Part identification using robust feature extraction and pattern classification

Martin A. Hunt; J. Steve Hicks; Shaun S. Gleason

doi:10.1117/12.232243

21 February 1996 Part identification using robust feature extraction and pattern classification

Martin A. Hunt, J. Steve Hicks, Shaun S. Gleason

Proceedings Volume 2665, Machine Vision Applications in Industrial Inspection IV; (1996) https://doi.org/10.1117/12.232243
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

Abstract

The approach presented in this work combines the high-speed nature of pixel-based processing with a standard feature-based classifier to obtain a fast, robust identification algorithm for artillery ammunition. The algorithm uses the Sobel kernel to estimate the vertical intensity gradient of an electronic image of a projectile's circumference. This operation is followed with a directed Hough transform at a theta of 0 degrees, resulting in a one-dimensional vector representing the magnitude and location of horizontal attributes. This sequence of operations generates a compact description of the attributes of interest which can be computed at high speed, has no threshold-based parameters, and is robust to degraded images. In the classification stage a fixed-length feature vector is generated by sampling the Hough vector at the spatial locations included in the union of attribute locations from each possible projectile type. The advantages of generating a feature set in this manner are that no high-level algorithms are necessary to detect the spatial location of attributes and the feature vector is compact. Features generated using this method have been used with a Mahalanobis distance, nearest mean classifier for the successful demonstration of a proof-of-concept system that automatically identifies 155 mm projectiles.

Citation Download Citation

Martin A. Hunt, J. Steve Hicks, and Shaun S. Gleason "Part identification using robust feature extraction and pattern classification", Proc. SPIE 2665, Machine Vision Applications in Industrial Inspection IV, (21 February 1996); https://doi.org/10.1117/12.232243

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KEYWORDS

Hough transforms

Image classification

Image processing

Feature extraction

Image enhancement

Mahalanobis distance

Artillery

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