[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Rezaie et al., 2020 - Google Patents

Comparison of crack segmentation using digital image correlation measurements and deep learning

Rezaie et al., 2020

View HTML
Document ID
1862303996142518426
Author
Rezaie A
Achanta R
Godio M
Beyer K
Publication year
Publication venue
Construction and Building Materials

External Links

Snippet

Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on discontinuous …
Continue reading at www.sciencedirect.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30108Industrial image inspection
    • G06T2207/30148Semiconductor; IC; Wafer
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0004Industrial image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/19Recognition of objects for industrial automation

Similar Documents

Publication Publication Date Title
Rezaie et al. Comparison of crack segmentation using digital image correlation measurements and deep learning
Huyan et al. Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network
Wei et al. Instance-level recognition and quantification for concrete surface bughole based on deep learning
Ye et al. Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks
Tong et al. Innovative method for recognizing subgrade defects based on a convolutional neural network
Huang et al. Deep learning-based instance segmentation of cracks from shield tunnel lining images
Deng et al. Vision based pixel-level bridge structural damage detection using a link ASPP network
Miao et al. Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques
Liu et al. Robust pixel-wise concrete crack segmentation and properties retrieval using image patches
Benz et al. Crack segmentation on UAS-based imagery using transfer learning
Dong et al. Innovative method for pavement multiple damages segmentation and measurement by the Road-Seg-CapsNet of feature fusion
Shamsabadi et al. Robust crack detection in masonry structures with transformers
König et al. What's cracking? A review and analysis of deep learning methods for structural crack segmentation, detection and quantification
Panella et al. Deep learning and image processing for automated crack detection and defect measurement in underground structures
Daneshvari et al. Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost
Zakaria et al. Advanced bridge visual inspection using real-time machine learning in edge devices
Abdellatif et al. Combining block-based and pixel-based approaches to improve crack detection and localisation
Gonthina et al. Deep CNN-based concrete cracks identification and quantification using image processing techniques
Wang et al. Understanding the effect of transfer learning on the automatic welding defect detection
Huyan et al. Three-dimensional pavement crack detection based on primary surface profile innovation optimized dual-phase computing
Zhang et al. Automated fatigue crack detection in steel box girder of bridges based on ensemble deep neural network
Chen et al. A hierarchical DCNN-based approach for classifying imbalanced water inflow in rock tunnel faces
Sarhadi et al. An Innovative Dense ResU-Net Architecture with T-Max-Avg Pooling for Advanced Crack Detection in Concrete Structures
Shashidhar et al. CrackSpot: Deep learning for automated detection of structural cracks in concrete infrastructure
Jafari et al. Segmentation of fatigue cracks in ancillary steel structures using deep learning convolutional neural networks