Feature extraction techniques for exploratory visualization of vector-valued imagery

G. Harikumar, Yoram Bresler

Research output: Contribution to journalReview article

Abstract

This paper addresses the exploratory visualization of multispectral image data. In such data, each component of the vector pixel corresponds to a different imaging modality or a different combination of imaging parameters, and may provide different levels of contrast sensitivity between different regions of the underlying image. We address the problem of presenting this multidimensional data to human observers by synthesizing a display matched to their visual capabilities. Specifically, we seek to determine a data-adaptive linear projection of the vector data to one dimension that produces a grayscale image providing maximum discrimination between the different regions of the underlying object. The approach is equivalent to the extraction of the best linear feature of the vector field. Several new feature-extraction criteria that take into account both the spatial and multivariate structures of the data are proposed and illustrated by simulations on test images.

Original languageEnglish (US)
Pages (from-to)1324-1334
Number of pages11
JournalIEEE Transactions on Image Processing
Volume5
Issue number9
DOIs
StatePublished - Dec 1 1996

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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