Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection

C Mera, M Orozco-Alzate, J Branch - Computers in Industry, 2019 - Elsevier
Abstract Most Multiple Instance Learning (MIL) algorithms are designed with the assumption
that the target concept is stationary in time, ie it is drawn from a stationary unknown …

16‐4: Invited Paper: Region‐Based Machine Learning for OLED Mura Defects Detection

J Lee - SID Symposium Digest of Technical Papers, 2021 - Wiley Online Library
Automatic inspection of Mura defects is a challenging task in display manufacturing. Due to
the nature of Mura defects, which appear as brightness variances in the low‐contrast images …

ÜRETİM AŞAMASINDA RAY ve PROFİLDE OLUŞAN KUSURLARININ TESPİTİNE YÖNELİK BİR PARALEL KUSUR ALGILAMA ALGORİTMASI

İM Orak, A Çelik - Gazi Üniversitesi Mühendislik Mimarlık Fakültesi …, 2017 - dergipark.org.tr
Otomatik bir sistem tarafından görüntü işlenerek sonuç elde etmek günümüzde pek çok
alanda gerekli olabilmektedir. Kusurlu ürün üretimi birçok alanda karşılaşılan üreticiler …

Proposal of local feature vector focusing on the differences among neighboring ROI's

K Muto, T Matsubara… - … International Workshop on …, 2018 - ieeexplore.ieee.org
It is difficult to cope with the product surface inspections with complicated textures by using
the conventional procedures such as basic banalization, morphology processing, etc. Then …

[PDF][PDF] Alexandre Durupt, alexandre. durupt@ utc, 0616016125

PANR TEMIS - utc.fr
Université de technologie de Compiègne - Proposition de thèse 1re partie : Fiche
scientifique Inspection visuelle automatique Page 1 École doctorale de rattachement : ED …

[HTML][HTML] Повышение эффективности программной реализации алгоритмов распознавания изображений с детальной оценкой состояния массового …

П с Антиплагиатом - Программирование - studres.ru
Повышение эффективности программной реализации алгоритмов распознавания
изображений с детальной оценкой состояния массового количества …