Detection of surface orientation and motion from texture by a stereoogical technique.
Artificial Intelligence
Improved methods of estimating shape from shading using the light source coordinate system
Artificial Intelligence
Artificial Intelligence
The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Imaging of Fractal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Boundary Detection Based on the Long Correlation Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from shading
Time series: data analysis and theory
Time series: data analysis and theory
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Where and Why Local Shading Analysis Works
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Shape from Texture without Boundaries
International Journal of Computer Vision
Object material classification by surface reflection analysis with a time-of-flight range sensor
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Adaptive object recognition with image feature interpolation
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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A new 3D texture model is developed by considering the scene image as the superposition of a random texture image on a smooth shaded image. The whole image is analyzed using a patch-by-patch process. Each patch is assumed to be a tilted and slanted texture plane. A modified reflectance map function is applied to describe the deterministic part, and the fractional differencing periodic model is chosen to describe the random texture because of its good performance in texture synthesis and its ability to represent the coarseness and the pattern of the surface at the same time. An orthographical projection technique is developed to deal with this particular random model, which has a nonisotropically distributed texture pattern. For estimating the parameter, a hybrid method that uses both the least square and the maximum-likelihood estimates is applied directly to the given intensity function. By using these parameters, the synthesized image is obtained and used to reconstruct the original image.