem using particle swarm optimization, by maximizing a match score between a projective transformed fragment of the input image using the estimated homography and a matched filter constructed from the reference image, while minimizing the reprojection error. The proposed method can estimate accurately a homography from a single-point correspondence, in contrast to existing methods, which require at least four points. The effectiveness of the proposed method is tested and discussed in terms of objective measures by processing several synthetic and experimental projective transformed images.

© 2022 Optica Publishing Group

Perspective transformation based-initial value estimation for the speckle control points matching in an out-of-focus camera calibration using a synthetic speckle pattern

Xiao Yang, Xiaobo Chen, and Juntong Xi
Opt. Express 30(2) 2310-2325 (2022)

Homography estimation by two PClines Hough transforms and a square-radial checkerboard pattern

Rigoberto Juarez-Salazar and Victor H. Diaz-Ramirez
Appl. Opt. 57(12) 3316-3322 (2018)

Rotation alignment of a camera–IMU system using a single affine correspondence

Yingjian Yu, Banglei Guan, Xiangyi Sun, Zhang Li, and Friedrich Fraundorfer
Appl. Opt. 60(24) 7455-7465 (2021)

Calibration for light field cameras based on fixed point constraint of spatial plane homography

Wang Xuechun, Wang Liang, and Duan Fuqing
Opt. Express 30(14) 24968-24983 (2022)

Efficient object detection and tracking in video sequences

Fadi Dornaika and Fadi Chakik
J. Opt. Soc. Am. A 29(6) 928-935 (2012)