Vector co-occurrence morphological edge detection for colour image

作     者：Lu, Ying He, Chunming Yu, Yu-Feng Xu, Guoxia Zhu, Hu Deng, Lizhen 

作者机构：Northwestern Polytech Univ Unmanned Syst Res Inst Xian Peoples R China Nanjing Univ Posts & Telecommun Bell Honors Sch Nanjing Peoples R China Guangzhou Univ Dept Stat Guangzhou Peoples R China Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Network Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Prov Key Lab Image Proc & Image Commun Nanjing 210003 Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2021年第15卷第13期

页      面：3063-3070页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of China 

主　　题：Algebra Optical, image and video signal processing Image recognition Filtering methods in signal processing Image sensors Image recognition Signal processing theory Computer vision and image processing techniques Algebra 

摘      要：Morphological edge detection is a principal component in pattern recognition and machine vision. Traditional edge detection operators only take pixel mutual into consideration. However, the edges are influenced not only by pixel mutual but also by the boundary characteristics. Here, the vector co-occurrence morphological edge detection operator is proposed, which takes the pixel and boundary information both into consideration. The vector co-occurrence algorithm is exploited to resist the influence of the noise points and detect the edges from the colour image rather than the grey image. And, we lead to define a precise definition of the manner of sorting high-dimensional data for the colour image. The experiment results always illustrate the advancement and practicability of our methods against the baseline method. In terms of experiments, the BSDS500 dataset is introduced to compare and analyse with other algorithms. Based on the standard benchmark index evaluation in the BSDS500 dataset, the ODS and AP of various algorithms are compared and analysed.