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The Academic World is being woken up to the future by the hugely talented team of researchers at Brainchip as they now spread their wings and publish their findings:
“Implementation of the Canny Edge Detector Using a Spiking Neural Network
Krishnamurthy V. Vemuru †
Citation: Vemuru, K.V. Implementation of the Canny Edge Detector Using a Spiking Neural Network. Future Internet 2022, 14, 371. https://doi.org/10.3390/fi14120371
Received: 6 June 2022
Accepted: 7 December 2022
Published: 11 December 2022
CurrentAddress:BrainChipInc.,23041AvenidadelaCarlota,LagunaHills,CA92653,USA.
Abstract: Edgedetectorsarewidelyusedincomputervisionapplicationstolocatesharpintensity 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.
Keywords: edge detection; segmentation; spiking neural networks; bio-inspired neurons
“Spiking neurons are regarded as the building blocks of the neural networks in the brain. Moreover, research in neuroscience indicates the spatiotemporal computing capabilities of spiking neurons play a role in the energy efficiency of the brain. In addition, spiking neurons leverage sparse time-based information encoding, event-triggered plasticity, and low-power inter- neuron signaling. In this context, neuromorphic computing hardware architecture and spike domain machine learning algorithms offer a low-power alternative to ANNs on von Neumann computing architectures. The availability of neuromorphic processors, such as IBM’s TrueNorth [1], Intel’s Loihi [2], and event-domain neural processors, for example, BrainChip’s Akida [3,4], which offers the flexibility to define both artificial neural network layers and spiking neuron layers, are motivating the research and development of new algorithms for edge computing. In the present work, we have investigated how one can program an algorithm for Canny type edge detection using a spiking neural network and spike-based computing.”
Merry Christmas and a joyful New Year to all.
My opinion only DYOR
FF
AKIDA BALLISTA
“Implementation of the Canny Edge Detector Using a Spiking Neural Network
Krishnamurthy V. Vemuru †
Citation: Vemuru, K.V. Implementation of the Canny Edge Detector Using a Spiking Neural Network. Future Internet 2022, 14, 371. https://doi.org/10.3390/fi14120371
Received: 6 June 2022
Accepted: 7 December 2022
Published: 11 December 2022
CurrentAddress:BrainChipInc.,23041AvenidadelaCarlota,LagunaHills,CA92653,USA.
Abstract: Edgedetectorsarewidelyusedincomputervisionapplicationstolocatesharpintensity 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.
Keywords: edge detection; segmentation; spiking neural networks; bio-inspired neurons
“Spiking neurons are regarded as the building blocks of the neural networks in the brain. Moreover, research in neuroscience indicates the spatiotemporal computing capabilities of spiking neurons play a role in the energy efficiency of the brain. In addition, spiking neurons leverage sparse time-based information encoding, event-triggered plasticity, and low-power inter- neuron signaling. In this context, neuromorphic computing hardware architecture and spike domain machine learning algorithms offer a low-power alternative to ANNs on von Neumann computing architectures. The availability of neuromorphic processors, such as IBM’s TrueNorth [1], Intel’s Loihi [2], and event-domain neural processors, for example, BrainChip’s Akida [3,4], which offers the flexibility to define both artificial neural network layers and spiking neuron layers, are motivating the research and development of new algorithms for edge computing. In the present work, we have investigated how one can program an algorithm for Canny type edge detection using a spiking neural network and spike-based computing.”
Merry Christmas and a joyful New Year to all.
My opinion only DYOR
FF
AKIDA BALLISTA
The young lad is looking much perkier today Rise 
I've had a few people call him Count Barkula. Yeah he's looking much better got another vet visit soon so they can check up on him to see if all is good. MERRY CHRISTMAS mate.
