Fast sigmoidal networks via spiking neurons
Neural Computation
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Cell Segmentation with Median Filter and Mathematical Morphology Operation
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Segmentation of Blood Images Using Morphological Operators
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Hebbian learning with winner take all for spiking neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Segmentation of histopathological section using snakes
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
A quantitative criterion to evaluate color segmentations application to cytological images
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
IEEE Transactions on Image Processing
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy for cell microscopic image segmentation. Results obtained confirm the validity of the approach. The strategy is performed on cytological color images. Quantitative measures are used to evaluate the resulting segmentations.