RFCNet: A Novel Approach for Wafer Map Defect Classification Based on Residual Learning Using Autoencoder-Based Data Balancing

Authors: Uddin, Md. Ryhan, Islam, Md. Saiful, Paul Tumpa, Priyanti

Source: 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) Electrical, Computer and Communication Engineering (ECCE), 2025 International Conference on. :1-6 Feb, 2025

Relation: 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE)

Wafer map defect classification using deep learning framework with data augmentation on imbalance datasets

Authors: Tsai, Tsung-Han^{Aff1, IDs13640025006663_cor1}, Wang, Chieng-Yang

Source: EURASIP Journal on Image and Video Processing. 2025(1)

Comprehensive Evaluation of Deep Neural Network Architectures for Parawood Pith Estimation

Authors: Wattanapong Kurdthongmee

Source: HighTech and Innovation Journal, Vol 4, Iss 3, Pp 543-559 (2023)

Subject Terms: Artificial intelligence, Relative standard deviation, Tree Height Estimation, Chemical Constituents and Production of Agarwood, 7. Clean energy, Industrial and Manufacturing Engineering, Systems engineering, Detection limit, Engineering, Business, Marketing, 2. Zero hunger, Pith, Geography, HD45-45.2, Statistics, Forestry, Fabric Defect Detection, Regression, Chemistry, Benchmarking, Physical Sciences, 8. Economic growth, regression, Mapping Forests with Lidar Remote Sensing, Surface Defect Detection, Regression analysis, Douglas fir, Technological innovations. Automation, Artificial neural network, Environmental Engineering, Wood Identification, Convolutional neural network, Fabric Defect Detection in Industrial Applications, FOS: Economics and business, FOS: Chemical sciences, Machine learning, FOS: Mathematics, Standard deviation, wood pith detection, Biology, parawood, resnet, Organic Chemistry, FOS: Environmental engineering, Botany, deep learning, image augmentation, Deep learning, 15. Life on land, Computer science, xception, accuracy, Environmental Science, Wafer Map Defect Classification, mobilenet, Estimation, Mathematics

Access URL: https://doaj.org/article/b8279e5936ee4251b9088dff4a4fa6f2

Foreign Object Debris (FOD) Classification Through Material Recognition Using Deep Convolutional Neural Network With Focus on Metal

Authors: Syeda Mahrukh Zainab, Khurram Khan, Adnan Fazil, Muhammad Zakwan

Source: IEEE Access, Vol 11, Pp 10925-10934 (2023)

Subject Terms: Artificial intelligence, Convolutional neural network, 02 engineering and technology, Pattern recognition (psychology), Oceanography, Fabric Defect Detection in Industrial Applications, 7. Clean energy, Industrial and Manufacturing Engineering, 12. Responsible consumption, Engineering, FOD, Object Detection, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Physics, Deep learning, Geology, Optics, FOS: Earth and related environmental sciences, Fabric Defect Detection, Focus (optics), Robust Line and Curve Detection using Hough Transform, Computer science, TK1-9971, Edge Detection, Deep convolutional neural network (DCNN), Physical Sciences, Computer Science, Wafer Map Defect Classification, Deep Learning in Computer Vision and Image Recognition, Surface Defect Detection, Object (grammar), Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition, Debris, material classification

Access URL: https://doaj.org/article/084a0889b6e64336a3a9e642c239b184

Quantification of Damages and Classification of Flaws in Mono-Crystalline Photovoltaic Cells Through the Application of Vision Transformers

Authors: João Pedro Costa Barnabé, Lenin Patricio Jiménez Jiménez, Gustavo Fraidenraich, Eduardo Rodrigues de Lima, Henrique Santos

Source: IEEE Access, Vol 11, Pp 112334-112347 (2023)

