Active contours for tracking distributions

Author

Freedman, Daniel ; Zhang, Tao

Author_Institution

Comput. Sci. Dept., Rensselaer Polytech. Inst., Troy, NY, USA

Volume

13

Issue

4

fYear

2004

fDate

4/1/2004 12:00:00 AM

Firstpage

518

Lastpage

526

Abstract

A new approach to tracking using active contours is presented. The class of objects to be tracked is assumed to be characterized by a probability distribution over some variable, such as intensity, color, or texture. The goal of the algorithm is to find the region within the current image, such that the sample distribution of the interior of the region most closely matches the model distribution. Two separate criteria for matching distributions are examined, and the curve evolution equations are derived in each case. The flows are shown to perform well in experiments.

Keywords

image matching; object detection; partial differential equations; statistical distributions; tracking; video signal processing; Bhattacharyya coefficient; Kullback-Leibler distance; active contours; curve evolution equation; density matching; object tracking; photometric variable; probability distribution; Active contours; Computer science; Image edge detection; Image generation; Level set; Lighting; Partial differential equations; Photometry; Probability distribution; Solid modeling; Algorithms; Animals; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Statistical; Movement; Pattern Recognition, Automated; Photometry; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Video Recording;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/TIP.2003.821445

Filename

1284388