This paper presents a systematic profile recognition and mensuration approach in machine vision. It can be utilized to recognize and measure the profiles of industrial parts in an automated manufacturing process by machine vision systems. A new method of profile representation by sampling the data from the object boundary in a digital image is presented. Autoregressive (AR) models are used to code the sampled data of the profiles into AR coefficients for profile recognition. Characterization of the profiles is accomplished by the Data Dependent Systems (DDS) methodology. The AR coefficients and characteristic roots help construct the AR and DDS descriptors to characterize the signatures of the profiles. The frequency domain information about the profiles can be extracted by DDS analysis. The measurement of the profile variation is obtained from the DDS results using optical mensuration method. Neural network and feature weighting method are utilized as reasoning machines for recognition. The illustrative examples in which the profile sampled data are corrupted by noise show that the profile recognition and mensuration approach is very effective and robust in a typical noisy environment on the shop floor.
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