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

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° 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
PublisherSpringer Verlag
Pages455-462
Number of pages8
Volume9887 LNCS
ISBN (Print)9783319447803
DOIs
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)03029743
ISSN (Electronic)16113349

Other

Other25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016
CountrySpain
CityBarcelona
Period9/6/169/9/16

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ghosh, R., Siyi, T., Rasouli, M., Thakor, N. V., & Kukreja, S. L. (2016). Pose-invariant object recognition for event-based vision with slow-ELM. In Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings (Vol. 9887 LNCS, pp. 455-462). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9887 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-44781-0_54