Sm. Pandit et R. Guo, PROFILE RECOGNITION AND MENSURATION IN MACHINE VISION, Journal of manufacturing science and engineering, 119(3), 1997, pp. 417-424
This paper presents a systematic profile recognition and mensuration a
pproach in machine vision. It can be utilized to recognize and measure
the profiles of industrial parts in an automated manufacturing proces
s by machine vision systems. A new method od profile representation by
sampling the data from the abject boundary in a digital image is pres
ented. Autoregressive (AR) models are used to code the sampled data of
the profiles into AR coefficients for profile recognition. Characteri
zation of the profiles is accomplished by the Data Dependent Systems (
DDS) methodology. The AR coefficients and characteristic roots help co
nstruct the AR and DDS descriptors to characterize the signatures of t
he profiles. The frequency domain information about the profiles can b
e extracted by DDS analysis. The measurement of the profile variation
is obtained from the DDS results using optical mensuration method. Neu
ral network and feature weighting method are utilized as reasoning mac
hines 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 noi
sy environment on the shop floor.