Subject Terms: Artificial intelligence, Photovoltaic Arrays, FOS: Political science, photovoltaic cells, FOS: Law, Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, 7. Clean energy, Industrial and Manufacturing Engineering, Engineering, Image processing, visual transformers, Machine learning, Image (mathematics), Political science, Photovoltaic system, Transformer, Energy, Renewable Energy, Sustainability and the Environment, Voltage, Photovoltaic Maximum Power Point Tracking Techniques, Virtual Metrology, Fabric Defect Detection, Computer science, Contextual image classification, TK1-9971, machine learning, Electrical engineering, Physical Sciences, Computer Science, Wafer Map Defect Classification, Damages, 8. Economic growth, Deep Learning in Computer Vision and Image Recognition, Surface Defect Detection, Computer vision, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition, Classifier (UML), Law

Access URL: https://doaj.org/article/9d9fedbe88a54c12829bc0cac3e1f46e

A statistical prediction model for pilling grades of blended worsted fabrics based on fabric bending rigidity

Authors: Eman Mustafa, Abdel Salam Malek, Sherwet El Gholmy, Adel El Geiheini, Sherien El kateb

Source: Alexandria Engineering Journal, Vol 61, Iss 12, Pp 11615-11621 (2022)

Subject Terms: Composite material, Polymers and Plastics, Bending, Materials Science, Polyester, Structural engineering, Flexural rigidity, 02 engineering and technology, Fabric Defect Detection in Industrial Applications, 01 natural sciences, Industrial and Manufacturing Engineering, Viscose, 12. Responsible consumption, Engineering, Pilling, Yarn, FOS: Mathematics, Bending rigidity, Wool, Textile Dyeing Techniques and Materials, Statistics, Worsted, Building and Construction, Engineering (General). Civil engineering (General), Fabric Defect Detection, Materials science, 0104 chemical sciences, Science of Clothing Comfort and Textile Properties, Rigidity (electromagnetism), Statistical analysis, Worsted fabrics, Physical Sciences, Wafer Map Defect Classification, 8. Economic growth, Regression Analysis, Textile Engineering, Texture Analysis, TA1-2040, Prediction, 0210 nano-technology, Mathematics, Fabric Testing

Access URL: https://doaj.org/article/9d1a0a9364784b119ec17496a519bb45

Detecting surface defects of heritage buildings based on deep learning

Authors: Xiaoli Fu, Niwat Angkawisittpan

Source: Journal of Intelligent Systems, Vol 33, Iss 1, Pp 163-9 (2024)

Subject Terms: Artificial intelligence, Automated Pavement Inspection and Maintenance, Science, 0211 other engineering and technologies, Geometry, 02 engineering and technology, Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Defect Detection, Engineering, transformer-based segmentation, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, historical places, Civil and Structural Engineering, 2. Zero hunger, Crack Detection, Architectural engineering, deep learning, Geology, Deep learning, QA75.5-76.95, FOS: Earth and related environmental sciences, 15. Life on land, sustainability, 3D Geospatial Modelling Techniques, Fabric Defect Detection, 16. Peace & justice, Computer science, thermography, images, Earth and Planetary Sciences, Informatik, Sonstiges, Electronic computers. Computer science, Physical Sciences, Wafer Map Defect Classification, Surface Defect Detection, Surface (topology), flipping, Mathematics

File Description: image/jpeg; application/pdf

Access URL: https://doaj.org/article/0268a861afb545a0b2dc790a6ef80ecf

A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition

Authors: Uzma Batool, Mohd Ibrahim Shapiai, Muḥammad Ṭâhir, Zool Hilmi Ismail, Noor Jannah Zakaria, Ahmed Elfakharany

Source: IEEE Access, Vol 9, Pp 116572-116593 (2021)

Subject Terms: Artificial intelligence, 0209 industrial biotechnology, Failure Analysis of Integrated Circuits, Convolutional neural network, 02 engineering and technology, Pattern recognition (psychology), TP Chemical technology, Fabric Defect Detection in Industrial Applications, Unsupervised learning, Industrial and Manufacturing Engineering, Electron Beam Lithography: Resolution and Applications, defect recognition, Engineering, Deep Learning, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, wafer bin map, Electrical and Electronic Engineering, systematic literature review, deep learning, Deep learning, Fabric Defect Detection, Computer science, TK1-9971, Wafer map defects, Physical Sciences, Wafer Map Defect Classification, Feature extraction, Surface Defect Detection, Electrical engineering. Electronics. Nuclear engineering

