Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Clustering within Integrate-and-Fire Neurons for Image Segmentation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The evidence for neural information processing with precise spike-times: A survey
Natural Computing: an international journal
Image Segmentation by Networks of Spiking Neurons
Neural Computation
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Hebbian learning with winner take all for spiking neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Image Segmentation Based on Adaptive Cluster Prototype Estimation
IEEE Transactions on Fuzzy Systems
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
A Mathematical Model of Retinal Ganglion Cells and Its Applications in Image Representation
Neural Processing Letters
| Hi-index | 0.00 |
The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are present in images. Artificial neural networks have been well developed so far. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, 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 in image segmentation and edge detection. Results obtained confirm the validity of the approach.