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Signal and Image Processing

Images and other sensing modalities are increasingly key to science, medicine, engineering, and many other fields, and hence computational methods for processing and extracting information from such sensors are of critical importance. Extracting useful information from raw, noisy data involves a wide range of mathematical techniques including inverse problems, optimization, modeling and prediction, discrete algorithms, and methods for high-level image understanding. In the Center for Applied Mathematics, researchers are creating new algorithms for these and other problems, solidly grounded in principled theory, and using these algorithms in a range of applications, from studying bird and insect flight, to reconstructing volume data from medical scans, to automatically reconstructing 3D geometry from millions of 2D photos on the Internet.