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Scale-invariant line descriptors for wide baseline matching

Bart Verhagen and Radu Timofte and Luc Van Gool
IEEE Winter Conference on Applications of Computer Vision (WACV)
March 2014

Abstract

In this paper we propose a method to add scale-invariance to line descriptors for wide baseline matching purposes. While finding point correspondences among different views is a well-studied problem, there still remain difficult cases where it performs poorly, such as textureless scenes, ambiguities and extreme transformations. For these cases using line segment correspondences is a valuable addition for finding sufficient matches. Our general method for adding scale-invariance to line segment descriptors consist of 5 basic rules. We apply these rules to enhance both the line descriptor described by Bay et al. [1] and the mean-standard deviation line descriptor (MSLD) proposed by Wang et al. [14]. Moreover, we examine the effect of the line descriptors when combined with the topological filtering method proposed by Bay et al. and the recent proposed graph matching strategy from K-VLD [6]. We validate the method using standard point correspondence benchmarks and more challenging new ones. Adding scale-invariance increases the accuracy when confronted with big scale changes and increases the number of inliers in the general case, both resulting in smaller calibration errors by means of RANSAC-like techniques and epipolar estimations.


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@InProceedings{eth_biwi_01164,
  author = {Bart Verhagen and Radu Timofte and Luc Van Gool},
  title = {Scale-invariant line descriptors for wide baseline matching},
  booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year = {2014},
  month = {March},
  pages = {493-500},
  publisher = {IEEE},
  keywords = {}
}