Martin Oswald
Global Head of Application Engineering | Machine Vision Leadership | MBA
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Martin Oswald is Global Head of Application Engineering with more than 20 years of experience in industrial machine vision, embedded vision systems, image sensors, optics, illumination, synchronization, high-speed networking and AI-enabled vision architectures.
Throughout his career, he has supported hundreds of deployments across factory automation, robotics, logistics, transportation, smart infrastructure and embedded AI applications. His work focuses on bridging the gap between imaging physics and intelligent decision-making systems.
He is passionate about translating complex imaging concepts into practical engineering decisions that improve real-world system performance.
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The Hidden Physics of Physical AI: Why Most Vision Problems Are Not AI Problems
For 2 Feb - 4 Feb 2027 / Moscone West, San Francisco:
Artificial Intelligence is transforming computer vision, yet many deployed systems still fail before the first inference is executed.
Across hundreds of machine vision projects, the same pattern repeatedly appears: teams focus on algorithms while the real issues originate in image acquisition. Illumination, optics, sensor physics, synchronization, dynamic range, thermal effects and image transport often have a greater impact on system performance than model selection.
For 2 Feb - 4 Feb 2027:
This presentation explores the hidden physics behind successful Physical AI systems. Through practical examples from industrial automation, robotics, logistics and transportation, attendees will learn why many vision failures are actually lighting problems, timing problems, sensor limitations or bandwidth bottlenecks.
Martin Oswald
Global Head of Application Engineering | Machine Vision Leadership | MBA
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