The last decade has seen a dramatic increase in computational power and sensor
ubiquity, as well as an ever increasing demand for finer resolution in both
scientific and geometric modeling. This has led to the creation of enormously
large data sets with exquisite detail. However, these data sets will be useful
only if we can process them efficiently, whether it be for storage,
transmission, visual display, fast on-line graphical query, correlation, or
registration against data from other modalities.
Raw data sets are typically inaccessible and need to be transformed to more
efficient representations for further processing. Several competing issues
emerge. Sparsity is essential for efficient transmission, storage, and
computation. Multiscale representations are critical to extract features at
desired scales. Implementation in silicon leads to new issues of robustness in
the face of computational error and imprecise circuit implementation.
An emerging technology to address these issues utilizes redundant
representations. High oversampling followed by coarse quantization is the
preferred method for analog to digital conversion of signals. Sparse
representation of images using redundant families of waveforms is effectively
utilized in feature extraction and denoising. These redundant families can be
frames, dictionaries, or libraries of bases.
On the other hand, there is, at present, no compelling theory to explain the
advantages of redundancy in image and signal processing. This program will
convene leading experts from data representation into two workshops to describe
the current understanding of the benefits of redundancy and to set forward a
program for further research. These experts will come from diverse areas such
as applied mathematics, statistics, computer science, engineering, and circuit
design.
A limited amount of funding for participants at all levels is available,
especially for researchers in the early stages of their career who want to
attend the full program.
Center for Scientific Computation And Mathematical Modeling (CSCAMM)
Computer Science Instructional Center (Building #406)
University of Maryland, College Park
College Park, MD 20742-3289
