Area-efficient robust Madaline based on continuous valued number system

Zamanlooy, Babak; Mirhassani, Mitra

doi:10.1016/j.neucom.2014.03.029

Cited by 4 publications

(1 citation statement)

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“…As the third generation of artificial neural networks, spiking neural networks (SNNs) have been successfully applied in image processing [17,18]. The neural network methods have also been applied in various related fields such as system stability [19], consensus problems for multi-agent systems [20] and multiple valued logic [21]. Spikes reveal a paradox in our knowledge of the brain.…”

Section: Introductionmentioning

confidence: 99%

Perception of noise in global illumination algorithms based on spiking neural network

Constantin

Rammouz

et al. 2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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This paper proposes a reduced reference quality assessment model based on spiking neural network (SNN) in order to predict which image highlights perceptual noise in unbiased global illumination algorithms. These algorithms provide photo-realistic images by increasing the number of paths as proved by Monte Carlo theory. The objective is to find the number of paths that are required in order to ensure that most of the observers cannot perceive noise in any part of the image. A comparative study of this model with human psycho-visual scores demonstrates the good consistency between these scores and the learning model quality measures. The proposed model that uses a simple architecture composed only from two parallel spike pattern association neurons (SPANs) has been also compared with other learning model like SVM and gives satisfactory performance.

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Section: Introductionmentioning

confidence: 99%