Image feature extraction using Gabor-like transform

Abstract

Noisy and highly textured images were operated on with a Gabor-like transform. The results were evaluated to see if useful features could be extracted using spatio-temporal operators. The use of spatio-temporal operators allows for extraction of features containing simultaneous frequency and orientation information. This method allows important features, both specific and generic, to be extracted from images. The transformation was applied to industrial inspection imagery, in particular, a NASA space shuttle main engine (SSME) system for offline health monitoring. Preliminary results are given and discussed. Edge features were extracted from one of the test images. Because of the highly textured surface (even after scan line smoothing and median filtering), the Laplacian edge operator yields many spurious edges.