Title :
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
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;
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
DOI :
10.1109/ICMLC.2008.4621046
