We consider the problem of planar shape registration on binary images. Our primary goal is to investigate novel methodologies which work without feature point extraction and established correspondences; avoid the solution of complex optimization problems; and provide an exact solution regardless of the strength of the distortion. The newly developed techniques will be validated on medical images.

The goal of the project is to develop efficient image acquisition methods to gain visual information from incorrect, noisy, and uncertain projections.

We consider the problem of estimation the parameters of transformations aligning two binary images. The advantage of our algorithm is that it is easy to implement, less sensitive to the strength of the deformation, and robust against segmentation errors.

The `gas of circles' (GOC) model is a tool to describe a set of circles with an approximately fixed radius. The model is based on the higher-order active contour (HOAC) framework. The method has been succesfully applied to tree crown extraction on aerial images.

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.

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.

Stochastic paintbrush rendering is an algorithm to simulate the painting process to get a picture similar to a real painting. The image can be described by the parameters of the consecutive paintbrush strokes, resulting in a parameter-series. In this project, we will explore this stroke-representation of images for Content Based Image Retrieval (CBIR).

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.

This project is about image segmentation algorithms using a Markovian approach. The main contribution is a new hierarchical MRF model and a Multi-Temperature Annealing (MTA) algorithm proposed for the energy minimization of the model. The convergence of the MTA algorithm has been proved towards a global optimum in the most general case, where each clique may have its own local temperature schedule.