Curvature Estimation in Oriented Patterns Using Curvilinear Models Applied to Gradient Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Shadows, Shading, and Projective Ambiguity
Shape, Contour and Grouping in Computer Vision
A Physics-Based Approach to Interactive Segmentation
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
On Computing Visual Flows with Boundaries: The Case of Shading and Edges
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
The Perceptual Organization of Texture Flow: A Contextual Inference Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hue geometry and horizontal connections
Neural Networks - 2004 Special issue Vision and brain
3D-spline reconstruction using shape from shading: Spline from shading
Image and Vision Computing
Surface flows for image-based shading design
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
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Many presume that parsing the shadows out of an image is a high-level task, because of the global nature of the shadow formation process. But shape-from-shading algorithms are low-level, in the sense that they seek solutions (surface normals or depth values) directly from image intensities. A dilemma arises: since shape-from-shading involves an illumination term, shadows must first be identified. We show that a structure intermediate between intensities and surfaces-the shading flow field-provides a solution to this dilemma. Our analysis is based on the observation that the geometric information that can be derived from images supports different inferences than the photometric information, and our specific goal will be to articulate this geometric structure and to show how shading flow fields can be reliably computed.