21 February 1996 Part identification using robust feature extraction and pattern classification
Author Affiliations +
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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); doi: 10.1117/12.232243; https://doi.org/10.1117/12.232243


Back to Top