Object segmentation for vehicle video and dental CBCT by neuromorphic convolutional recurrent neural network

Abstract

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.