Title :
Active contours for tracking distributions
Author :
Freedman, Daniel ; Zhang, Tao
Author_Institution :
Comput. Sci. Dept., Rensselaer Polytech. Inst., Troy, NY, USA
fDate :
4/1/2004 12:00:00 AM
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;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2003.821445
