IMAGE BESTSony Investor Relations Day 2022AMMJENACIONAL

Slides from the Imaging and Sensing Solutions Segment from Sony's Investor Relations day:
https://www.sony.com/en/SonyInfo/IR/library/presen/irday/pdf/2022/ISS_E.pdf

























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IMAGE BESTWill event-cameras dominate computer vision?AMMJENACIONAL

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 com...
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IMAGE BESTVeoneer and BMW agreement on next-gen automotive vision systemsAMMJENACIONAL

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IMAGE BEST"End-to-end" design of computational camerasAMMJENACIONAL

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A team from MIT Media Lab has posted a new arXiv preprint titled "Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging". Abstract : Cameras were originally designed using physics-based heuristics to capture aesthetic images. In recent years, there has been a transformation in camera design from being purely physics-driven to increasingly data-driven and task-specific. In this paper, we present a framework to understand the building blocks of this nascent field of end-to-end design of camera hardware and algorithms. As part of this framework, we show how methods that exploit both physics and data have become prevalent in imaging and computer vision, underscoring a key trend that will continue to dominate the future of task-specific camera design. Finally, we share current barriers to progress in end-to-end design, and hypothesize how these barriers can be overcome. Preprint:  https://arxiv.org/pdf/2204.09871.pdf
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