Testing Geodesic Active Contours

Authors:
A. Caro;T. Alonso;P. G. Rodríguez;M. L. Durán;M. M. Ávila
Affiliations:
Departamento de Informática, Escuela Politécnica, Universidad de Extremadura, E-10071 Cáceres, Spain;Sicubo S.L.TM, Spain;Departamento de Informática, Escuela Politécnica, Universidad de Extremadura, E-10071 Cáceres, Spain;Departamento de Informática, Escuela Politécnica, Universidad de Extremadura, E-10071 Cáceres, Spain;Departamento de Informática, Escuela Politécnica, Universidad de Extremadura, E-10071 Cáceres, Spain
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Year:
2007

Citing 7
Cited 0

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Using Dynamic Programming for Solving Variational Problems in Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast algorithm for active contours and curvature estimation

CVGIP: Image Understanding
Geodesic Active Contours

International Journal of Computer Vision
Information Retrieval

Information Retrieval
Guidelines for Choosing Optimal OParameters of Elasticity for Snakes

CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Gradient flows and geometric active contour models

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Active Contours are a widely used Pattern Recognition technique. Classical Active Contours are curves evolutionate by minimizing an energy function. However, they can detect only one o bject within an image with several objects, and the solution is highly dependent on parameters in its formulation. A solution can be found in Geodesic Active Contours (GAC). We have developed a version of this technique and improved some aspects to apply on real and practical cases. The algorithm has been tested with both synthetic and real images.