Open AccessGround target localization and recognition via descriptors fusion
Author(s) -
Mohamed Kamel,
Hatem Taha,
Gouda I. Salama,
Yehia Z. Elhalwagy
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/610/1/012015
Subject(s) - artificial intelligence , ransac , computer science , discriminative model , pattern recognition (psychology) , computer vision , scale invariant feature transform , feature extraction , matching (statistics) , image (mathematics) , mathematics , statistics
Keypoint matching can be defined as locating the position of a particular point in two images precisely. Recently, keypoint descriptors have taken a great effect targeting to be powerfully invariant to rotation, scale and translation for improving target detection. The detection task is carried out by a reference-scene image matching to localize the desired target in the input scene. An innovative approach is proposed in this work to fuse the state-of-the-art feature descriptors ORB, BRISK for the sake of accurate ground target detection in two phases. Firstly, off-line phase, where the fused features are extracted from different perspective, azimuth angles of the desired target to build a comprehensive reference image representation. Secondly, on-line phase, where the fused features extraction task is carried out from the whole scene. Hence, it is matched with the stored reference one to find the keypoints correspondence. The outliers’ problem is eliminated using Random Sample Consensus (RANSAC) algorithm resulting in speeding up the matching procedure. The conducted comparative analysis has revealed the discriminative power of the fused features in localization and recognition tasks while keeping the proposed system works in real-time.