A novel approach has been developed for fast registration of two sets of 3-D curves or surfaces. The technique is an extension of Besl and Mackay's iterative closest point (ICP) algorithm. This technique solves the computation complexity associated with the ICP algorithm b y applying a novel grid closest point (GCP) transform and a genetic algorithm to minimize the cost function. A detailed description of the algorithm is presented along with a comparison of its performance versus several registration techniques. Two applications are presented in this paper. In the first, the algorithm is used to register 2-D head contours extracted from CT/MRI data to correct for possible missalignment caused by motion artifact during scanning. I n the second, the algorithm is used to register 3-0 segments of the human jaw obtained using shape from shading technique. Registration using the GCP/GA technique is found to be significantly faster and of comparable accuracy than two popular techniques in the computer vision and medical imaging literature.
In this work, an automated image analysis procedure for the quantification of microstructure evolution during creep is proposed for evaluating scanning electron microscopy micrographs of a single crystal Ni-based superalloy before and after creep at 950 °C and 350 MPa. scanning electron microscopy-micrographs of γ/γ′ microstructures are transformed into binary images. Image analysis, which involves pixel by pixel classification and feature extraction, is then combined with a supervised machine learning algorithm to improve the binarization and the quality of the results. The binarization of the gray scale images is not always straight forward, especially when the difference in gray levels between the γ-channels and the γ′-phase is small. To optimize feature extraction, we utilized a series of bilateral filters as well as a machine learning algorithm, known as the gradient boosting method, that was used for training and classifying the micrograph pixels. After testing the two methods, the gradient boosting method was identified as the most effective. Subsequently, a Python routine was written and implemented for the automated quantification of the γ′ area fraction and the γ channel width. Our machine learning method is documented and the results of the automatic procedure are discussed based on results which we previously reported in the literature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.