Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM
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by
Rohan Ghosh, Siyi Tang, Mahdi Rasouli, Nitish Thakor, Sunil Kukreja
2019
Abstract
Neuromorphic image sensors produce activity-driven spiking output at every
pixel. These low-power consuming imagers which encode visual change information
in the form of spikes help reduce computational overhead and realize complex
real-time systems; object recognition and pose-estimation to name a few.
However, there exists a lack of algorithms in event-based vision aimed towards
capturing invariance to transformations. In this work, we propose a methodology
for recognizing objects invariant to their pose with the Dynamic Vision Sensor
(DVS). A novel slow-ELM architecture is proposed which combines the
effectiveness of Extreme Learning Machines and Slow Feature Analysis. The
system, tested on an Intel Core i5-4590 CPU, can perform 10,000 classifications
per second and achieves 1% classification error for 8 objects with views
accumulated over 90 degrees of 2D pose.
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