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Proceedings Paper

An improved ASIFT algorithm for indoor panorama image matching
Author(s): Han Fu; Donghai Xie; Ruofei Zhong; Yu Wu; Qiong Wu
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Paper Abstract

The generation of 3D models for indoor objects and scenes is an attractive tool for digital city, virtual reality and SLAM purposes. Panoramic images are becoming increasingly more common in such applications due to their advantages to capture the complete environment in one single image with large field of view. The extraction and matching of image feature points are important and difficult steps in three-dimensional reconstruction, and ASIFT is a state-of-the-art algorithm to implement these functions. Compared with the SIFT algorithm, more feature points can be generated and the matching accuracy of ASIFT algorithm is higher, even for the panoramic images with obvious distortions. However, the algorithm is really time-consuming because of complex operations and performs not very well for some indoor scenes under poor light or without rich textures. To solve this problem, this paper proposes an improved ASIFT algorithm for indoor panoramic images: firstly, the panoramic images are projected into multiple normal perspective images. Secondly, the original ASIFT algorithm is simplified from the affine transformation of tilt and rotation with the images to the only tilt affine transformation. Finally, the results are re-projected to the panoramic image space. Experiments in different environments show that this method can not only ensure the precision of feature points extraction and matching, but also greatly reduce the computing time.

Paper Details

Date Published: 21 July 2017
PDF: 8 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201C (21 July 2017); doi: 10.1117/12.2281615
Show Author Affiliations
Han Fu, Capital Normal Univ. (China)
Donghai Xie, Capital Normal Univ. (China)
Ruofei Zhong, Capital Normal Univ. (China)
Yu Wu, Institute of Remote Sensing and Digital Earth (China)
Qiong Wu, Capital Normal Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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