68‐4: Speedy and Quantitative Evaluation of Luminance Non‐Uniformity Based on Deep Neural Networks | Zendy

Tsutsukawa Kazuki | Zendy; Tabata Nobunari | Zendy; Bamba Yusuke | Zendy

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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.

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