Name
Abstract | ||
|---|---|---|
In this paper, we try to address the challenging problem of combining local shape features to describe long and continuous shape characteristics. To this end, we firstly propose a novel type of local shape feature, namely Active Contour Fragment (ACF), to encode the shape deformation in a local region. An ACF is automatically learnt from the contours of a specific object class and capable to describe the intra-class shape characteristics based on the point distribution model. Secondly, we combine multiple ACFs into a group, namely Active Contour Group (ACG), to describe the long shape characteristics .We model the ACFs in an ACG using an undirected chain model and estimate the parameters of the chain model in a subspace for accelerating the learning and matching processes of ACGs. Finally, we discriminatively train the classifiers based on ACFs and ACGs in a boosting framework for localizing objects as well as delineating object boundaries. Both qualitative and quantitative evaluations show that our approach is capable of describing long shapes and the proposed recognition algorithm achieves promising performance on the public datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-37331-2_22 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
object recognition,active contour fragment,undirected chain model,long shape,point distribution model,shape deformation,intra-class shape characteristic,chain model,long shape characteristic,local region,local shape feature,continuous shape characteristic | Active contour model,Point distribution model,Object detection,ENCODE,Computer vision,Active shape model,Subspace topology,Pattern recognition,Computer science,Artificial intelligence,Boosting (machine learning),Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
7724 LNCS | PART 1 | 16113349 |
Citations | PageRank | References |
3 | 0.39 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zheng | 1 | 71 | 4.82 |
| Songlin Song | 2 | 3 | 0.39 |
| Hong Chang | 3 | 1834 | 96.46 |
| Xilin Chen | 4 | 6291 | 306.27 |