File Description: application/pdf

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https://doaj.org/article/5090733250b74e6d886ac94a30349683
https://ieeexplore.ieee.org/document/9517097
https://dblp.uni-trier.de/db/journals/access/access9.html#BatoolSTIZE21
https://doi.org/10.1109/ACCESS.2021.3106171

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

Authors: Hanen Karamti, Maha M. A. Lashin, Fadwa Alrowais, Abeer M. Mahmoud

Source: IEEE Access, Vol 9, Pp 58838-58851 (2021)

Subject Terms: Artificial intelligence, imbalanced samples, Machine Fault Diagnosis and Prognostics, rotating machinery, Automated Currency Recognition and Authentication, 02 engineering and technology, Redundancy (engineering), Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Engineering, sparse autoencoders, Automated Inspection, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, variational autoencoder, Data mining, Fault diagnosis, logistic regression, Deep learning, Autoencoder, Fault Diagnosis, Fabric Defect Detection, Computer science, TK1-9971, Operating system, Control and Systems Engineering, Physical Sciences, Computer Science, Wafer Map Defect Classification, Surface Defect Detection, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition, Classifier (UML)

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09399084.pdf
https://doaj.org/article/c3635dce090a46ff85f0d90aa7823bf1

Assessment of the Factors Affecting Apparel Pattern Grading Accuracy: Problems Identification and Recommendations

Authors: Md. Mazharul Islam, Mohammad Abdul Jalil, Md. Shohan Parvez, Md. Mahmudul Haque

Source: Tekstilec, Vol 63, Iss 3, Pp 166-184 (2020)

Subject Terms: Polymers and Plastics, Engineering drawing, Materials Science, Computational Mechanics, Fabric Defect Detection in Industrial Applications, 01 natural sciences, Industrial and Manufacturing Engineering, Clothing, Engineering, grading system, TP890-933, 0502 economics and business, Analysis of Three-Dimensional Shape Structures, Civil engineering, pattern making, Geography, 05 social sciences, grading, Grading (engineering), Textile bleaching, dyeing, printing, etc, Fabric Defect Detection, Computer science, 0104 chemical sciences, Science of Clothing Comfort and Textile Properties, grading problems, Archaeology, Physical Sciences, Wafer Map Defect Classification, cad, Surface Defect Detection, Texture Analysis, FOS: Civil engineering, Fabric Testing

Access URL: https://doaj.org/article/d945e922c46643c19564d6d21b1867fd

Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation

Authors: Praveen Kumar Moganam, Denis Ashok Sathia Seelan

Source: Journal of Leather Science and Engineering, Vol 4, Iss 1, Pp 1-21 (2022)

Subject Terms: Artificial neural network, Artificial intelligence, 0209 industrial biotechnology, Class (philosophy), Computational Mechanics, FOS: Mechanical engineering, Convolutional neural network, Class activation map, TP1-1185, 02 engineering and technology, Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Machine vision, Industrial and Manufacturing Engineering, 12. Responsible consumption, Engineering, Deep Learning, Segmentation, Machine learning, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Machine learning classifier, Chemical technology, Characterization of Surface Roughness in Optical Components, Mechanical Engineering, Multi class classification, Convolution neural networks, Texture (cosmology), Deep learning, 15. Life on land, Fabric Defect Detection, Leather defects, Computer science, Process (computing), Operating system, Physical Sciences, Welding Techniques and Residual Stresses, Wafer Map Defect Classification, Texture Analysis, Surface Defect Detection, Computer vision

Access URL: https://doaj.org/article/3dd4858bc7a348a58ac8328896cc4e10

Classification of defects in wooden structures using pre-trained models of convolutional neural network

Authors: Rana Ehtisham, Waqas Qayyum, Charles V. Camp, Vagelis Plevris, Junaid Mir, Qaiser-uz Zaman Khan, Afaq Ahmad

