Image segmentation based on situational DCT descriptors
Pattern Recognition Letters
International Journal of Computer Vision
Unsupervised segmentation of medical images using DCT coefficients
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Image and Vision Computing
Visual detection of novel terrain via two-class classification
Proceedings of the 2009 ACM symposium on Applied Computing
Neural Network Based Terrain Classification Using Wavelet Features
Journal of Intelligent and Robotic Systems
Radial basis function based level set interpolation and evolution for deformable modelling
Image and Vision Computing
Robotics and Autonomous Systems
Comparison of different approaches to visual terrain classification for outdoor mobile robots
Pattern Recognition Letters
Hi-index | 0.00 |
In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a maximum descriptiveness criterion, and allows to choose any number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)