An efficient method for tensor voting using steerable filters

In many image analysis applications there is a need to extract curves in noisy images. To achieve a more robust extraction, one can exploit correlations of oriented features over a spatial context in the image. Tensor voting is an existing technique to extract features in this way. In this paper, we present a new computational scheme for tensor voting on a dense field of rank-2 tensors. Using steerable filter theory, it is possible to rewrite the tensor voting operation as a linear combination of complex-valued convolutions. This approach has computational advantages since convolutions can be implemented efficiently. We provide speed measurements to indicate the gain in speed, and illustrate the use of steerable tensor voting on medical applications

An efficient method for tensor voting using steerable filters

In many image analysis applications there is a need to extract curves in noisy images. To achieve a more robust extraction, one can exploit correlations of oriented features over a spatial context in the image. Tensor voting is an existing technique to extract features in this way. In this paper, we present a new computational scheme for tensor voting on a dense field of rank-2 tensors. Using steerable filter theory, it is possible to rewrite the tensor voting operation as a linear combination of complex-valued convolutions. This approach has computational advantages since convolutions can be implemented efficiently. We provide speed measurements to indicate the gain in speed, and illustrate the use of steerable tensor voting on medical applications

Topological Features in Glyph-Based Corotation Visualization

Abstract This chapter introduces a novel method for vortex detection in flow fields based on the corotation of line segments and glyph rendering. The corotation mea-sure is defined as a point-symmetric scalar function on a sphere, suitable for direct representation in the form of a three-dimensional glyph. Appropriate placement of these glyphs in the domain of a flow field makes it possible to depict vortical fea-tures present in the flow. We demonstrate how topological analysis of this novel glyph-based representation of vortex features can reveal vortex characteristics that lie beyond the capabilities of visualization techniques that consider vortex direction and magnitude information only.