Learning Local Evidence for Shading and Reflectance

A fundamental, unsolved vision problem is to distinguish image intensity variations caused by surface normal variations from those caused by re ectance changes{ie, to tell shading from paint. A solution to this problem is necessary for machines to interpret images as people do and could have many

[1]  Edward H. Adelson,et al.  Recovering reflectance and illumination in a world of painted polyhedra , 1993, 1993 (4th) International Conference on Computer Vision.

[2]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[3]  Zoubin Ghahramani,et al.  Factorial Learning and the EM Algorithm , 1994, NIPS.

[4]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[5]  N. Graham,et al.  Investigating simple and complex mechanisms in texture segregation using the speed-accuracy tradeoff method , 1995, Vision Research.

[6]  C. von der Malsburg,et al.  Improving object recognition by transforming Gabor filter responses. , 1996, Network.

[7]  Paul A. Viola,et al.  Bayesian Model of Surface Perception , 1997, NIPS.

[8]  Paul A. Viola,et al.  Texture recognition using a non-parametric multi-scale statistical model , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[9]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[10]  William T. Freeman,et al.  Learning low-level vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  Paul A. Viola,et al.  Boosting Image Retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  William H. Press,et al.  Numerical recipes in C , 2002 .