Source: Case Studies in Construction Materials, Vol 19, Iss, Pp e02530-(2023)

Subject Terms: Artificial neural network, Artificial intelligence, Automated Pavement Inspection and Maintenance, Convolutional neural network, Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Defect Detection, 12. Responsible consumption, Identification (biology), Engineering, Image (mathematics), Materials of engineering and construction. Mechanics of materials, Biology, Civil and Structural Engineering, Crack Detection, 4. Education, Botany, Geology, Deep learning, FOS: Earth and related environmental sciences, 15. Life on land, 3D Geospatial Modelling Techniques, Fabric Defect Detection, Computer science, Contextual image classification, Earth and Planetary Sciences, Pre-trained Models, Categorization, Physical Sciences, Wafer Map Defect Classification, TA401-492, Surface Defect Detection, Wooden defects, Defects Classification, CNN

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https://hdl.handle.net/10576/59642

Weld Inspection Based on Radiography Image Segmentation with Level Set Active Contour Guided Off-Center Saliency Map

Authors: Mohamed Ben Gharsallah, Ezzeddine Ben Braiek

Source: Advances in Materials Science and Engineering, Vol 2015 (2015)

Subject Terms: Artificial intelligence, Feature (linguistics), Robustness (evolution), FOS: Mechanical engineering, 02 engineering and technology, Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Biochemistry, Gene, Quantum mechanics, 01 natural sciences, Industrial and Manufacturing Engineering, Defect Detection, Engineering, Segmentation, 0103 physical sciences, Salient, 0202 electrical engineering, electronic engineering, information engineering, Level set method, Welding, Materials of engineering and construction. Mechanics of materials, Non-Destructive Techniques Based on Eddy Current Testing, Laser Welding, Image segmentation, Mechanical Engineering, Physics, Level set (data structures), Linguistics, Fabric Defect Detection, Computer science, Materials science, FOS: Philosophy, ethics and religion, 3. Good health, Radiography, Energy minimization, Chemistry, Philosophy, Welding Techniques and Residual Stresses, Physical Sciences, Wafer Map Defect Classification, Metallurgy, TA401-492, FOS: Languages and literature, Medicine, Surface Defect Detection, Computer vision, Pixel, Radiology, Active contour model

File Description: text/xhtml

Access URL: http://downloads.hindawi.com/journals/amse/2015/871602.pdf
https://doaj.org/article/be4adc281b5f4685b80276826a207408
https://www.hindawi.com/journals/amse/2015/871602/
https://core.ac.uk/display/89338525
https://downloads.hindawi.com/journals/amse/2015/871602.pdf
http://downloads.hindawi.com/journals/amse/2015/871602.pdf

ANALYSIS AND PREDICTION OF MULTIPLE-CRACKED PLASTIC BEAM

Authors: Hang Xuan Le, Luong T. H. Nguyen

Source: Science and Technology Development Journal. 12:37-45

Subject Terms: Finite element method, Flexibility (engineering), FOS: Mechanical engineering, Structural engineering, Fitness function, Structural Damage Detection, Cantilever, Structural Health Monitoring Techniques, Fabric Defect Detection in Industrial Applications, Vibration, Industrial and Manufacturing Engineering, Stiffness, Identification (biology), Engineering, Stiffness matrix, Machine learning, FOS: Mathematics, Biology, Civil and Structural Engineering, Vibration-based Damage Identification, Mechanical Engineering, Physics, Statistics, Botany, Acoustics, Fabric Defect Detection, Computer science, Materials science, Genetic algorithm, Flexibility method, Physical Sciences, Wafer Map Defect Classification, Beam (structure), Natural frequency, Surface Defect Detection, Dimensional Metrology and Error Compensation, Mathematics

Access URL: http://stdj.scienceandtechnology.com.vn/index.php/stdj/article/download/2381/2734

A sight on defect detection methods for imbalanced industrial data

Authors: Meryem Chaabi, Mohamed Hamlich

Source: ITM Web of Conferences, Vol 43, p 01012 (2022)

