Image segmentation by networks of spiking neurons.
A network of leaky integrate-and-fire (IAF) neurons is proposed to segment gray-scale images. The network architecture with local competition between neurons that encode segment assignments of image blocks is motivated by a histogram clustering approach to image segmentation. Lateral excitatory connections between neighboring image sites yield a local smoothing of segments. The mean firing rate of class membership neurons encodes the image segmentation. A weight modification scheme is proposed that estimates segment-specific prototypical histograms. The robustness properties of the network implementation make it amenable to an analog VLSI realization. Results on synthetic and real-world images demonstrate the effectiveness of the architecture.
Buhmann
Joachim M
JM
Swiss Federal Institute of Technology, CH-8092 Zurich, Switzerland. jbuhmann@inf.ethz.ch
Lange
Tilman
T
Ramacher
Ulrich
U
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001921:Brain / Q000502:physiology; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D013569:Synapses / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology; D014795:Visual Pathways / Q000502:physiology