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Amélioration des paramètres des contours actifs par une technique issue de la théorie de l'apprentissage
Presentation of Snake (or active contour), and of the Greedy Algorithm
Realized within IUT GEII of Tours (FRANCE), au Laboratoire d'Informatique de Tours (www.li.univ-tours.fr)

Abstract
Active contours, called snake, is used to detect image's contours. It is based on several energies, externs, interns and contexts energies, which make snake move into images and make it stop if it detect a contour. The Greedy algorithm we used, is splited on five ponderated enregies : curve, intensity, continuity, gradient and balloon energy. The move is determined comparing differents fonctionnels of energy between all neighborhood of pixels of each point in a snake. In the Greedy algorithm alone, the ponderation of energies is static and we decided to change this and make it dynamic, according to influence of each energie. The first step was determine which energie was the more chosen. Then, we modify the coefficient of this more followed energie according each point for the local method and according all of points for the global method. Moreover, we modify coefficients in two differents ways : either we add a constant percentage (ex 10 %), or we add a percentage depending of the variation of energy. We check our algorithm in differents ways, with two kinds of images (real and artificial), with several percents of augmentation, and especially with two kinds of actives contours : the active contour with constants coefficients, and with the Autonomous Snake.

Screenshot

Author
Julien Michot
Under the direction of :
Jean-Jacques Rousselle [Polytech'Tours - LI ]
Size
3,8Mo
Date of creation
2004
Downloads
Report 455
       

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