Mr. Zhipeng Deng Profile

Zhipeng Deng

at National Univ of Defense Technology

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Proceedings Article | 15 October 2015 Paper

A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

Zhipeng Deng, Lin Lei, Shilin Zhou

Proceedings Volume 9643, 96431S (2015) https://doi.org/10.1117/12.2194427

KEYWORDS: Image registration, Remote sensing, Feature extraction, Image processing, Image quality, Sensors, Gaussian filters, Image filtering, Detection and tracking algorithms, Image fusion

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Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

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