Impact Factor 2.073

The world's most-cited Neurosciences journals

This article is part of the Research Topic

Neural Living Rooms

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Comput. Neurosci. | doi: 10.3389/fncom.2018.00103

Hough Transform Implementation For Event-Based Systems: Concepts and Challenges

 Sajjad Seifozzakerini1, 2*, Wei Y. Yau2, Kezhi Mao1 and Hossein Nejati3
  • 1Nanyang Technological University, Singapore
  • 2Institute for Infocomm Research (A*STAR), Singapore
  • 3Singapore University of Technology and Design, Singapore

Hough transform (HT) is one of the most well-known techniques in computer vision that has been the basis of many practical image processing algorithms. HT however is designed to work for frame-based systems such as conventional digital cameras. Recently, event-based systems such as Dynamic Vision Sensor (DVS) cameras, has become popular among researchers. Event-based cameras have a significantly high temporal resolution (1 microseconds), but each pixel can only detect change and not color. As such, the conventional image processing algorithms cannot be readily applied to event-based output streams. Therefore, it is necessary to adapt the conventional image processing algorithms for event-based cameras. This paper provides a systematic explanation, starting from extending conventional HT to 3D HT, adaptation to event-based systems, and the implementation of the 3D HT using Spiking Neural Networks (SNNs). Using SNN enables the proposed solution to be easily realized on hardware using FPGA, without requiring CPU or additional memory. In addition, we also discuss techniques for optimal SNN-based implementation using efficient number of neurons for the required accuracy and resolution along each dimension, without increasing the overall computational complexity. We hope that this will help to reduce the gap between event-based and frame-based systems.

Keywords: neuromorphic engineering (NE), dynamic vision sensor (DVS), parameter space, Spiking neural network (SNN), lateral inhibition, inhibitory connections, Event-based video, Shape detection, Line segment detection (LSD), concept, Generalized Hough Transform (GHT), Hough transform (HT)

Received: 23 Jun 2018; Accepted: 05 Dec 2018.

Edited by:

Frank M. Klefenz, Fraunhofer-Institut für Digitale Medientechnologie IDMT, Germany

Reviewed by:

Pilar Bachiller-Burgos, Universidad de Extremadura, Spain
Eduardo J. Bayro Corrochano, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico  

Copyright: © 2018 Seifozzakerini, Yau, Mao and Nejati. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Sajjad Seifozzakerini, Nanyang Technological University, Singapore, Singapore,