Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of methods for recovering quadrics in triangle meshes
ACM Computing Surveys (CSUR)
Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making colors worth more than a thousand words
Proceedings of the 2008 ACM symposium on Applied computing
Classification of Local Surface Using Neural Network and Object Rotation of Two Degrees of Freedom
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Improvement of Accuracy for Gaussian Curvature Using Modification Neural Network
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Relative magnitude of gaussian curvature from shading images using neural network
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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We compute the sign of Gaussian curvature using a purely geometric definition. Consider a point p on a smooth surface S and a closed curve 驴 on S which encloses p. The image of 驴 on the unit normal Gaussian sphere is a new curve β. The Gaussian curvature at p is defined as the ratio of the area enclosed by 驴 over the area enclosed by β as 驴 contracts to p. The sign of Gaussian curvature at p is determined by the relative orientations of the closed curves 驴 and β.We directly compute the relative orientation of two such curves from intensity data. We employ three unknown illumination conditions to create a photometric scatter plot. This plot is in one-to-one correspondence with the subset of the unit Gaussian sphere containing the mutually illuminated surface normals. This permits direct computation of the sign of Gaussian curvature without the recovery of surface normals. Our method is albedo invariant. We assume diffuse reflectance, but the nature of the diffuse reflectance can be general and unknown. Error analysis on simulated images shows the accuracy of our technique. We also demonstrate the performance of this methodology on empirical data.