IKDSIFT: An Improved Keypoint Detection Algorithm Based-on SIFT Approach for Non-uniform Illumination

摘要

In this paper, we propose an improved keypoint detection algorithm of object-based recognition for non-uniform illumination, called IKDSIFT, which is implemented using the SIFT approach, morphological operations, Top-Hat filtering and various techniques in pre-processing procedures. The number of keypoint rate of data sets was compared. Data sets consist of three hundred 150x150 images and thirty 851x566 images with different uniform and non-uniform illumination. The experimental results show that the number of keypoint detection is reciprocal to peak selection thresholds. The best algorithm is the proposed IKDSIFT, followed by the SIFT. The ASIFT performs the worst. Additionally, the SIFT and ASIFT can detect some peak selection thresholds while the IKDSIFT can detect all ranges of the peak and obtains the best result comparing to other ones. Hence, the proposed algorithm looks promising to be used for recognizing under non-uniform illumination.