Object Segmentation for Vehicle Video and Dental CBCT by Neuromorphic Convolutional Recurrent Neural Network

  • Woo-Sup Han
  • Il Song Han
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 751)


The neuromorphic visual processing inspired by the biological vision system of brain offers an alternative process into applying machine vision in various environments. With the emerging interests on transportation safety enhancement of Advanced Driver Assistance System or a driverless car, the neuromorphic convolutional recurrent neural networks was proposed and tested for the night-time vehicle or VRU detection. The effectiveness of proposed convolutional-recurrent neural networks of neuromorphic visual processing was evaluated successfully for the object detection without optimized complex template matching or prior denoising neural network. The real life road video dataset at night time demonstrated 98% of successful detection/segmentation rate with 0% False Positive. The robust performance of proposed convolutional-recurrent neural network was also applied successfully to the tooth segmentation of dental X-ray 3D CT including the gum region. The feature extraction was based on neuromorphic visual processing filters of either hand-cut filters mimicking the visual cortex experimentation or the auto-encoder filter trained by partial X-ray images. The consistent performance of either hand-cut filters or the small auto-encoder filters demonstrated the feasibility of real-time and robust neuromorphic vision implemented by either the small embedded system or the portable computer.


Neuromorphic visual processing Visual cortex Machine vision Vehicle detection CBCT Convolution-recurrent neural networks 



The research of 3D tooth segmentation was sponsored by Vatech, Korea. We are thankful to Mr. Ik Kim for his cooperation to our research on tooth segmentation using Dental X-ray images, specifically on the guidance and discussion about developing the medical applications.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.ODIGA LtdLondonUK
  2. 2.Graduate School for Green TransportationKorea Advanced Institute of Science and TechnologyDaejonKorea

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