Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in co...
Training of conditional random fields often takes the form of a double-loop procedure with message-passing inference in the inner loop. This can be very expensive, as the need to...
Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously wi...
The minimal-length encoding approach is applied to define concept of sequence similarity. Asequence is defined to be similar to another sequence or to a set of keywords if it can ...
Motion-compensated temporal wavelet decomposition is a useful framework for fully scalable video compression schemes. In this paper we propose a new approach to reduce the ghostin...