Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

17

ICPR
2010
IEEE

favoriteEmaildiscussreport

172views Computer Vision» more ICPR 2010»

Learning Probabilistic Models of Contours

14 years 26 days ago

Learning Probabilistic Models of Contours

Download membres-lig.imag.fr

We present a methodology for learning spline-based probabilistic models for sets of contours, proposing a new Monte Carlo variant of the EM algorithm to estimate the parameters of a family of distributions defined over the set of spline functions (with fixed complexity). The proposed model effectively captures the major morphological properties of the observed set of contours as well as its variability, as the simulation results presented demonstrate.

Laure Amate, Maria João Rendas

Real-time Traffic

Computer Vision | ICPR 2010 | Major Morphological Properties | Monte Carlo Variant | Spline-based Probabilistic Models |

claim paper

Related Content

» Learning Probabilistic Models for Contour Completion in Natural Images

» Linear and nonlinear generative probabilistic class models for shape contours

» A Hybrid Framework for Image Segmentation Using Probabilistic Integration of Heterogeneous...

» A Graphical Model Framework for Coupling MRFs and Deformable Models

» Active contours

» Spatiotemporal Contour Grouping Using Abstract Part Models

» A Probabilistic Multiscale Model for Contour Completion Based on Image Statistics

» Parametric contour tracking using unscented Kalman filter

» Fast probabilistic snake algorithm

Post Info
More Details (n/a)

Added	12 Oct 2010
Updated	12 Oct 2010
Type	Conference
Year	2010
Where	ICPR
Authors	Laure Amate, Maria João Rendas

Comments (0)