Subject Terms: Artificial intelligence, Outlier Detection, Class (philosophy), defect detection, 0211 other engineering and technologies, Handling Imbalanced Data in Classification Problems, Geometry, Information technology, 02 engineering and technology, Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Systems engineering, Anomaly Detection in High-Dimensional Data, Task (project management), Engineering, Artificial Intelligence, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Product (mathematics), Imbalanced Data, 9. Industry and infrastructure, 4. Education, T58.5-58.64, Fabric Defect Detection, Computer science, one-class classification, Computer Science, Physical Sciences, Wafer Map Defect Classification, imbalanced data, Surface Defect Detection, Mathematics, data augmentation, Training set

Access URL: https://doaj.org/article/a95aaf87fbe2405e9ab68c30e99c1db8

Using fault detection and classification techniques for machine breakdown reduction of the HGA process caused by the slider loss defect

Authors: Thanasak Wanglomklang, Phathan Chommaungpuck, Kontorn Chamniprasart, Jiraphon Srisertpol

Source: Manufacturing Review, Vol 9, p 21 (2022)

Subject Terms: Artificial intelligence, 0209 industrial biotechnology, Manufactures, FOS: Mechanical engineering, 02 engineering and technology, Pattern recognition (psychology), Industrial and Manufacturing Engineering, Reliability engineering, Anomaly Detection in High-Dimensional Data, Engineering, 0302 clinical medicine, Actuator, Slider, data analytics, Seismology, Physics, 4. Education, Geology, Power (physics), Engineering (General). Civil engineering (General), Fabric Defect Detection, Mechanical engineering, Downtime, machine learning, Reliability (semiconductor), Physical Sciences, 8. Economic growth, Anomaly Detection, Surface Defect Detection, Reduction (mathematics), Fault detection and isolation, TA1-2040, Artificial neural network, Outlier Detection, Geometry, Fabric Defect Detection in Industrial Applications, Quantum mechanics, TS1-2301, Fault (geology), 03 medical and health sciences, Artificial Intelligence, FOS: Mathematics, T1-995, Technology (General), Advanced Monitoring of Machining Operations, Mechanical Engineering, Fault Simulator, FOS: Earth and related environmental sciences, Computer science, machine automation, Process (computing), Stuck-at fault, Operating system, Computer Science, Wafer Map Defect Classification, fault detection and classification, artificial neural network, Mathematics

Access URL: https://doaj.org/article/13a5a91bd80048e89784cd71c2c2a02c

Classification of reduction invariants with improved backpropagation

Authors: Siti Mariyam Shamsuddin, Maslina Darus, Md. Nasir Sulaiman

Source: International Journal of Mathematics and Mathematical Sciences, Vol 30, Iss 4, Pp 239-247 (2002)

Subject Terms: Artificial neural network, Artificial intelligence, Principal component analysis, Backpropagation, 02 engineering and technology, Handwriting Recognition and Text Detection, Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Engineering, Shape Matching and Object Recognition, QA1-939, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Feature Descriptors, Data mining, Pattern recognition, speech recognition, Pure mathematics, Document Image Analysis, Fabric Defect Detection, Dimensionality reduction, Computer science, Dimension (graph theory), Computer Science, Physical Sciences, Wafer Map Defect Classification, data reduction, Feature extraction, Surface Defect Detection, Computer Vision and Pattern Recognition, Mathematics

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https://www.emis.de/journals/HOA/IJMMS/Volume30_4/390584.pdf
https://www.maths.tcd.ie/EMIS/journals/HOA/IJMMS/30/4239.pdf
http://emis.maths.adelaide.edu.au/journals/HOA/IJMMS/30/4239.pdf
http://gmm.fsksm.utm.my/~mariyam/PUBLISHED_PAPERS_DRCTM/YEAR_2000-2002/IJMMS_2000.pdf

