Feature Detection and Matching With Linear Adjustment and Adaptive Thresholding

Abstract

Image feature detection and matching technologies are crucial aspects in machine vision. However it is still facing the dilemma between fast operation for real-time application and robust matching. To address this issue, we propose a robust and relatively fast method for image feature extraction and matching with linear adjustment and adaptive thresholding (LAAT) in this paper. The major challenge of this method is reducing the sensitivity to the brightness. To solve this problem, we adopt brightness and contrast adjustment for image pairs processed by Gaussian filtering. An adaptive thresholding FAST approach is applied for feature selection to improve the performance. The proposed method is compared with the traditional and state-of-the-art extraction methods on public dataset. Particularly, this paper focuses on the illumination change, image blur, and image rotation aspects. Experiments show that our proposed algorithm is superior to other algorithms in the comprehensive evaluation of various parameters, especially for illumination and blur transformations.