Probability density function of object contours using regional regularized stochastic watershed

Abstract : In this paper, a probability density function of object contours based on the stochastic watershed transform is carried out. The watershed transform produces an over-segmentation of the image due to noise, illumination problems, low contrast, etc., because each regional minimum of the image gives place to a region in the output image. To solve this problem, the efforts are focused on the definition of markers to impose new minima in the image, and enhancing the gradient image. The stochastic watershed performs a probability density function (pdf) of the object contours based on a MonteCarlo simulation of random markers. A variation on the method for defining this pdf based on regional regularization of the image is carried out. The objective is to obtain a pdf of the object contours with less noise and better contrast than that produced by the stochastic watershed to use it as a new gradient image for segmentation purposes.
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Communication dans un congrès
IEEE International Conference on Image Processing (ICIP'2014 ) , Oct 2014, Paris, France. pp.4762 - 4766, 2014, IEEE Proc. of ICIP'2014. 〈10.1109/ICIP.2014.7025965〉
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Fernando López-Mir, Valery Naranjo, Sandra Morales, Jesus Angulo. Probability density function of object contours using regional regularized stochastic watershed. IEEE International Conference on Image Processing (ICIP'2014 ) , Oct 2014, Paris, France. pp.4762 - 4766, 2014, IEEE Proc. of ICIP'2014. 〈10.1109/ICIP.2014.7025965〉. 〈hal-01536377〉

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