Graph Clustering for Image Segmentation
We propose a graph clustering approach for image segmentation by developing diffusion processes defined on arbitrary graphs.
We propose a graph clustering approach for image segmentation by developing diffusion processes defined on arbitrary graphs.
We pursue a concise texture modeling, analysis and segmentation system for generic natural images.
We investigate multigrid techniques for the solution of the time-dependent PDEs of geometric active contour models in Computer Vision.
Using an information-theoretic approach to study bottom-up spatial saliency, we show how Bayesian surprise can be interpreted to explain spatial saliency.
We have been working on PDE and wavelet-based techniques for the digital restoration of missing parts in paintings.
We have developed a visual processing framework for hands and head tracking and feature extraction from SL/gesture videos.