||Extraction of Near Circular Objects using Markov Random Fields: The Multilayer 'Gas of Circles' Model|
A multi-layer binary Markov random field model for extracting an unknown number of possibly touching or overlapping near-circular objects.
||Multicue Image and Video Segmentation: a Multi-layer MRF Framework|
The human visual system is not treating different features sequentially.
Instead, multiple cues are perceived simultaneously and
then they are integrated by our visual system in order to explain the
observations. Therefore different image features has to be handled in
a parallel fashion. In this project, we attempt to develop such a model
in a Markovian framework.
||Automatic Color Image Segmentation via Reversible Jump MCMC|
The goal of this project is to propose a method which is able to segment a color image without any human intervention. The only input is the observed image, all other parameters are estimated during the segmentation process. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes.
||Cellular Neural Networks for Markov Random Field Image Segmentation|
The main contribution of this project is the implementation of a Markovian image segmentation model
on a new chip, called CNN (Cellular Neural
Networks), which is capable
to do the task in real time.