SILT: scale-invariant line transform

Authors:
Bahador Khaleghi;Malek Baklouti;Fakhreddin O. Karray
Affiliations:
Pattern Analysis and Machine Intelligence Lab, Department of Electrical and Computer Engineering, University of Waterloo;Pattern Analysis and Machine Intelligence Lab, Department of Electrical and Computer Engineering, University of Waterloo;Pattern Analysis and Machine Intelligence Lab, Department of Electrical and Computer Engineering, University of Waterloo
Venue:
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Year:
2009

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Abstract

Line matching is useful in many computer vision tasks such as object recognition, image registration, and 3D reconstruction. The literature on line matching has advanced in recent years, nevertheless, compared to other features (such as point and region matching approaches) it has made little progress. Especially, very few algorithms address the problem of image scaling. In this paper, we present a new line detection and matching algorithm that is invariant to image scale variation (SILT). The algorithm detects line segments as local extrema in the scale-space. Each detected line segment is represented in a distinctive manner using Haar-like features. PCA is further deployed to improve upon the compactness and robustness of representation. Experimental results demonstrate the effectiveness of the proposed approach to deal with image scale variations.