Premium
68‐4: Speedy and Quantitative Evaluation of Luminance Non‐Uniformity Based on Deep Neural Networks
Author(s) -
Tsutsukawa Kazuki,
Tabata Nobunari,
Bamba Yusuke
Publication year - 2019
Publication title -
sid symposium digest of technical papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.351
H-Index - 44
eISSN - 2168-0159
pISSN - 0097-966X
DOI - 10.1002/sdtp.13087
Subject(s) - luminance , artificial intelligence , computer science , similarity (geometry) , artificial neural network , encoder , computer vision , pattern recognition (psychology) , deep neural networks , image (mathematics) , operating system
We developed a method for automated evaluation of display luminance non‐uniformity using an auto‐encoder. Usually, a reconstruction loss of auto‐encoder is used for abnormality detection. In our method, we used reconstruction loss as the main indicator and cosine similarity as a secondary indicator. Our method succeeded in the non‐uniformity evaluation.