Contour tracking using weighted structure tensor based variational level set

A novel contour tracking method using weighted structure tensor based variational level set is proposed in this paper.The image is first converted to weighted structure tensor field by extracting apositive definite symmetric covariance matrix for each pixel.Then,a level set method is employed to represent object contour implicitly which separates the image domain into two areas each modeled by tensor field based Gaussian mixture model separately.By solving agradient flow equation of energy functional with respect to the level set,the object contour will converge to its real profile in the newly arrived frame.Experimental results on several video sequences demonstrate the better performance of our method than the other two contour tracking algorithms.

Cartoon capture by key-frame based contour tracking

Traditional cartoons have been widely used in entertainment,education,and advertisement.Thus,a large amount of cartoon data is available.In this paper,we propose a new technique for capturing the motion of a character in an existing cartoon sequence.This technique tracks the contours of the cartoon character in the sequence,and key frames are used to guide the tracking.We model contour tracking as a space-time optimization problem in which an energy function including both temporal and spatial constraints is defined.First,the user labels the contours of the character on the key frames.Then,the contours on the intermediate frames are tracked by minimizing the energy function.The user may need to interactively adjust the tracking result and restart the optimization process to refine the result.Finally,an edge snapping algorithm is applied to make the tracking result more precise.Experiments show that our technique works effectively.