Pattern Recognition
Image Estimation Using Doubly Stochastic Gaussian Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
The construction of multivariate distributions from Markov random fields
Journal of Multivariate Analysis
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
| Hi-index | 0.00 |
From the statistical point of view, segmentation methodsare dependent upon how the characteristics in image areformulated and where they are extracted from. In thispaper, the joint conditional probability is exploited tocharacterize the statistical properties and is also localizedto better capture the local properties of the neighborhood.Two different neighborhood configurations are definedand each of them incorporates with given prior informationthrough Bayesian formula. It is considered as a criterionfunction in the proposed method. The proposedmethod segments images by maximizing the given criterionfunction. The results show the comparison of theresults from four different methods depending on thecombination of neighborhood configurations with priorinformation.