Open Access

ARTICLE

Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions

Ibrahim El rube'^*, Sameer Alsharif

Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.o. Box 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: Ibrahim El rube'. Email:

Computer Systems Science and Engineering 2022, 42(1), 407-421. https://doi.org/10.32604/csse.2022.022400

Received 06 August 2021; Accepted 09 September 2021; Issue published 02 December 2021

Abstract

This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing descriptors. Usually, the existent descriptors such as speeded up robust features (SURF), Kaze, binary robust invariant scalable keypoints (BRISK), features from accelerated segment test (FAST), and oriented FAST and rotated BRIEF (ORB) can competently detect, describe, and match images in the presence of some artifacts such as blur, compression, and illumination. However, the performance and reliability of these descriptors decrease for some imaging variations such as point of view, zoom (scale), and rotation. The introduced description method improves image matching in the event of such distortions. It utilizes a contourlet-based detector to detect the strongest key points within a specified window size. The selected key points and their neighbors control the size and orientation of the surrounding regions, which are mapped on rectangular shapes using polar transformation. The resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard deviation. Consequently, the descriptor obtained is invariant (translation, rotation, and scale) because of the two methods; the extraction of the region and the polar transformation techniques used in this paper. The description method introduced in this article is tested against well-established and well-known descriptors, such as SURF, Kaze, BRISK, FAST, and ORB, techniques using the standard OXFORD dataset. The presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature.

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Keywords

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