2 research outputs found
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Address event representation (AER) cameras have recently attracted more
attention due to the advantages of high temporal resolution and low power
consumption, compared with traditional frame-based cameras. Since AER cameras
record the visual input as asynchronous discrete events, they are inherently
suitable to coordinate with the spiking neural network (SNN), which is
biologically plausible and energy-efficient on neuromorphic hardware. However,
using SNN to perform the AER object classification is still challenging, due to
the lack of effective learning algorithms for this new representation. To
tackle this issue, we propose an AER object classification model using a novel
segmented probability-maximization (SPA) learning algorithm. Technically, 1)
the SPA learning algorithm iteratively maximizes the probability of the classes
that samples belong to, in order to improve the reliability of neuron responses
and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced
in SPA to locate informative time points segment by segment, based on which
information within the whole event stream can be fully utilized by the
learning. Extensive experimental results show that, compared to
state-of-the-art methods, not only our model is more effective, but also it
requires less information to reach a certain level of accuracy.Comment: AAAI 2020 (Oral
An extended modular processing pipeline for event-based vision in automatic visual inspection
Dynamic Vision Sensors differ from conventional cameras in that only intensity changes of individual pixels are perceived and transmitted as an asynchronous stream instead of an entire frame. The technology promises, among other things, high temporal resolution and low latencies and data rates. While such sensors currently enjoy much scientific attention, there are only little publications on practical applications. One field of application that has hardly been considered so far, yet potentially fits well with the sensor principle due to its special properties, is automatic visual inspection. In this paper, we evaluate current state-of-the-art processing algorithms in this new application domain. We further propose an algorithmic approach for the identification of ideal time windows within an event stream for object classification. For the evaluation of our method, we acquire two novel datasets that contain typical visual inspection scenarios, i.e., the inspection of objects on a conveyor belt and during free fall. The success of our algorithmic extension for data processing is demonstrated on the basis of these new datasets by showing that classification accuracy of current algorithms is highly increased. By making our new datasets publicly available, we intend to stimulate further research on application of Dynamic Vision Sensors in machine vision applications