Image-Models for 2-D Flow Visualization and Compression

Abstract

Pattern models for the analysis, visualization, and compression of experimental 2-D flow imagery are developed. These models are based on the 2-D linear phase portrait, and consist of a superposition of flow primitives that are equivalent to the canonical form of phase portraits. The phase portrait is a compact flow descriptor specified by a 2 × 2 A matrix, and it provides for classification into one of six possible patterns based on the matrix eigenvalues. The modeling requires computation of the orientation field, critical point detection, and estimation of the associated phase portraits as preliminary analysis steps. Existing methods to compute the orientation field that are appropriate for highly textured images are employed, but a technique for its computation in weakly textured imagery is included. Critical points are located with a detector that is based on the index (or winding number) of a vector field. A performance analysis of the detector is included. A linear least-squares method of estimating the phase portrait A matrix from the orientation field is presented. Flows are then modeled as a superposition of primitives, where their associated strengths are determined from the orientation field. This modeling works well for flows that exhibit nearly ideal behavior. Finally, the derived models are employed to compress scalar images that exhibit little or gradual variation along the flow streamlines. Compression ratios on the order of 100: 1 are achieved.