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An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations.

Hanyu Wang1, Jiangtao Xu1, Zhiyuan Gao1

  • 1School of Electronic Information Engineering, Tianjin University Tianjin, China.

Frontiers in Neuroscience
|November 22, 2016
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Summary

This study introduces a novel event-based neurobiological recognition system for asynchronous address-event representation (AER) image sensors. The system effectively recognizes objects in multiple orientations using single-orientation training data, enhancing computer vision capabilities.

Keywords:
MNISTaddress-event representation (AER)dynamic vision senses (DVS)multi-orientation object recognitionspiking neural network (SNN)visual tracking

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Area of Science:

  • Neuroscience and Computer Vision
  • Event-based Vision Processing

Background:

  • Object recognition systems often require extensive training data across various orientations.
  • Asynchronous Address-Event Representation (AER) image sensors offer efficient data capture but pose challenges for complex recognition tasks.

Purpose of the Study:

  • To develop an event-based neurobiological recognition system capable of multi-orientation object recognition.
  • To enable recognition of objects in multiple orientations using training samples with only single-orientation movement.

Main Methods:

  • Integration of recognition and tracking functions within a novel architecture.
  • Extraction of multi-scale, multi-orientation line features inspired by primate visual cortex models.
  • Utilizing a modified Gaussian blob tracking algorithm for orientation detection and object tracking simultaneously with feature extraction.

Main Results:

  • The system successfully performs event-based multi-orientation recognition on datasets like MNIST and dynamic vision sensor (DVS) acquired data.
  • Accuracy was improved by incorporating both ON and OFF events.
  • The proposed system demonstrates effective recognition of moving digits, pokers, and vehicles.

Conclusions:

  • The developed system provides a new tracking-recognition architecture for feedforward categorization systems.
  • An address reordering approach allows classification of multi-orientation objects using event-based data.
  • This research offers a novel method for multi-orientation object recognition with limited single-orientation training data.