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Shape-Matching Model Optimization Using Discrete-point Sampling and Feature Salience

The component classification and potential fault region locating in the full-automatic inspection system of a freight train require a computer vision method with the ability of classifying quickly and locating precisely, addressing anti-nonlinear deformations, and being able to perform extensible learning. Inspired by these requirements, this paper specifically optimizes the three elements of a shape-matching model, including the scene map, the shape template, and the matching.  Our method uses a discrete-point sampling map (DPSM) as an intermediate representation, to enhance the stability of the scene maps, uses the criterion function based on feature salience to select a better shape-template group, and matches hand-sketches with regions in DPSMs to reduce the difficulty of the matching calculation. Based on our optimized shape-matching model, we set up a new procedure for component classifications and potential fault region locating in the full-automatic inspection system for freight trains, which has been applied successfully on more than 10 parts of freight train cars in the railway for more than 2 years. The results of anti-noise testing in laboratory and daily operation at several inspecting stations show that our method has a strong ability to survive with nonlinear deformations, and has a good extensibility to be used with different parts, which meet application demands for the full-automatic inspection system.