Title :
Modeling Bayesian estimation for deformable contours
Author :
Li, Stan Z. ; Lu, Juwei
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
A novel trainable snake model called EigenSnake, is presented in the Bayesian framework. In the EigenSnake, prior knowledge of a specific object shape, such as that of face outlines and facial features, is derived from a training set of the shape and incorporated into a Bayesian snake model in the form of the prior distribution. Further, a “shape space”, which is constructed on the basis of a set of eigenvectors obtained from principle component analysis, is used to restrict and stabilize the search for the optimal solution. The effectiveness is demonstrated by experiments, which shows that the EigenSnake produces more reliable and accurate results than existing models
Keywords :
Bayes methods; edge detection; eigenvalues and eigenfunctions; feature extraction; principal component analysis; Bayesian estimation modelling; EigenSnake; deformable contours; eigenvectors; face outlines; facial features; optimal solution search; principle component analysis; prior object shape knowledge; trainable snake model; Active shape model; Bayesian methods; Biomedical imaging; Deformable models; Electrical capacitance tomography; Encoding; Image edge detection; Image recognition; Inspection; Read only memory;
Conference_Titel :
Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on
Conference_Location :
Kerkyra
Print_ISBN :
0-7695-0164-8
DOI :
10.1109/ICCV.1999.790376