Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and boundary segmentation via minimal-length encoding on the connection machine
Proceedings of a workshop on Image understanding workshop
Bivariate interpolation and smooth surface fitting based on local procedures
Communications of the ACM
Bayesian Modeling of Uncertainty in Low-Level Vision
Bayesian Modeling of Uncertainty in Low-Level Vision
Robot Vision
A Full Bayesian Approach to Curve and Surface Reconstruction
Journal of Mathematical Imaging and Vision
A Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Antifaces: A Novel, Fast Method for Image Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Recognizing image "style" and activities in video using local features and naive Bayes
Pattern Recognition Letters
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
| Hi-index | 0.14 |
In order to use interpolated data wisely, it is important to have reliability and confidence measures associated with it. A method for computing the reliability at each point of any linear functional of a surface reconstructed using regularization is presented. The proposed method is to define a probability structure on the class of possible objects and compute the variance of the corresponding random variable. This variance is a natural measure for uncertainty, and experiments have shown it to correlate well with reality. The probability distribution used is based on the Boltzmann distribution. The theoretical part of the work utilizes tools from classical analysis, functional analysis, and measure theory on function spaces. The theory was tested and applied to real depth images. It was also applied to formalize a paradigm of optimal sampling, which was successfully tested on real depth images.