Paper Info
Title
Enhance ASMs based on adaboost-based salient landmarks localization and confidence-constraint shape modeling
Abstract
Active Shape Model (ASM) has been recognized as one of the typical methods for image understanding. Simply speaking, it iterates two steps: profile-based landmarks local searching, and statistics-based global shape modeling. We argue that the simple 1D profile matching may not localize landmarks accurately enough, and the unreliable localized landmarks will mislead the following shape matching. Considering these two problems, we propose to enhance ASM from two aspects: (1) in the landmarks local searching step, we introduce more efficient AdaBoost method to localize some salient landmarks instead of the relatively simple profile matching as in the traditional ASMs; (2) in the global shape modeling step, the confidences of the landmark localization are exploited to constrain the shape modeling and reconstruction procedure by not using those unreliably located landmarks to eliminate their negative effects. We experimentally show that the proposed strategies can impressively improve the accuracy of the traditional ASMs.
Year
DOI
Venue
2005
10.1007/11569947_2
IWBRS
Keywords
Field
DocType
confidence-constraint shape modeling,adaboost-based salient landmarks localization,shape modeling,profile matching,global shape modeling step,active shape model,traditional asms,efficient adaboost method,localize landmark,simple profile,statistics-based global shape modeling,following shape matching,local search
Active contour model,Active shape model,Computer vision,AdaBoost,Computer science,Active appearance model,Supervised learning,Artificial intelligence,Landmark,Pattern matching,Salient
Conference
Volume
ISSN
ISBN
3781
0302-9743
3-540-29431-7
Citations 
PageRank 
References 
2
0.40
9
Authors
5
Name
Order
Citations
PageRank
Zhiheng Niu1824.46
Shiguang Shan26322283.75
Xilin Chen36291306.27
Bingpeng Ma465136.63
Wen Gao511374741.77