Fast detection of line features in large images

Abstract

This paper addresses the automated detection of line features in large industrial inspection images. The manual examination of these images is labor-intensive and causes undesired delay of inspection results. Hence, it is desirable to automatically detect certain features of interest. In this paper we are concerned with the detection of vertical or slanted line features that appear at unpredictable intervals across the image. The line features may appear distorted due to shortcomings of the sensor and operator conditions. Line features are modeled as a pair of smoothed step edges of opposite polarity that are in close proximity, and two operators are used to detect them. The individual operator-outputs are combined in a non-linear fashion to form the line-feature response. The line features are then obtained by following the ridge of the line-feature response. In experiments on four datasets, over 98.8% of line features are correctly detected, with a low false-positive rate. Experiments also show that the approach works well in the presence of considerable noise due to poor operating conditions or sensor failure.