A finite mixture model for image segmentation
Statistics and Computing
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
A principal component regression strategy for estimating motion
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Error concealment by means of clustered blockwise PCA
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
Image segmentation based on Gabor filter and MRF model
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Integrating local distribution information with level set for boundary extraction
Journal of Visual Communication and Image Representation
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
Spatially variant mixtures of multiscale ARMA model for SAR imagery segmentation
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
An efficient unsupervised mixture model for image segmentation
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A mixture model based markov random field for discovering patterns in sequences
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Automated PET-guided liver segmentation from low-contrast CT volumes using probabilistic atlas
Computer Methods and Programs in Biomedicine
A clustering based feature selection method in spectro-temporal domain for speech recognition
Engineering Applications of Artificial Intelligence
A finite mixture model for detail-preserving image segmentation
Signal Processing
Unsupervised classification of SAR images using normalized gamma process mixtures
Digital Signal Processing
A novel clustering algorithm based Gaussian mixture model for image segmentation
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
| Hi-index | 0.00 |
Gaussian mixture models (GMMs) constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation, where spatially constrained mixture models have been trained using the expectation-maximization (EM) framework. In this letter, we elaborate on this method and propose a new methodology for the M-step of the EM algorithm that is based on a novel constrained optimization formulation. Numerical experiments using simulated images illustrate the superior performance of our method in terms of the attained maximum value of the objective function and segmentation accuracy compared to previous implementations of this approach.