A large portion of today’s world population suffers from vision impairments and wears prescription eyeglasses. However, prescription glasses cause additional bulk and discomfort when used with virtual reality (VR) headsets, negatively impacting the viewer’s visual experience. In this work, we remedy the usage of prescription eyeglasses with screens by shifting the optical complexity into the software. Our proposal is a prescription-aware rendering approach for providing sharper and more immersive imagery for screens, including VR headsets. To this end, we develop a differentiable display and visual perception model encapsulating the human visual system’s display-specific parameters, color, visual acuity, and user-specific refractive errors. Using this differentiable visual perception model, we optimize the rendered imagery in the display using gradient-descent solvers. This way, we provide prescription glasses-free sharper images for a person with vision impairments. We evaluate our approach and show significant quality and contrast improvements for users with vision impairments.
Since 2020 in the USA 1 and 2021 in Europe, all medical devices have to be marked with a Unique Device Identification (UDI) code to ensure their traceability. UDI codes are laser marked but the engraving process is error-prone due laser-related or external conditions. Defects may be assessed visually but this process is costly and gives rise to human errors. Using machine vision to perform this task for large batches of UDI codes may be challenging due to alterations in readability caused by marking defects or image quality. Therefore, we have tested several learned methods to achieve two goals: correctly recognize characters and identifying marking defects on UDI codes. As the codes were engraved on cylindrical metallic surfaces with a metallic paint effect, we had to address the problem of specular and stray reflections through the development of a tailor-made lighting engine. Our image grabbing and processing pipeline comprises of an imaging device designed to prevent reflections onto engraved codes; an Optical Character Recognition (OCR) algorithm (multilayer perceptron, support vector machine, classical image segmentation), and a probabilistic model to detect faulty characters that need to be further qualified by a human operator. Our results show that multilayer perceptron (MLP) and support vector machine (SVM) recognition performances are very close together and above classical image segmentation.
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