Contour detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

作     者：Xi-Qi Yang Qing-Feng Zhang Zhan Li 杨悉琪;张庆丰;李展

作者机构：Department of Computer ScienceJinan UniversityGuangzhou 510632China State Key Lab of CAD&CGZhejiang UniversityHangzhou 310000China Sino-French Joint Laboratory for AstrometryDynamics and Space ScienceJinan UniversityGuangzhou 510632China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2020年第20卷第1期

页      面：83-92页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：partly supported by the National Natural Science Foundation of China(Grant Nos.U1431227 and 11873026) Natural Science Foundation of Guangdong Province,China(Grant No.2016A030313092) the Fundamental Research Funds for the Central Universities(Grant No.21619413) 

主　　题：techniques:image processing methods:data analysis astrometry 

摘      要：In Cassini ISS(Imaging Science Subsystem)images,contour detection is often performed on disk-resolved objects to accurately locate their ***,contour detection is a key *** edge detection methods,such as Canny and Roberts,often extract the contour with too much interior details and *** the deep convolutional neural network has been applied successfully in many image tasks,such as classification and object detection,it needs more time and computer *** this paper,a contour detection algorithm based on H-ELM(Hierarchical Extreme Learning Machine)and Dense CRF(Dense Conditional Random Field)is proposed for Cassini ISS *** experimental results show that this algorithm’s performance is better than both traditional machine learning methods,such as Support Vector Machine,Extreme Learning Machine and even deep Convolutional Neural *** extracted contour is closer to the actual ***,it can be trained and tested quickly on the general configuration of PC,and thus can be applied to contour detection for Cassini ISS images.