by Krishnamurthy V. Vemuru †
Riverside Research, 2900 Crystal Dr., Arlington, VA 22202, USA
†Current Address:
BrainChip Inc., 23041 Avenida de la Carlota, Laguna Hills, CA 92653, USA.
AbstractEdge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step of noise reduction using a Gaussian kernel and a final step to remove the weak edges by the hysteresis threshold. In this work, a spike-based computing algorithm is presented as a neuromorphic analogue of the Canny edge detector, where the five steps of the conventional algorithm are processed using spikes. A spiking neural network layer consisting of a simplified version of a conductance-based Hodgkin–Huxley neuron as a building block is used to calculate the gradients. The effectiveness of the spiking neural-network-based algorithm is demonstrated on a variety of images, showing its successful adaptation of the principle of the Canny edge detector. These results demonstrate that the proposed algorithm performs as a complete spike domain implementation of the Canny edge detector.
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Conclusions
In conclusion, we present a spiking neural network (SNN) implementation of the Canny edge detector as its neuromorphic analogue by introducing algorithms for spike based computation in the five steps of the conventional algorithm with the conductance-based Hodgkin–Huxley neuron as the building block. Edge detection examples are presented for RGB and infrared images with a variety of objects.
A quantitative comparison of the edge maps from the SNN-based Canny detector, conventional Canny detector and a Sobel detector using the F1�1-score as a metric, shows that the neuromorphic implementation of the Canny edge detector achieves better performance. The SNN architecture of the Canny edge detector also offers promise for image processing and object recognition applications in the infrared domain. The SNN Canny edge detector is also evaluated with medical images, and the edge maps compare well with the edges generated with the conventional Canny detector. Future work will focus on the implementation of the algorithm on an FPGA or on a neuromorphic chip for hardware acceleration and testing in an infrared object detection task potentially with edge maps as features together with a pre-processing layer to remove any distortions, enhance contrast, remove blur, etc., and a spiking neuron layer as a final layer to introduce a machine learning component. An extension of the SNN architecture of the Canny edge detector with additional processing layers for object detection in LiDAR point clouds would be another interesting new direction of research [39].
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