The cortex transform: rapid computation of simulated neural images
Computer Vision, Graphics, and Image Processing
Visual reconstruction
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Data fusion in robotics and machine intelligence
Data fusion in robotics and machine intelligence
Variational methods in image segmentation
Variational methods in image segmentation
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter Estimation for Optimal Object Recognition: Theory andApplication
International Journal of Computer Vision
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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It is of practical importance to fuse data obtained by multiple sensors for improving the performance of computer vision systems. This paper introduces an algorithm for pixel-based data fusion on the variational framework. An adaptive system fuses data effectively using a variational technique. Previously, we have introduced a technique to fuse gray-scale image and texture extracting features for segmenting an image with both textured and non-textured surfaces. This paper extends the study for more general multi-valued data and improve the previous algorithm in terms of the performance and speed.