Will event-cameras dominate computer vision?

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Dr. Ryad Benosman, a professor at University of Pittsburgh believes a huge shift is coming to how we capture and process images in computer vision applications. He predicts that event-based (or, more broadly, neuromorphic) vision sensors are going to dominate in the future.

Dr. Benosman will be a keynote speaker at this year's Embedded Vision Summit

EETimes published an interview with him; some excerpts below.


According to Benosman, until the image sensing paradigm is no longer useful, it holds back innovation in alternative technologies. The effect has been prolonged by the development of high–performance processors such as GPUs which delay the need to look for alternative solutions.

“Why are we using images for computer vision? That’s the million–dollar question to start with,” he said. “We have no reasons to use images, it’s just because there’s the momentum from history. Before even having cameras, images had momentum.”

Benosman argues, image camera–based techniques for computer vision are hugely inefficient. His analogy is the defense system of a medieval castle: guards positioned around the ramparts look in every direction for approaching enemies. A drummer plays a steady beat, and on each drumbeat, every guard shouts out what they see. Among all the shouting, how easy is it to hear the one guard who spots an enemy at the edge of a distant forest?

“People are burning so much energy, it’s occupying the entire computation power of the castle to defend itself,” Benosman said. If an interesting event is spotted, represented by the enemy in this analogy, “you’d have to go around and collect useless information, with people screaming all over the place, so the bandwidth is huge… and now imagine you have a complicated castle. All those people have to be heard.”

“Pixels can decide on their own what information they should send, instead of acquiring systematic information they can look for meaningful information — features,” he said. “That’s what makes the difference.”

This event–based approach can save a huge amount of power, and reduce latency, compared to systematic acquisition at a fixed frequency.

“You want something more adaptive, and that’s what that relative change [in event–based vision] gives you, an adaptive acquisition frequency,” he said. “When you look at the amplitude change, if something moves really fast, we get lots of samples. If something doesn’t change, you’ll get almost zero, so you’re adapting your frequency of acquisition based on the dynamics of the scene. That’s what it brings to the table. That’s why it’s a good design.”


He goes on to admit some of the key challenges that need to be addressed before neuromorphic vision becomes the dominant paradigm. He believes these challenges are surmountable.

“The problem is, once you increase the number of pixels, you get a deluge of data, because you’re still going super fast,” he said. “You can probably still process it in real time, but you’re getting too much relative change from too many pixels. That’s killing everybody right now, because they see the potential, but they don’t have the right processor to put behind it.” 

“[Today’s DVS] sensors are extremely fast, super low bandwidth, and have a high dynamic range so you can see indoors and outdoors,” Benosman said. “It’s the future. Will it take off? Absolutely!”

“Whoever can put the processor out there and offer the full stack will win, because it’ll be unbeatable,” he added. 

Read the full article here: https://www.eetimes.com/a-shift-in-computer-vision-is-coming/

 

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Telluride Neuromorphic Workshop 2022

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The 2022 edition of the Telluride Neuromorphic Workshop series will be held in-person June 26 to July 16 in beautiful Telluride, Colorado. The topics of interest are broadly in "neuromorphic engineering" with neuromorphic vision sensors (including event cameras and other "spiking"-based vision sensors) being key areas of interest.

Neuromorphic engineers design and fabricate artificial neural systems whose organizing principles are based on those of biological nervous systems. Over the past 27 years, the neuromorphic engineering research community focused on the understanding of low-level sensory processing and systems infrastructure; efforts are now expanding to apply this knowledge and infrastructure to addressing higher-level problems in perception, cognition, and learning. In this 3-week intensive workshop and through the Institute for Neuromorphic Engineering (INE), the mission is to promote interaction between senior and junior researchers; to educate new members of the community; to introduce new enabling fields and applications to the community; to promote ongoing collaborative activities emerging from the Workshop, and to promote a self-sustaining research field.

The workshop will be organized in four topic areas

  • Neuromorphic Tactile Exploration (Enhance the tactile exploration capabilities of robots)
  • Lifelong Learning at Scale: From Neuroscience Theory to Robotic Applications (Apply neuro-inspired principles of lifelong learning to autonomous systems.)
  • Cross-modality brain signals: auditory, visual and motor 
  • Neuromorphics Tools, Techniques and Hardware (SpiNNaker 2 and FPAAs)

Researchers from academia, industry and national labs are all encouraged to apply... 

... in particular if they are prepared to work on specific projects, talk about their own work or bring demonstrations to Telluride (e.g. robots, chips, software). 

An application is required to attend, and financial support is available. Application deadline is April 8, 2022.

Call for applications.

Application submission page.

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