Research Interests

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Image Processing, Visualization, and Analysis

Images have become one of the main sources of information to study physical phenomena and to the design of intelligent systems. In their most general form, these images can be multi-dimensional (e.g., CT and MR images), multi-spectral (e.g., remote sensing images), or both (e.g., digital video). In any case, we are interested in image processing and analysis techniques that enhance, encode, extract, represent, describe, and classify the image content. Particularly, we have studied image operators based on pixel connectivity for filtering, segmentation, classification, compression, object representation and description. We are also interested in special rendering techniques for multidimensional image visualization and in the quantitative analysis of biomedical structures for the purpose of diagnosis, treatment, and research.

Content-Based Image Retrieval

Content-based image retrieval systems rely on image processing methods that extract and represent image content in a compact way. We are interested in shape, color, and texture descriptors for efficient indexation and effective information retrieval from large image databases. However, efficiency also depends on special data structures for similarity-based search and low-level image features are usually not enough to satisfy the users' expectations. Therefore, we are also interested in the design of suitable data structures, which take into account the user's preferences, and on relevance feedback techniques to reduce the semantic gap.

Pattern Recognition and Machine Learning

Images, pixels, or image objects can be considered samples of a dataset. We are interested in clustering, pattern classification, and machine learning techniques for general data analysis and for image processing applications, such as restoration, compression, and retrieval. Particularly, we have investigated methods that exploit a connectivity function between samples in the feature space.

Digital Video and Biomedical Imaging Applications

A digital video is a sequence of color images along the time. This makes of it a 3-dimensional and 3-spectral data set. Our interest is on graph-based video processing techniques for compression, retrieval, segmentation, and object tracking. In medical imaging, sequences of digital images form 3D/4D data sets containing object systems under study. The visualization and analysis of such objects (e.g., organs, tumors) usually require filtering, interpolation, alignment, registration, segmentation, representation, description, and classification of multidimensional data. We are interested in such techniques applied to magnetic resonance images of the human brain for the study of epilepsy and other degenerative diseases. We are also interested in the automation of the diagnosis of intestinal parasites by the analysis of microscopy images.