Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (6): 65-80.doi: 10.3969/j.issn.1000-5641.2021.06.008

• Computer Science • Previous Articles     Next Articles

A fast key points matching method for high resolution images of a planar mural

Xinye ZHANG1, Weiqing TONG1,2,*(), Haisheng LI1   

  1. 1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
    2. Shanghai Commercial Digital Printing Co., Ltd, Shanghai 200041, China
  • Received:2020-06-14 Online:2021-11-25 Published:2021-11-26
  • Contact: Weiqing TONG


Existing methods of key points matching were invented for grayscale images and are not suitable for high resolution images. Mural images typically have very high resolution, and there may be areas with the same gray textures and different colors. For this special kind of image, this paper proposes a high-speed algorithm of key points matching for high-resolution mural images (NeoKPM for short). NeoKPM has two main innovations: (1) first, the homography matrix of rough registration for the original image is obtained by downsampling the image, which substantially reduces the time required for key points matching; (2) second, a feature descriptor based on gray and color invariants is proposed, which can distinguish different colors of texture with the same gray level, thereby improving the correctness of key points matching. In this paper, the performance of the NeoKPM algorithm is tested on a real mural image library. The experimental results show that on mural images with a resolution of 80 million pixels, the number of correct matching points per pair of images is nearly 100 000 points higher than that of the SIFT (Scale Invariant Feature Transform) algorithm, the processing speed of key points matching is more than 20 times faster than that of the SIFT algorithm, and the average error of dual images based on a single pixel of the image is less than 0.04 pixels.

Key words: mural digitizing, key points matching, brute force matching, color invariance

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