Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours
International Journal of Computer Vision
Multi-Reference Shape Priors for Active Contours
International Journal of Computer Vision
Anisotropic α-kernels and associated flows
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Smoothing of optical flow using robustified diffusion kernels
Image and Vision Computing
Robust processing of optical flow of fluids
IEEE Transactions on Image Processing
Smoothing, enhancing filters in terms of backward stochastic differential equations
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Anisotropic $\alpha$-Kernels and Associated Flows
SIAM Journal on Imaging Sciences
Euler's approximations to image reconstruction
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Multi-agent stochastic level set method in image segmentation
Computer Vision and Image Understanding
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In previous years, curve evolution, applied to a single contour or to the level sets of an image via partial differential equations, has emerged as an important tool in image processing and computer vision. Curve evolution techniques have been utilized in problems such as image smoothing, segmentation, and shape analysis. We give a local stochastic interpretation of the basic curve smoothing equation, the so called geometric heat equation, and show that this evolution amounts to a tangential diffusion movement of the particles along the contour. Moreover, assuming that a priori information about the shapes of objects in an image is known, we present modifications of the geometric heat equation designed to preserve certain features in these shapes while removing noise. We also show how these new flows may be applied to smooth noisy curves without destroying their larger scale features, in contrast to the original geometric heat flow which tends to circularize any closed curve.