Relative Magnitude of Gaussian Curvature Using Neural Network and Object Rotation of Two Degrees of Freedom
Yi Ding, Yuji Iwahori, Tsuyoshi Nakamura, Lifeng He, Robert J. Woodham, Hidenori Itoh · 2007
We�propose a new approach to recover the relative magnitude of Gaussian curvature�from multiple images. Previous approaches recover the sign of Gaussian curvature from the spatial relationship of points mapped onto a sphere. Here, the relative magnitude of Gaussian curvature is recovered at each point. No calibration object is required. Instead, the test object itself is rotated in both the vertical and horizontal directions to estimate the position coordinates of a marker�point. An RBF neural network learns the mapping of intensities to marker� position coordinates along a virtual sphere. That is, self-calibration is performed by moving a marker�point. The neural network represents the mapping of observed image intensities�to coordinates on a virtual sphere.