Fresnel lens defect classification using deep learning technique

Fresnel lens defect classification using deep learning technique

Show other versions (1)

Plastic injection molded Fresnel lens is one of the important components for illumination in smart devices. To perform inspection on this type of optical component is challenging for machine vision due to the presence of groove pattern and texture. This paper discusses the limitation of classical im...

Full description

Bibliographic Details
Main Authors:	Loo, Kean Li, Saw, Chong Keat, Ibrahim, M. H.
Format:	Book Section
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	TK Electrical engineering. Electronics Nuclear engineering

Similar Items

Fresnel lens defect classification using deep learning technique
by: Loo, Kean Li, et al.
Published: (2022)

A fast and flexible turret based Automated Vision Inspection (AVI) to inspect 6 sides of small discrete components
by: Loo, Kean Li, et al.
Published: (2022)

Microprocessor Solder Bump Bridging Defects Screening Strategy In Manufacturing Test Flow
by: Loo , Kean Ann
Published: (2017)

Classification of defects on bare PCB using neural network technique
by: Heriansyah, Rudi
Published: (2004)

A Review on System Development in Eddy Current Testing and Technique for Defect Classification and Characterization
by: Kharudin, Ali, et al.
Published: (2017)

Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
by: Zuwairie, Ibrahim, et al.
Published: (2014)

An image processing approach towards classification of defects on printed circuit board
by: Ibrahim, Zuwairie, et al.
Published: (2007)

An algorithm for classification of five types of defects on bare printed circuit board
by: Ibrahim, Ismail, et al.
Published: (2011)

Convolutional neural network for imbalanced data classification of silicon wafer defects
by: Batool, Uzma, et al.
Published: (2020)

A printed circuit board inspection system with defect classification capability
by: Ibrahim, Ismail, et al.
Published: (2012)

A printed circuit board inspection system with defect classification capability
by: Ibrahim, Zuwairie, et al.
Published: (2011)

Neural network paradigm for classification of defects on PCB
by: Heriansyah, Rudi, et al.
Published: (2003)

Die defect classification using image processing
by: Maniam, Darmadevaindra
Published: (2015)

Object classification using deep learning
by: Fong, Soon Fei
Published: (2015)

Comparison of palm oil Fresh Fruit Bunches (FFB) ripeness classification technique using deep learning method
by: Khamis, Nurulaqilla, et al.
Published: (2022)

Morphological image segmentation for detection and classification of PCB defects
by: Indera Putera, Siti Hazurah
Published: (2007)

Online network traffic classification with incremental learning
by: Loo, H. R., et al.
Published: (2016)

Omni directional loudspeaker using giant magneto striction technology
by: Loh, Justin Kean Loo
Published: (2011)

Flat lens design using phase correction technique for horn antenna applications
by: Nur Hazimah Syazana, Abdul Razak, et al.
Published: (2021)

Ventricular arrhythmias classification based on deep learning algorithm
by: Chai, Adrian Kah Seng
Published: (2020)

Plant disease severity classification using deep learning
by: Lee, San Kong
Published: (2020)

Defect detection in thermal image using thresholding technique
by: Heriansyah, Rudi, et al.
Published: (2007)

Classification of phishing websites using machine learning techniques
by: Zamani, Hadi
Published: (2013)

Deep learning-based classification model for botnet attack detection
by: Ahmed, Abdulghani Ali, et al.
Published: (2020)

Defect And Components Recognition In Printed Circuit Boards Using Convolution Neural Network
by: Cheong, Leong Kean
Published: (2018)

Die-level defects classification using region-based convolutional neural network
by: You, Kwong Ming, et al.
Published: (2022)

NBTI defects characterization using energy profiling simulation technique
by: Hussin, Hanim, et al.
Published: (2023)

Classification of lung diseases from X-ray images using deep learning
by: Tan, Zheng Yu
Published: (2022)

Improving neural networks training using experiment design approach
by: Chong, Wei Kean
Published: (2005)

Survey on multi field packet classification techniques
by: Al-Nejadi, Ammar Yahya Daeef, et al.
Published: (2015)

Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
by: Ho, Aik Hong
Published: (2015)

Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
by: Ho, Aik Hong, et al.
Published: (2016)

Online traffic classification for malicious flows using efficient machine learning techniques
by: Chan, Y. Y., et al.
Published: (2021)

Utilizing different types of deep learning models for classification of series arc in photovoltaics systems
by: Omran, Alaa Hamza, et al.
Published: (2021)

Surgical dexterity investigation and classification using deep learning on a virtual reality simulator
by: Cham, Ying Kit
Published: (2021)

Neuro fuzzy classification and detection technique for bioinformatics problems
by: Othman, Mohd. Fauzi, et al.
Published: (2007)

Singleton spam review detection using classification techniques
by: Heydari, Atefeh
Published: (2014)

The classification of EEG signal processing using different machine learning techniques for BCI application
by: Rashid, Mamunur, et al.
Published: (2019)

Malicious traffic classification using hybrid heuristic-payload based technique with machine learning
by: Bahar, Nurul Aze Alia, et al.
Published: (2023)

Human behaviors classification using deep learning technique
by: Shun, Cheang Chi, et al.
Published: (2022)