D²iCE at the Electronic Imaging conference 2025

2. Daniel Jakab

Daniel Jakab had the fantastic opportunity of presenting a paper entitled, “SOLAS: Superpositioning an Optical Lens in Automotive Simulation”. This work demonstrates the use of ray-tracing as a method for optical simulations in the context of automotive simulation utilizing the Python-based ray-tracing library, KrakenOS a relatively new tool developed by Joel Herrera Vázquez and his research team at the Institute of Astronomy, National Autonomous University of Mexico (UNAM). Please see the following link to the published paper here. Special thanks to Julian Barthel and Alexander Braun from Düsseldorf University of Applied Sciences for making the optical simulations from KrakenOS visually possible by applying them directly on automotive simulations. Special shout out to Daniel Jakab’s supportive supervisors, fellow researchers and colleagues from D²iCE Research Centre, University of Galway and Valeo Vision Systems: Ciarán Eising, Reenu Mohandas, Dara Molloy, Mahendar Kumbham, PhD, Brian Deegan, Fiachra Collins and Tony Scanlan who have all helped to make this publication possible.

Automotive Simulation is a potentially cost-effective strategy to identify and test corner case scenarios in automotive perception. Recent work has shown a significant shift in creating realistic synthetic data for road traffic scenarios using a video graphics engine. However, a gap exists in modeling realistic optical aberrations associated with cameras in automotive simulation. This paper builds on the concept from existing literature to model optical degradations in simulated environments using the Python-based ray-tracing library KrakenOS. As a novel pipeline, we degrade automotive fisheye simulation using an optical doublet with +/-2◦ Field of View(FOV), introducing realistic optical artifacts into two simulation images from SynWoodscape and Parallel Domain Woodscape. We evaluate KrakenOS by calculating the Root Mean Square Error (RMSE), which averaged around 0.023 across the RGB light spectrum compared to Ansys Zemax OpticStudio, an industrial benchmark for optical design and simulation. Lastly, we measure the image sharpness of the degraded simulation using the ISO12233:2023 Slanted Edge Method and show how both qualitative and measured results indicate the extent of the spatial variation in image sharpness from the periphery to the center of the degradations.

3.  Short Courses at Electronic Imaging 2025

During this conference, on the 5th of February, Daniel Jakab had an incredible opportunity to attend two Short Courses offered by Electronic Imaging. The first course was called “Image Quality Foundations: Standards for Mobile, Automotive, and Machine Vision Applications” presented by Dr. Peter D. Burns, Managing Director of Burns Digital Imaging (formed in 2011), and Don Williams, Consultant of Image Science Associates. This was the second time Daniel Jakab was able to attend this session following on from last year’s Electronic Imaging 2024 and as a returning scholar, Daniel Jakab highly recommends this course where each year it is different and just as good (if not better!) than the last. This course is extremely beneficial for both researchers and industrial experts alike interested in the field of Image Quality. The course material is designed to be inclusive where discussion is encouraged, providing an excellent learning environment for all. Please do see a previous blog post here by Daniel Jakab who recounts a day-to-day diary at Electronic Imaging.

The second course was called “Information Metrics for Optimizing Machine Vision Systems” presented by Norman Koren (the Founder of Imatest LLC, a leader in image quality testing since 2004). This short course is designed to be a perfect follow-on course to the previous where Information Metrics linking Image Quality and Computer Vision performance is the end goal. A question that is perhaps on everyone’s mind, is how do we design our cameras and imaging systems such that we know how our Computer Vision and Artificial Intelligence algorithms behave? It is a compelling thought, and the truth is we don’t know until we do the measurements. But even before this, we must first devise the metrics to take credible measurements. And this is exactly what this course delivers. Not only are the mathematical foundations of new metrics presented but, a live demonstration is provided to all using new Imatest software called Simatest, an Image Signal Processing (ISP), and camera simulator. With a course as advanced and forward-thinking as this, you would not want to miss this next year.

Overall, if Electronic Imaging were to be described in a particular way, the phrase ‘once-in-a-lifetime experience’ would come to mind. Any imaging or data scientist would easily find their place here no matter what their background is whether it is medical imaging, automotive, astronomy, mobile, or any other visual domain for that matter. At this stage, if Electronic Imaging isn’t already on your calendar for next year, then…make sure it is!

6.  Aryan Singh

Title: Image Segmentation: Inducing graph-based learning 

EI conference was a great experience, I had the opportunity to present my work and interact with some very interesting people at the venue, explored the city, and it was very much an enjoyable experience. Our presented study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN based U-Net architecture on three distinct datasets: PascalVOC, a standard benchmark for natural image segmentation, WoodScape, a challenging dataset of fisheye images commonly used in autonomous driving, introducing significant geometric distortions; and ISIC2016, a dataset of dermoscopic images for skin lesion segmentation. We compare our proposed UNet-GNN model against established convolutional neural networks (CNNs) based segmentation models, including U-Net and U-Net++, as well as the transformer-based SwinUNet. Unlike these methods, which primarily rely on local convolutional operations or global selfattention, GNNs explicitly model relationships between image regions by constructing and operating on a graph representation of the image features. This approach allows the model to capture long-range dependencies and complex spatial relationships, which we hypothesize will be particularly beneficial for handling geometric distortions present in fisheye imagery and capturing intricate boundaries in medical images. Our analysis demonstrates the versatility of GNNs in addressing diverse segmentation challenges and highlights their potential to improve segmentation accuracy in various applications, including autonomous driving and medical image analysis.