Paper Info
Title
Concurrent Segmentation and Recognition with Shape-Driven Fast Marching Methods
Abstract
We present a variational framework that integrates the statistical boundary shape models into a Level Set system that is capable of both segmenting and recognizing objects. Since we aim to recognize objects, we trace the active contour and stop it near real object boundaries while inspecting the shape of the contour instead of enforcing the contour to get a priori shape. We get the location of character boundaries and character labels at the system output. We developed a promising local front stopping scheme based on both image and shape information for fast marching systems. A new object boundary shape signature model, based on directional Gauss gradient filter responses, is also proposed. The character recognition system that employs the new boundary shape descriptor outperforms the other systems, based on well-known boundary signatures such as centroid distance, curvature etc.
Year
DOI
Venue
2006
10.1109/ICPR.2006.400
ICPR (1)
Keywords
Field
DocType
fast marching method,feature extraction,fast marching,active contour,image segmentation,gaussian processes,statistical analysis,level set,object recognition
Active contour model,Computer vision,Active shape model,Pattern recognition,Fast marching method,Computer science,Level set,Feature extraction,Image segmentation,Artificial intelligence,Centroid,Cognitive neuroscience of visual object recognition
Conference
ISSN
ISBN
Citations 
1051-4651
0-7695-2521-0
9
PageRank 
References 
Authors
0.70
11
2
Name
Order
Citations
PageRank
Abdulkerim Çapar1194.28
Muhittin Gökmen213716.12