Open AccessEnhanced Global and Local Curvature Properties for Corner Detection
Author(s) -
Suraya Abu Bakar,
Muhammad Suzuri Hitam,
Wan Nural Jawahir Hj Wan Yussof,
Junaida Sulaiman
Publication year - 2020
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/769/1/012041
Subject(s) - corner detection , curvature , artificial intelligence , scale invariant feature transform , position (finance) , benchmark (surveying) , computer science , computer vision , blob detection , mathematics , image (mathematics) , pattern recognition (psychology) , image processing , geometry , edge detection , geology , geodesy , finance , economics
Corner detection is basically a methods used to extract certain kind of features in images which could produce some information including the location or position of the corner points. Thus, in this paper an enhancement shape corner detection method is proposed to detect true corners of shape images. The overall performance of the proposed enhanced shape corner detector and six other existing shape detectors and descriptors including Harris, SUSAN, Harris-Laplace, CSS, SIFT and global and local curvature properties is presented. The experimental results of corner detection methods are tested using the benchmark binary image MPEG-7 Core Experiment Shape-1 Part B dataset. To measure the performance of corner detection evaluation, an appropriate number of true corners were determined.