A parametric method for edge detection based on recursive mean-separate image decomposition

Author

Nercessian, Shahan ; Panetta, Karen ; Agaian, Sos

Author_Institution

Dept. of Electr. & Comput. Eng., Tufts Univ., Medford, MA

Volume

7

fYear

2008

fDate

12-15 July 2008

Firstpage

3689

Lastpage

3694

Abstract

Edge detection has played an important role in the field of computer vision. A parametric edge detection method based on recursive mean-separate image decomposition is introduced. A method for automatic parameter selection and two methods for thresholding are also suggested. Experimental results show that the proposed method outperforms many popular edge detection methods, including Sobel, Prewitt, Frei-Chen, and Canny both visually and by quantitative edge map evaluation. Proper parameter selection can also provide segmentation of materials such as potential threat objects in X-ray luggage scan images.

Keywords

edge detection; feature extraction; image segmentation; object detection; recursive estimation; X-ray luggage scan images; automatic parameter selection; computer vision; parametric edge detection method; recursive mean-separate image decomposition; Computer vision; Cybernetics; Detectors; Image decomposition; Image edge detection; Image segmentation; Kernel; Machine learning; Object detection; X-ray imaging; Edge detection; feature extraction; image decomposition; image segmentation; object detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4621046

Filename

4621046