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An Efficient Tool Identification System Using Principal Component Analysis

Adithya Job, Anooj Rohit, B. Suryanarayanan, Prashanth Joseph Panangadan, Arun A. Balakrishnan

An efficient implementation of tool identification system based on feature extraction technique is proposed and validated. Principal Component Analysis (PCA) is used for extracting features from a large training database images of different classes of tools like spanner screwdriver, knife and hammer. Original image from each class is rotated by 5° to obtain 72 training images for each individual class of tools. In the proposed method, the initial computation of features of the training images using PCA is computed for the entire database and is saved in the memory. Computed results are loaded from memory when a test image is provided to the system for identification. Simulation results shows that proposed method can recognize a tool within 15 seconds from the database containing 288 training images and hence the proposed method can be used for real time tool identification systems.