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The Multi-Dimensional Signal Processing Laboratory (MDSP Lab) currently has research interests which concentrate around the following topics
- Multidimensional statistical signal and image processing
- Detection and estimation
- Geometric modeling and estimation
- Bio-medical signal and image processing
- Sensor networks and distributed signal processing
The main research thrusts we are currently involved in are:
- Feature Enhanced Imaging
Remotely sensed images are becoming pervasive for a wide spectrum of tasks. The increasing volume of data being produced by such systems means that automated decision-making processes must be employed. The success of such automated processing depends on how well reconstructed imagery exhibit salient features of the underlying scene. This thrust aims to include the feature oriented goals of back end reasoning systems in the low level processing of sensor data.
- Dynamic Inverse Problems
The main focus of this research thrust is the modeling and solution of inverse problems where the unknown of interest is dynamic and evolves as data are being acquired. Challenges include identifying appropriate models of the scene unknowns and their dynamics and the inclusion of this information in formulations which lead to tractable optimization problems.
- Object-Based Modeling and Statistical Inference
In many image processing problems the quantity of interest is primarily geometric in nature, such as the shape or location of an object. In this research we focus on such primarily geometric-oriented problems. We aim to develop both prior models of shape and geometry in scenes and to use these models for statistical inference, reasoning, and estimation.
- Bio-medical Signal and Image Processing
The ability to probe both the body and cell has increased dramatically in recent years. There has been an explosion in new and intriguing modalities and an associated need for models and methods for the extraction of information from them in the face of noise. These modalities range from magnetic resonance imaging to fluorescent microscopy. Advances in our ability to robustly separate signal from noise are linked to advances in fundamental science.
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