Fusion of range and reflectance image data using Markov random fields

The problem of fusion of range and luminance data is addressed. Fusion is accomplished by minimization of an objective function which requires that the observed brightness agree with both the observed range image and a reflectivity model. The performance of this technique is evaluated.<<ETX>>

[1]  Jake K. Aggarwal,et al.  Experiments in combining intensity and range edge maps , 1983, Comput. Vis. Graph. Image Process..

[2]  Bir Bhanu,et al.  Representation and Shape Matching of 3-D Objects , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  B. K. Jenkins,et al.  Image restoration using a neural network , 1988, IEEE Trans. Acoust. Speech Signal Process..

[4]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Kenneth J. Overton,et al.  Using range data for inspecting printed wiring boards , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[6]  George Wolberg,et al.  Restoration of binary images using stochastic relaxation with annealing , 1985, Pattern Recognit. Lett..

[7]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.