My research activities in Structural Pattern Recognition and Computer Vision can be broadly grouped under the headings
Unsupervised (parameter) learning for MRFs on bipartite graphs.
We consider a modified EM algorithm for unsupervised learning of Markov random fields defined on bipartite graphs. The main idea is to replace the intractable maximum likelihood estimator in the M-step by a tractable pseudo-likelihood estimator. The modified EM algorithm becomes tractable for MRFs on bipartite graphs and is competitive with persistent contrastive divergence - a popular method used for unsupervised learning in the context of deep learning.
We apply the approach for learning a-priori shape models in the context of image segmentation.
Modelling composite shapes by Gibbs Random Fields with pairwise interactions.
We analyse the following question. Do GRFs with pairwise interactions have enough expressive power
to model (1) spatial relations between segments, (2) simple shapes and (3) composite shapes?
Segmentation with shape and shading.
We propose a probabilistic segmentation scheme, which is widely
applicable to some extent. Besides the segmentation itself our model
incorporates object specific shape priors and shading.