This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of ...
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold...
Efficient segmentation of globally optimal surfaces representing object boundaries in volumetric data sets is important and challenging in many medical image analysis applications....
This paper presents an efficient solution technique for the steady-state analysis of the second-order Stochastic Fluid Model underlying a second-order Fluid Stochastic Petri Net (...
We describe a hybrid algorithm that is designed to reconstruct a piecewise smooth surface mesh from noisy input. While denoising, our method simultaneously regularizes triangle me...