Abstract In this paper, we propose a new geometric contour framework with support of specified vector field. First we define three criteria for selection of vector field in geometric model. According to the criteria, EdgeFlow, a powerful segmentation tool, is selected to generate desirable initial vector field. In order to overcome the drawbacks of conventional geometric models, multi-source external forces, such as from texture and multi-spectra, are integrated to provide the ability for segmenting the texture-rich and complex scene images. Instead of common smoothing pre-processing to denoise and suppress possible spurious edges, the more advanced complex diffusion filters are adopted in our algorithm, which result in the piecewise filtered image to help detect those sharp transition regions. We test our model on the Berkeley Segmentation Database, and the experimental results are promising.
A Geometric Contour Framework with Vector Field Support
Computer Vision – ACCV 2006 ; 1 ; 214-223
Lecture Notes in Computer Science ; 3852 , 1
2006-01-01
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Segmentation Result , Curve Evolution , Active Contour Model , Complex Scene , Weak Edge Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
A Geometric Contour Framework with Vector Field Support
British Library Conference Proceedings | 2006
|Gradient Flows and Geometric Active Contour Models
British Library Conference Proceedings | 1995
|Using Geometric Model Guided Active Contour Method for Traffic Sign Detection
British Library Conference Proceedings | 1990
|A geometric active contour model without re-initialization for color images
British Library Online Contents | 2009
|