Replacing Goniophotometer with Camera and U-Net with Hypercolumn Rescale Block
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14135 LNCS, Page: 423-434
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
We deal with replacing a costly and slow goniophotometer device with a standard, inexpensive, and fast camera in the task of evaluating an illuminated area by a car headlamp. This solution is novel, has not yet been solved, and has the potential to speed up the process of prototyping headlamps. The difficulties lie in the significantly different resolutions of the two devices and in the disparity between intensities captured by the camera and goniophotometer due to the nonlinear behavior of the light. We propose to capture images by a camera with various exposure times and handle them as a multispectral image. The image is processed by U-Net architecture where we replaced the standard decoder with a Hypercolumn rescale block. The proposed scheme produces a mean absolute percentage difference between the real goniophotometer and our solution of less than 0.5%.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85174508991&origin=inward; http://dx.doi.org/10.1007/978-3-031-43078-7_35; https://link.springer.com/10.1007/978-3-031-43078-7_35; https://dx.doi.org/10.1007/978-3-031-43078-7_35; https://link.springer.com/chapter/10.1007/978-3-031-43078-7_35
Springer Science and Business Media LLC
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