Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Radial basis function neural network based on order statistics
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Median M-type radial basis function neural network
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Smoothing of optical flow using robustified diffusion kernels
Image and Vision Computing
Modelling and Simulation in Engineering
3D modeling of multiple-object scenes from sets of images
Pattern Recognition
| Hi-index | 0.01 |
We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training. A new robust training algorithm for RBF networks based on α-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images