An algorithm to estimate the crown patterns of diamonds based on machine vision

In this paper, we describe an algorithm that estimates the cut quality of the crown patterns of diamonds based on machine vision. To accurately extract the features of the edges of diamonds in complicated diamond images, a strategy based on multi-scale decomposition is employed. Using an enhanced Eigen space method, the orientation of the diamond can be roughly estimated. From the traditional least squares distance method, we derive the conditions of the least squares distance weighted by wavelet transform modulus. Then, the problem of diamond-edge feature extraction is transformed into a virtual control process through building a virtual girder truss model (VGTM) and a virtual attraction field (VAF). Using two stages, rough feature extraction and refined feature extraction, all the desired diamond edges can be accurately located by the virtual beams in the VGTM. Then, the cut quality of the diamond's crown pattern can be effectively estimated according to the feature extraction results. The algorithm is demonstrated with a real machine vision system.