Search Results for author:
Found 16 papers, 3 papers with code
Sup3r: A Semi-Supervised Algorithm for increasing Sparsity, Stability, and Separability in Hierarchy Of Time-Surfaces architectures
The Hierarchy Of Time-Surfaces (HOTS) algorithm, a neuromorphic approach for feature extraction from event data, presents promising capabilities but faces challenges in accuracy and compatibility with neuromorphic hardware.
Flow-Based Visual Stream Compression for Event Cameras
The introduced method itself is shown to achieve an average compression ratio of 2. 81 on a variety of event-camera datasets with the evaluation configuration used.
An event-based implementation of saliency-based visual attention for rapid scene analysis
The saliency map model, originally developed to understand the process of selective attention in the primate visual system, has also been extensively used in computer vision.
Adaptive Global Decay Process for Event Cameras
To achieve this, at least one of three main strategies is applied, namely: 1) constant temporal decay or fixed time window, 2) constant number of events, and 3) flow-based lifetime of events.
Event Based Time-Vectors for auditory features extraction: a neuromorphic approach for low power audio recognition
In recent years tremendous efforts have been done to advance the state of the art for Natural Language Processing (NLP) and audio recognition.
Neutron-Induced, Single-Event Effects on Neuromorphic Event-based Vision Sensor: A First Step Towards Space Applications
The results show that event-based cameras are capable of functioning in a space-like, radiative environment with a signal-to-noise ratio of 3. 355.
Real-time high speed motion prediction using fast aperture-robust event-driven visual flow
Optical flow is a crucial component of the feature space for early visual processing of dynamic scenes especially in new applications such as self-driving vehicles, drones and autonomous robots.
Event-Based Features Selection and Tracking from Intertwined Estimation of Velocity and Generative Contours
This paper presents a new event-based method for detecting and tracking features from the output of an event-based camera.
When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning frawmework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware
We introduce in this paper the principle of Deep Temporal Networks that allow to add time to convolutional networks by allowing deep integration principles not only using spatial information but also increasingly large temporal window.
Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities
This paper introduces a framework of gesture recognition operating on the output of an event based camera using the computational resources of a mobile phone.
A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors
This paper introduces an unsupervised time-oriented event-based machine learning algorithm building on the concept of hierarchy of temporal descriptors called time surfaces.
Event-based Face Detection and Tracking in the Blink of an Eye
We will rely on a new feature that has never been used for such a task that relies on detecting eye blinks.
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification
Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation.
Ranked #10 on
Robust classification
on N-ImageNet
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data.
HFirst: A Temporal Approach to Object Recognition
The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems.
STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation using Neurons, Precise Timing, Delays, and Synchrony
There has been significant research over the past two decades in developing new platforms for spiking neural computation.
