Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 143-149, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-143-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 143-149, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-143-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  09 Jun 2016

09 Jun 2016

DENSE IMAGE MATCHING WITH TWO STEPS OF EXPANSION

Zuxun Zhang, Jia’nan He, Shan Huang, and Yansong Duan Zuxun Zhang et al.
  • School of Remote Sensing and Information Engineering, Wuhan University, No.129 Luoyu Road, Wuhan, China

Keywords: Dense Image Matching, Two Frames, Expansion, Seed-and-Grow, No Geometric Constraint, Adaptive

Abstract. Dense image matching is a basic and key point of photogrammetry and computer version. In this paper, we provide a method derived from the seed-and-grow method, whose basic procedure consists of the following: First, the seed and feature points are extracted, after which the feature points around every seed point are found in the first step of expansion. The corresponding information on these feature points needs to be determined. This is followed by the second step of expansion, in which the seed points around the feature point are found and used to estimate the possible matching patch. Finally, the matching results are refined through the traditional correlation-based method. Our proposed method operates on two frames without geometric constraints, specifically, epipolar constraints. It (1) can smoothly operate on frame, line array, natural scene, and even synthetic aperture radar (SAR) images and (2) at the same time guarantees computing efficiency as a result of the seed-and-grow concept and the computational efficiency of the correlation-based method.