Fast detection of line features in large images

Thomas B. Sebastian; Kai F. Goebel; Tahs Saleh

doi:10.1117/12.591649

24 February 2005 Fast detection of line features in large images

Thomas B. Sebastian, Kai F. Goebel, Tahs Saleh

Proceedings Volume 5679, Machine Vision Applications in Industrial Inspection XIII; (2005) https://doi.org/10.1117/12.591649
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

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.

Citation Download Citation

Thomas B. Sebastian, Kai F. Goebel, and Tahs Saleh "Fast detection of line features in large images", Proc. SPIE 5679, Machine Vision Applications in Industrial Inspection XIII, (24 February 2005); https://doi.org/10.1117/12.591649

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KEYWORDS

Inspection

Sensors

Image segmentation

Brain

Feature extraction

Neuroimaging

Distortion

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