A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereoscopic processing in monkey visual cortex: a review
Early vision and beyond
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from Defocus vs. Stereo: How Different Really Are They?
International Journal of Computer Vision - Special issue on computer vision research at the Technion
Illumination for computer generated pictures
Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating Illumination Constraints in Deformable Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A neural network for recovering 3D shape from erroneous and few depth maps of shaded images
Pattern Recognition Letters
Numerical methods for shape-from-shading: A new survey with benchmarks
Computer Vision and Image Understanding
A possible neural mechanism for computing shape from shading
Neural Computation
Shape from shading through photometric motion
Pattern Analysis & Applications
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The goal of shape from shading (SFS) is to recover a relative depth map from the variations of image intensity associated to changes in surface shape. There have been very few attempts at developing biologically plausible solutions to this problem, and a sound neurophysiological basis is still missing. Here we present a biologically inspired approach to SFS, formulated in terms of the well-known linear-nonlinear model of neuronal responses. Without resorting to the image irradiance equation, which is at the heart of the traditional SFS algorithms, we submit the input image to a linear filter followed by nonlinear transformations modelled on the tuning curves of the disparity-selective binocular neurons. This yields plausible shape estimates, without requiring information regarding surface reflectance or illumination.