Paper Info
Title
Self-Training Statistic Snake for Image Segmentation and Tracking
Abstract
In this work we propose a new supervised deformable model that generalizes the classical contour-based snake. This model is defined to deform in a feature space generated by a set of Gaussian derivative filter responses. The snake selects and classifies image features by a parametric vector that gives the direction in the feature space minimizing the dissimilarity between the learned and found image features and maximizing the distance between different contour configurations. Each snake curve patch is devoted to searching for a special contour configuration. The classes corresponding to different contour configurations are obtained by means of a statistical supervised learning technique using samples of different contours and no contour points. The snake starts with a large set of Gaussian filters that is reduced by means of principal component analysis in a supervised way to optimize it in the feature search
Year
DOI
Venue
1999
10.1109/ICIAP.1999.797629
Venice
Keywords
Field
DocType
image segmentation,feature search,classical contour-based snake,different contour,different contour configuration,new supervised deformable model,contour point,special contour configuration,image feature,snake curve patch,feature space,self-training statistic snake,feature extraction,edge detection,principal component analysis,image features,layout,gaussian distribution,parameter estimation,learning artificial intelligence,computer vision,parametric statistics,image classification,shape,supervised learning,minimisation
Computer vision,Feature vector,Pattern recognition,Computer science,Edge detection,Feature (computer vision),Feature extraction,Supervised learning,Image segmentation,Parametric statistics,Artificial intelligence,Contextual image classification
Conference
ISBN
Citations 
PageRank 
0-7695-0040-4
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
X.M. Pardo1736.75
P. Radeva211513.89
J. J. Villanueva3102.34