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Proceedings Paper

Automated object detection for astronomical images
Author(s): Sonny Orellana; Lei Zhao; Helen Boussalis; Charles Liu; Khosrow Rad; Jane Dong
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Paper Abstract

Sponsored by the National Aeronautical Space Association (NASA), the Synergetic Education and Research in Enabling NASA-centered Academic Development of Engineers and Space Scientists (SERENADES) Laboratory was established at California State University, Los Angeles (CSULA). An important on-going research activity in this lab is to develop an easy-to-use image analysis software with the capability of automated object detection to facilitate astronomical research. This paper presented a fast object detection algorithm based on the characteristics of astronomical images. This algorithm consists of three steps. First, the foreground and background are separated using histogram-based approach. Second, connectivity analysis is conducted to extract individual object. The final step is post processing which refines the detection results. To improve the detection accuracy when some objects are blocked by clouds, top-hat transform is employed to split the sky into cloudy region and non-cloudy region. A multi-level thresholding algorithm is developed to select the optimal threshold for different regions. Experimental results show that our proposed approach can successfully detect the blocked objects by clouds.

Paper Details

Date Published: 24 October 2005
PDF: 8 pages
Proc. SPIE 6015, Multimedia Systems and Applications VIII, 60150Y (24 October 2005); doi: 10.1117/12.631033
Show Author Affiliations
Sonny Orellana, California State Univ./Los Angeles (United States)
Lei Zhao, California State Univ./Los Angeles (United States)
Helen Boussalis, California State Univ./Los Angeles (United States)
Charles Liu, California State Univ./Los Angeles (United States)
Khosrow Rad, California State Univ./Los Angeles (United States)
Jane Dong, California State Univ./Los Angeles (United States)

Published in SPIE Proceedings Vol. 6015:
Multimedia Systems and Applications VIII
Anthony Vetro; Chang Wen Chen; C.-C. J. Kuo; Tong Zhang; Qi Tian; John R. Smith, Editor(s)

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