Line Detection in Noisy and Structured Backgrounds Using Græco-Latin Squares

Abstract

In this paper new methods for detection of line targets in digital images using multiple-way Analysis of Variance (ANOVA) methods based on the Græco-Latin square (GLS) are developed and demonstrated. After presentation of the underlying statistical theory upon which the GLS is based, the philosophy of using ANOVA methods in pattern recognition problems is illustrated by one-way and two-way models. The GLS detectors are then described in detail and their performance demonstrated. The detectors are not only capable of detecting lines of different direction, but their complexity also can be used to estimate and remove some types of unwanted image structure. Also proposed is an adaptive ANOVA method for line detection, which uses information contained in the GLS statistics to eliminate unnecessary estimation of some of the structure parameters and again improve the power of the detector. The problem of false alarms in regions of the image containing sharp gray-level discontinuities also is addressed, and adjustments are made to the algorithms for their suppression.