Search

We propose a neuromimetic architecture capable of always-on pattern recognition, i.e. at any time during processing. To achieve this, we have extended an existing event-based algorithm (Lagorce et al., 2017), which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events captured by a neuromorphic camera, these time surfaces allow to encode the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we have extended this method to improve its performance. First, we add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns (Grimaldi et al., 2021). We also provide a new mathematical formalism that allows an analogy to be drawn between the HOTS algorithm and Spiking Neural Networks (SNN). Following this analogy, we transform the offline pattern categorization method into an online and event-driven layer. This classifier uses the spiking output of the network to define new time surfaces and we then perform the online classification with a neuromimetic implementation of a multinomial logistic regression. These improvements not only consistently increase the performance of the network, but also bring this event-driven pattern recognition algorithm fully online. The results have been validated on different datasets: Poker-DVS (Serrano-Gotarredona and Linares-Barranco, 2015), N-MNIST (Orchard, Jayawant et al., 2015) and DVS Gesture (Amir et al., 2017). This demonstrates the efficiency of this bio-realistic SNN for ultra-fast object recognition through an event-by-event categorization process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Johnson-Nyquist noise is the electronic noise generated by the thermal agitation of charge carriers, which increases when the sensor overheats. Current high-speed cameras used in low-light conditions are often cooled down to reduce thermal noise and increase their signal to noise ratio. These sensors, however, record hundreds of frames per second, which takes time, requires energy, and heavy computing power due to the substantial data load. Event-based sensors benefit from a high temporal resolution and record the information in a sparse manner. Based on an asynchronous time-based image sensor, we developed another version of this event-based camera whose pixels were designed for low-light applications and added a Peltier-effect-based cooling system at the back of the sensor in order to reduce thermal noise. We show the benefits from thermal noise reduction and study the improvement of the signal to noise ratio in the estimation of event-based normal flow norm and angle and particle tracking in microscopy.

This paper deals with the calibration of a cylindric omnidirectional imaging system, based on a rotating 2,048 pixels linear camera which provides high definition panoramas. The two-step algorithm relies on line-segment projections as calibration features, which are sinusoidal curves. We proposed a cylindrical line detection, based on the dual principle of the Hough transform. Moreover, the use of Plucker coordinates introduces some new characteristics in the calibration process. This kind of formalism allow a linearization of the cylindrical projection, which is non-linear in the usual way. Results obtained from this first step are used to evaluate one of the intrinsics, the other one being determined by a linear criterion minimization in the dual space, i.e. the sines magnitudes space. [ABSTRACT FROM AUTHOR]