Multimodal score-level fusion using hybrid ga-pso for multibiometric system

Due to the limitations that unimodal systems suffer from, Multibiometric systems have gained much interest in the research community on the grounds that they alleviate most of these limitations and are capable of producing better accuracies and performances. One of the important steps to reach this is the choice of the fusion techniques utilized. In this paper, a modeling step based on a hybrid algorithm, that includes Particle Swarm Optimization and Genetic Algorithm, is proposed to combine two biometric modalities at the score level. This optimization technique is employed to find the optimum weights associated to the modalities being fused. An analysis of the results is carried out on the basis of comparing the EER accuracies and ROC curves of the fusion techniques. Furthermore, the execution speed of the hybrid approach is discussed and compared to that of the single optimization algorithms, GA and PS

Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition

Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and different computational features and complexities. Consequently, there is a need to define the right level of abstraction from biology in order to get the best performance in accurate, efficient and fast inference in neuromorphic hardware. In this context, we explore the impact of synaptic and membrane leakages in spiking neurons. We confront three neural models with different computational complexities using feedforward and recurrent topologies for event-based visual and auditory pattern recognition. Our results showed that, in terms of accuracy, leakages are important when there are both temporal information in the data and explicit recurrence in the network. Additionally, leakages do not necessarily increase the sparsity of spikes flowing in the network. We also investigated the impact of heterogeneity in the time constant of leakages. The results showed a slight improvement in accuracy when using data with a rich temporal structure, thereby validating similar findings obtained in previous studies. These results advance our understanding of the computational role of the neural leakages and network recurrences, and provide valuable insights for the design of compact and energy-efficient neuromorphic hardware for embedded systems.</p

Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition

Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and different computational features and complexities. Consequently, there is a need to define the right level of abstraction from biology in order to get the best performance in accurate, efficient and fast inference in neuromorphic hardware. In this context, we explore the impact of synaptic and membrane leakages in spiking neurons. We confront three neural models with different computational complexities using feedforward and recurrent topologies for event-based visual and auditory pattern recognition. Our results showed that, in terms of accuracy, leakages are important when there are both temporal information in the data and explicit recurrence in the network. Additionally, leakages do not necessarily increase the sparsity of spikes flowing in the network. We also investigated the impact of heterogeneity in the time constant of leakages. The results showed a slight improvement in accuracy when using data with a rich temporal structure, thereby validating similar findings obtained in previous studies. These results advance our understanding of the computational role of the neural leakages and network recurrences, and provide valuable insights for the design of compact and energy-efficient neuromorphic hardware for embedded systems.</p

Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition

Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and different computational features and complexities. Consequently, there is a need to define the right level of abstraction from biology in order to get the best performance in accurate, efficient and fast inference in neuromorphic hardware. In this context, we explore the impact of synaptic and membrane leakages in spiking neurons. We confront three neural models with different computational complexities using feedforward and recurrent topologies for event-based visual and auditory pattern recognition. Our results showed that, in terms of accuracy, leakages are important when there are both temporal information in the data and explicit recurrence in the network. Additionally, leakages do not necessarily increase the sparsity of spikes flowing in the network. We also investigated the impact of heterogeneity in the time constant of leakages. The results showed a slight improvement in accuracy when using data with a rich temporal structure, thereby validating similar findings obtained in previous studies. These results advance our understanding of the computational role of the neural leakages and network recurrences, and provide valuable insights for the design of compact and energy-efficient neuromorphic hardware for embedded systems

Reduction of speckle in SAR images using wavelet transform: the gamma geberalised gaussian filter

International audienc

Utilisation d'un modèle symbolique pour l'interprétation d'images Radar à Ouverture Synthétique

Au fil des années, l'imagerie radar à synthèse d'ouverture (RSO ) s'affirme comme une modalité fiable et pertinente en télédétection. En effet, la détection du réseau routier sur ce type d'images par des opérateurs de bas niveau tels que les détecteurs de lignes nous donne un taux de fausses détection élevé. Ceci est dû notamment à la présence du speckle. D'où la nécessité de faire suivre l'étape de bas-niveau par une étape de plus haut niveau, dans laquelle nous injectons des informations structurelles sur la forme des routes par l'intermédiaire d'un modèle symbolique. A cet effet, nous proposons dans cette thèse de traiter deux niveaux de modèles symboliques, par complexité croissante. Dans un premier temps nous traitons un modèle assez complet issu de données cartographiques, avec des informations spatiales relativement précises. Dans un second temps nous utilisons un modèle grossier correspondant à un schéma "manuel" représentant les objets à extraire avec des indications approximatives sur leurs formes et leurs positions relatives. Ainsi, nous proposons dans cette thèse deux approches pour intégrer au mieux cette information exogène. En effet, dans une première partie, nous proposons une méthode markovienne qui permet par l'intermédiaire du terme a priori d'introduire des connaissances sur les objets recherchés. Dans une second partie, nous proposons une méthode de recherche de chemin optimal basée sur la programmation dynamique. Enfin, nous présentons notre méthode d'extraction de réseau routier sur les images radar (RSO ) en utilisant des données symboliques ainsi que quelques applications de notre travail.Synthetic Aperture Radar (SAR) instruments are active microwave sensors that operate independently of time of day and weather conditions. SAR can achieve high resolution from long range and provide information about the physical structure and the electrical properties of remotely sensed objects. There has been a growing interest in SAR for automatic target recognition. We are interested in this thesis by road detection in spaceborne SAR (Synthetic Aperture Radar images. Several approaches have been proposed in the literature. They generally consist of two steps: in the first step they use a local operator like edge and line detectors and then they apply a global criterion which incorporates additional knowledge about the structure of the objects to be detected. The aim of this thesis is the detection of road on SAR images starting from a graphical sketch of road defined by a user which is considered as a model of road. To do this we propose to compare two methods. The first one combines both local and global criteria based on Markov Field(MRF). It is based on a previously published methods for road detection in SAR images. The second method uses a dynamic programming, it defines a cost, which depends on local information, and performs a summation minimization process in a graph. The results obtained with the two approaches applied to different SAR images are presented and evaluated with an objective criterion. Finnally, we applied our method based of dynamic programming to extract road network on the different SAR images and we present some applications of our work.PARIS-CNAM (751032301) / SudocPARIS-Télécom ParisTech (751132302) / SudocSudocFranceF