Pose-invariant object recognition for event-based vision with slow-ELM

Rohan Ghosh, Tang Siyi, Mahdi Rasouli, Nitish V. Thakor, Sunil L. Kukreja

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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° of 2D pose.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
EditorsAlessandro E.P. Villa, Paolo Masulli, Antonio Javier Pons Rivero
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319447803
StatePublished - 2016
Externally publishedYes
Event25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016 - Barcelona, Spain
Duration: Sep 6 2016Sep 9 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9887 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016


  • Extreme learning machines
  • Neuromorphic vision
  • Object recognition
  • Slow feature analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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