Contrast techniques for line detection in a correlated noise environment

Abstract

A new class of line detectors based on the theory of linear contrasts is developed. By incorporating the F-statistic and the shape test, the new algorithm detects and locates line features simultaneously, and thereby increases the detection power substantially. Many classes of detectors, including the step edge detector, naturally becomes subclasses of the proposed detector. Mathematical analysis indicates that the new algorithm that accounts for noise dependence performs better than its counterpart that neglects the dependence of noise. A symmetrical balanced incomplete-blocks (SBIB) design is introduced to model real-world images in terms of a treatment group that is associated with preexistent, conspicuous, features such as lines, edges, etc., and an incomplete treatment group that is randomized to accommodate image textures. The new algorithm in conjunction with the SBIB design is applied in the four main directions, perpendicularly to the line under investigation. It is robust, simple, efficient, and amenable to real-time applications. All pertinent statistics for the implementation of the procedure are estimated from data. Extensive computer simulations demonstrate the performance of the proposed detector.