Title :
Automatic contour detection by encoding knowledge into active contour models
Author :
Gérard, Olivier ; Makram-Ebeid, Shérif
Author_Institution :
Lab. d´´Electron., Philips SAS, Limeil Brevannes, France
Abstract :
An original method for an automatic detection of contours in difficult images is proposed. This method is based on a tight cooperation between a multi-resolution neural network and a hidden Markov model-enhanced dynamic programming procedure. This new method is able to overcome the three major drawbacks of the “standard” active contours, initialization dependency, exclusive use of local information and occlusion sensitivity. The driving idea is to introduce high-order a priori information in each step of the system. An application to the automatic detection of the left ventricle in digital X-ray images is proposed
Keywords :
computer vision; dynamic programming; edge detection; hidden Markov models; image coding; neural nets; active contour models; automatic contour detection; digital X-ray images; hidden Markov model-enhanced dynamic programming; high-order a priori information; initialization dependency; knowledge encoding; left ventricle; local information; multi-resolution neural network; occlusion sensitivity; Active contours; Application software; Encoding; Hidden Markov models; Image edge detection; Neural networks; Robustness; X-ray detection; X-ray detectors; X-ray imaging;
Conference_Titel :
Applications of Computer Vision, 1998. WACV '98. Proceedings., Fourth IEEE Workshop on
Conference_Location :
Princeton, NJ
Print_ISBN :
0-8186-8606-5
DOI :
10.1109/ACV.1998.732867
