A probabilistic prediction method for object contour tracking

Title:

A probabilistic prediction method for object contour tracking

Creator:

Weiler, Daniel, Clemente , Irene Ayllón, Willert, Volker, and Eggert, Julian

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:53f9af5a-a462-4139-b687-39ee3d49f942
uuid:53f9af5a-a462-4139-b687-39ee3d49f942

Subject:

Level set method, segmentation, prediction, contour, probabilistic, tracking, and image sequence

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

We present an approach for probabilistic contour prediction within the framework of an object tracking system. We combine level-set methods for image segmentation with optical flow estimations based on probability distribution functions (pdfs) calculated at each image position. Unlike most recent level-set methods that consider exclusively the sign of the level-set function to determine an object and its background, we introduce a novel interpretation of the value of the level-set function that reflects the confidence in the contour. To this end, in a sequence of consecutive images, the contour of an object is transformed according to the optical flow estimation and used as the initial object hypothesis in the following image. The values of the initial level-set function are set according to the optical flow pdfs and thus provide an opportunity to incorporate the uncertainties of the optical flow estimation in the object contour prediction.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2009 Volume:19 | Number:5

Harvested from:

CDK

Metadata only:

false