Particle Reduction at Metal Deposition Process in Wafer Fabrication

Authors: Fakhreza Abdul, Izham Hazizi, Norzima Zulkifli, Rosnah Mohd

Contributors: Abdul Aziz, Faieza

Source: Manufacturing System ISBN: 9789535105305
Manufacturing System

Subject Terms: Composite material, Alternative medicine, Geometry, Deposition (geology), Oceanography, Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Fabrication, Engineering, Range (aeronautics), FOS: Mathematics, Pathology, Nanotechnology, Optoelectronics, Particle deposition, FOS: Nanotechnology, Metal, Wafer, Paleontology, Geology, FOS: Earth and related environmental sciences, Materials science, Particle (ecology), Physical Sciences, Wafer Map Defect Classification, Metallurgy, Medicine, Reduction (mathematics), Sediment, Mathematics

Access URL: https://www.intechopen.com/citation-pdf-url/36402
http://www.intechopen.com/articles/show/title/particle-reduction-at-metal-deposition-process-in-wafer-fabrication

An improved wrapper-based feature selection method for machinery fault diagnosis

Authors: Kar Hoou Hui, Ching Sheng Ooi, Meng Hee Lim, M. Salman Leong, Salah Al-Obaidi

Source: PLoS One
PLoS ONE, Vol 12, Iss 12, p e0189143 (2017)

Subject Terms: Artificial intelligence, Support Vector Machine, Machine Fault Diagnosis and Prognostics, Support vector machine, QH301 Biology, Science, Feature (linguistics), 02 engineering and technology, Process Fault Detection and Diagnosis in Industries, Redundancy (engineering), Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Feature vector, Vibration, Quantum mechanics, Industrial and Manufacturing Engineering, Engineering, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Physics, Rolling-element bearing, Linguistics, Fault Detection, Fault Diagnosis, Fabric Defect Detection, Computer science, FOS: Philosophy, ethics and religion, Equipment Failure Analysis, Philosophy, Operating system, Control and Systems Engineering, Physical Sciences, Wafer Map Defect Classification, Feature selection, FOS: Languages and literature, Medicine, Surface Defect Detection, Classifier (UML), Algorithms, Research Article

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https://pubmed.ncbi.nlm.nih.gov/29261689
https://doaj.org/article/13245355a7194cbead63d3f00a64872d
https://core.ac.uk/display/154349625
http://eprints.utm.my/id/eprint/74838/
http://europepmc.org/articles/PMC5738058
https://ideas.repec.org/a/plo/pone00/0189143.html
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189143
https://www.ncbi.nlm.nih.gov/pubmed/29261689

Automatic Semiconductor Wafer Image Segmentation for Defect Detection Using Multilevel Thresholding

Authors: Nor Hidayah Saad, Awaise Ahmad, Hariyanti Mohd Saleh, Ahmad Fariz Hasan

Source: MATEC Web of Conferences, Vol 78, p 01103 (2016)

Subject Terms: Autofocusing in Microscopy and Photography, Artificial intelligence, 02 engineering and technology, Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Grayscale, Engineering, Segmentation, Automated Inspection, Media Technology, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Image histogram, Image texture, Image segmentation, Histogram, Wafer, Engineering (General). Civil engineering (General), Fabric Defect Detection, Robust Line and Curve Detection using Hough Transform, Computer science, Histogram matching, Edge Detection, Semiconductor device fabrication, Thresholding, Balanced histogram thresholding, Electrical engineering, Physical Sciences, Computer Science, Wafer Map Defect Classification, Surface Defect Detection, Computer vision, Computer Vision and Pattern Recognition, TA1-2040

Access URL: https://www.matec-conferences.org/articles/matecconf/pdf/2016/41/matecconf_icongdm2016_01103.pdf
https://doaj.org/article/669bb5ca957a452e91f69f96b0997a49
https://doaj.org/article/669bb5ca957a452e91f69f96b0997a49
https://www.matec-conferences.org/articles/matecconf/ref/2016/41/matecconf_icongdm2016_01103/matecconf_icongdm2016_01103.html
https://core.ac.uk/display/91036554
https://www.matec-conferences.org/10.1051/matecconf/20167801103