This paper will analyse the benefits and drawbacks of traditional image processing and deep learning. It aims to provide clarity on the subject of deep learning, which can then help data scientists and industries choose the most suitable method depending on the task.
Resources
20 April 2020
Czech firm Pekat Vision writes about quality control projects it has solved in pharmaceutical and food inspection, both key markets that need to be kept running during the Covid-19 pandemic
15 April 2020
Imaging and Machine Vision Europe hosted a live panel discussion featuring the heads of VDMA, EMVA, AIA and UKIVA to discuss vision's response to the Covid 19 crisis
17 March 2020
Philipp Huber, Hans-Joachim Gelke and Matthias Rosenthal, from the Institute of Embedded Systems at Zurich University of Applied Sciences, describe a method for direct data transfer between a GPU and FPGA for real-time imaging
13 March 2020
When optical designers attempt to compare the performance of optical systems, a commonly used measure is modulation transfer function. In order to understand the significance of MTF, consider some general principles and practical examples for defining MTF including its components, importance, and characterisation
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Outfield analyses aerial images of orchards using machine learning to predict crop yield
Farmers are starting to reap the rewards of robotics and machine vision, as Keely Portway finds out
Open source software has advanced to a point where it’s now a credible option for industrial imaging, Matthew Dale finds
Active Silicon’s 4xCXP-12 FireBird Coaxpress frame grabber (PCIe Gen3 x8)
Chris Beynon, Active Silicon’s CTO and technical chair of the Coaxpress committee, updates on the Coaxpress standard
Neil Trevett and Chris Yates
The Khronos Group and the EMVA are to explore software standards for embedded vision. Khronos’ Neil Trevett and EMVA’s Chris Yates explain the work
Greg Blackman reports from the Embedded World show, where industry experts gave insights into vision processing at the edge
The first colour image, captured by a CMV20000 sensor, to be sent back by the hazard cameras on the Perseverance rover after its landing on Mars on 18 February. Credit: NASA/JPL-Caltech
Greg Blackman speaks to Guy Meynants, formerly of Cmosis, and Paul Jerram, of Teledyne e2v, about the history of the image sensors onboard the Mars rover
