Open AccessA SIFT Description Approach for Non-Uniform Illumination and Other Invariants
Author(s) -
Krittachai Boonsiva,
Worawat Sa-ngiamvibool
Publication year - 2021
Publication title -
ingénierie des systèmes d'information/ingénierie des systèmes d'information
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.161
H-Index - 8
eISSN - 2116-7125
pISSN - 1633-1311
DOI - 10.18280/isi.260603
Subject(s) - scale invariant feature transform , artificial intelligence , rotation (mathematics) , sensitivity (control systems) , computer vision , set (abstract data type) , moment (physics) , computer science , image (mathematics) , mathematics , pattern recognition (psychology) , physics , classical mechanics , electronic engineering , engineering , programming language
The new improvement keypoint description technique of image-based recognition for rotation, viewpoint and non-uniform illumination situations is presented. The technique is relatively simple based on two procedures, i.e., the keypoint detection and the keypoint description procedure. The keypoint detection procedure is based on the SIFT approach, Top-Hat filtering, morphological operations and average filtering approach. Where this keypoint detection procedure can segment the targets from uneven illumination particle images. While the keypoint description procedures are described and implemented using the Hu moment invariants. Where the central moments are being unchanged under image translations. The sensitivity, accuracy and precision rate of data sets were evaluated and compared. The data set are provided by color image database with variants uniform and non-uniform illumination, viewpoint and rotation changes. The evaluative results show that the approach is superior to the other SIFTs in terms of uniform illumination, non-uniform illumination and other situations. Additionally, the paper demonstrates the high sensitivity of 100%, high accuracy of 83.33% and high precision rate of 80.00%. Comparisons to other SIFT approaches are also included.