Raman spectroscopy is expected as a non-invasive and effective method for the accurate identification of peripheral nerves. However, the discrimination basis of the Raman spectroscopic method is sometimes ambiguous due to the partial and complicated information of tissue molecules reflected in Raman spectra.
In this study, we developed a method for identifying spectral features in Raman spectroscopic detection of peripheral nerves by utilizing a support vector machine (SVM). Raman spectral features for the discrimination of tissue species were extracted by analyzing the feature weight obtained from the linear SVM classifier.
In this study, we focus on automatic three-dimensional (3D) face reconstruction from two-dimensional (2D) face images using a deep learning model. The conventional methods have been used to develop models that can reconstruct 3D faces from 2D images. However, for Japanese faces, the models cannot accurately reconstruct images, large errors occur in areas such as the nose and mouth, because most of the training data are foreigner’s face images. To solve this problem, we proposed a method that uses not only a frontal 2D face image but also a side-view 2D face image for the 3D face reconstruction, and the resulting 3D model is a combination of two 3D reconstructed models, which are created from the frontal and side-view 2D face images using iterative closest point algorithm. As a result, the accuracy of the proposed method is better than the conventional method.
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