In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
Many optimization techniques have been adopted for efficient job scheduling in grid computing, such as: genetic algorithms, simulated annealing and stochastic methods. Such techni...
Renato Porfirio Ishii, Rodrigo Fernandes de Mello,...
We present an intuitive, fast and accurate interactive segmentation method for visualizing and analyzing 3D medical images. Our method combines a general deformable subdivision-sur...
We present a real-time model-based line tracking approach with adaptive learning of image edge features that can handle partial occlusion and illumination changes. A CAD (VRML) mo...
: The biomechanical properties of soft tissue derived from experimental measurements are critical for developing a reality-based model for minimally invasive surgical training and ...