The authors report a methodology that enhances the compression of Landsat thematic mapper (TM) multispectral imagery, while reducing the image information loss. The method first removes interband correlation of the image data by use of the Karhunen-Loeve transform (KLT) to produce the image principal components. Each principal component is spatially decorrelated using a discrete wavelet transform. The resulting coefficients are then quantized and losslessly encoded. Image compressions of typically 80:1 demonstrate that the method should be quite suitable for rapid browsing applications where small amounts of image loss are tolerable.>

A fundamental assumption made in formulating optical-flow algorithms, that motion at any point in an image can be represented as a single pattern component undergoing a simple translation, fails for a number of situations that commonly occur in real-world images. An alternative formulation of the local motion assumption in which there may be two distinct patterns undergoing coherent (e.g. affine) motion within a given local analysis region is proposed. An algorithm for the analysis of two-component motion in which tracking and nulling mechanisms applied to three consecutive image frames separate and estimate the individual components is given. Precise results are obtained, even for components that differ only slightly in velocity as well as for a faint component in the presence of a dominant, masking component. The algorithm provides precise motion estimates for a set of elementary two-motion configurations and is robust in the presence of noise.>

A fundamental assumption made in formulating optical-flow algorithms is that motion at any point in any image can be represented as a single pattern undergoing a simple translation: even complex motion will appear as a uniform displacement when viewed through a sufficiently small window. This assumption fails in a number of common situations. The authors propose an alternative formulation in which there may be two distinct patterns undergoing coherent motion within a given local analysis region. They then present an algorithm for the analysis of two-component motion. They also demonstrate that the algorithm provides precise motion estimates for a set of elementary two-motion configurations, and show that it is robust in the presence of noise.>