What does the retina know about natural scenes?
Neural Computation
Shape from Texture Using Local Spectral Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct computation of shape cues using scale-adapted spatial derivative operators
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
Computing Local Surface Orientation and Shape from Texture forCurved Surfaces
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Shape from Periodic Texture Using the Eigenvectors of Local Affine Distortion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Texture Gradient Equation for Recovering Shape from Texture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust estimation of multiple surface shapes from occluded textures
ISCV '95 Proceedings of the International Symposium on Computer Vision
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Shape from Texture Via Fourier Analysis
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Modelling Spatio-Temporal Saliency to Predict Gaze Direction for Short Videos
International Journal of Computer Vision
ACM SIGGRAPH Asia 2009 papers
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Automatic detection of pain intensity
Proceedings of the 14th ACM international conference on Multimodal interaction
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This paper addresses the question: at the level of cortical cells present in the primary area V1, is the information sufficient to extract the local perspective from the texture? Starting from a model of complex cells in visual area V1, we propose a biologically plausible algorithm for frequency analysis applied to the shape from texture problem. First, specific log-normal filters are designed in replacement of the classical Gabor filters because of their theoretical properties and of their biological plausibility. These filters are separable in frequency and orientation and they better sample the image spectrum which makes them appropriate for any pattern analysis technique. A method to estimate the local frequency in the image, which discards the need to choose the best local scale, is designed. Based on this frequency analysis model, a local decomposition of the image into patches leads to the estimation of the local frequency variation which is used to solve the problem of recovering the shape from the texture. From the analytical relation between the local frequency and the geometrical parameters, under perspective projection, it is possible to recover the orientation and the shape of the original image. The accuracy of the method is evaluated and discussed on different kind of textures, both regular and irregular, with planar and curved surfaces and also on natural scenes and psychophysical stimuli. It compares favorably to the best existing methods, with in addition, a low computational cost. The biological plausibility of the model is finally discussed.