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Deep Learning / Software
Why AI Innovation Should Start with the Algorithm
Why AI Innovation Should Start with the Algorithm
Dr. Michael Fanous advocates for shifting from hardware-centric to algorithm-driven development in AI and machine vision, fostering creativity and broader solution spaces.
Sept. 9, 2026
White-and-red SCANIMUS research-use-only scanning-microscopy device positioned on a sunlit coastal rock, with a sandy beach, ocean, cliffs, and blue sky behind it.
Designing the Blur Budget: Continuous-motion as an Imaging System Lever
Sept. 2, 2026
MVTec
In this example, a GMM-based model is used for simple fruit classification. The selected features are object circularity and area, though additional features such as color could be added to improve robustness. The key point is that the classifier can be trained and tuned with relatively little manual effort, making it a practical option for straightforward sorting or inspection tasks. (Images and test algorithms copyright MVTec.)
NextGen
Practical Machine Vision: Leveraging Machine Learning Tools
Aug. 19, 2026
Measuring the Impact of AI in Machine Vision
NextGen
Measuring the Impact of AI in Machine Vision
March 27, 2026
Focus On Vision: March 25, 2026
Cameras and Accessories
Focus On Vision: Smart Farming, Gas Leak Detection & Data Storage | March 25, 2026
March 25, 2026
Industry Trends in AI Adoption for Machine Vision
NextGen
Industry Trends in AI Adoption for Machine Vision
March 25, 2026
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automatic inspection on vials
Next-Gen Machine Vision: Anomaly Detection for High-Speed, High-Mix Production Environments
Feb. 25, 2026
SwitchOn
Feb. 25, 2026
Siemens
Siemens has acquired Canopus AI to expand its semiconductor design and manufacturing digital thread
Home
Siemens Acquires Canopus AI to Integrate AI-driven Metrology into Semiconductor Manufacturing
Feb. 4, 2026
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