The size and number of images for image acquisition instruments and processing techniques have grown exponentially, along with the associated storage needs and network requirements. In the fields using large amounts of data, such as life-sciences, automotive or aerospace, most of the images are processed with machine learning techniques that rely on information that is invisible to the eye, but that can be revealed by observing fine correlations between pixels. Therefore, it is important that the raw images follow specific standards, though currently there is a lack of standardized definitions and quality measures for these images.
One of our recent joiners, Arianne Bercowsky, will present insights into recent work involving standardization and image quality assessment to help future-proof image data. She will discuss image properties that are often overlooked, explain raw images from technical and physical perspectives, their main features, and how to evaluate image quality to ensure data-centric artificial intelligence (AI) and machine learning. She will also demonstrate Jetraw high-performance raw image compression technology, as a software for biomedical and pharma, followed by our CTO Bruno Sanguinetti, who will demonstrate its field-programmable gate array (FPGA) implementation for camera manufacturers. The goal of Jetraw technology is not only to tackle the issues of big data, such as storage space and associated costs, CO2 emissions, and data transfer rates; but also, to prepare image data for the era of AI processing in a reliable and scalable manner.
Arianne and Bruno will be joined by Gerhard Holst, from one of Dotphoton’s partners, Excelitas PCO.
Arianne Bercowsky, Ph.D., is an application specialist at Dotphoton. She received her doctorate in bioengineering and biotechnology at École Polytechnique Fédérale de Lausanne (EPFL) in the lab of professor Andrew C. Oates. Oates' lab was one of Dotphoton's first clients to integrate Jetraw technology into its image acquisition and processing workflow. The lab's time-lapse data sets were measured in terabytes, which made data handling, storage, and transfer an issue. With Jetraw Oates' lab reduced data transfer time from two hours per dataset to just 15 minutes, reducing costs and the lab's carbon footprint along the way. Arianne saw an immediate need for such technology in biomedical, medical imaging, and machine learning applications, so now she is passionate about helping researchers improve their image data acquisition and processing workflow.
Bruno Sanguinetti, Ph.D. is co-founder of Dotphoton and a CTO. After years of academic research in quantum optics, Bruno has dedicated the past 10 years of his career to bringing cutting-edge quantum cryptography and metrology technologies to the market. His expertise extends to processing photonic information algorithms, sensor characterization, image compression, error correction and cryptographic functions. Bruno is a member of the ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) as part of AI for Good (WHO), working on standardization of AI technology for reliable deployment in real-world applications.
Gerhard Holst, Ph.D. graduated from the Technical University Aachen in Germany in 1991, earning a degree in electrical engineering. He went on to complete his doctorate at the University of Dortmund in collaboration with the Max-Planck-Institute for System Physiology in Dortmund, Germany. Gerhard furthered his research as member of the Microsensor Research Group at the Max-Planck-Institute for Marine Microbiology in Bremen, Germany. From 2001 – 2021 he was head of the research and science department at PCO AG, where he was responsible for new technologies, all research, and sensor projects, such as the development of sCMOS image sensors. Since the acquisition by Excelitas in 2021 he is responsible for ongoing research grants and product manager.
