Part identification using robust feature extraction and pattern classification

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.