A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An Algebraic Approach to Surface Reconstruction from Gradient Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Face Recognition using 2.5D Shape Information
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Combinatorial Surface Integration
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Recovering Facial Shape Using a Statistical Model of Surface Normal Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving spanning trees using the heat equation
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
A graph-spectral approach to shape-from-shading
IEEE Transactions on Image Processing
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This paper presents a method to decompose a field of surface normals (needle-map). A diffusion process is used to model the flow of height information induced by a field of surface normals. The diffusion kernel can be decomposed into eigenmodes, each corresponding to approximately independent modes of variation of the flow. The surface normals can then be diffused using a modified kernel with the same eigenmodes but different coefficients. When used as part of a surface integration process, this procedure allows choosing the trade-off between local and global influence of each eigenmode in the modified field of surface normals. This graph-spectral method is illustrated with surface normals extracted from a face. Experiments are carried with local affinity functions that convey both the intrinsic and extrinsic geometry of the surface, and an information-theoretic definition of affinity.