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Comparison of CNN and MLP classifiers for algae detection in underwater pipelines
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot
Visualizing and Understanding Convolutional Networks
inferredcites →https://doi.org/10.1007/978-3-540-44871-6_7openalex-snapshot
Underwater Cable Tracking by Visual Feedback
inferredcites →https://doi.org/10.1109/coconet.2015.7411282openalex-snapshot
Texture based multi-view face recognition in noisy images using BRINE feature
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccic.2014.7238412openalex-snapshot
Tracking multiple moving object based on combined color and centroid feature in video sequence
inferredcites →https://doi.org/10.1109/icdsp.2016.7868522openalex-snapshot
Deep learning-based recognition of underwater target
inferredcites →https://doi.org/10.1109/icina.2010.5636373openalex-snapshot
Rotation-invariant texture features extraction using Dual-Tree Complex Wavelet Transform
inferredcites →https://doi.org/10.1109/icme.2012.90openalex-snapshot
Comparison of Curvelet and Wavelet Texture Features for Content Based Image Retrieval
inferredcites →https://doi.org/10.1109/icpr.1994.576881openalex-snapshot
Word-level training of a handwritten word recognizer based on convolutional neural networks
inferredcites →https://doi.org/10.1109/oceans-bergen.2013.6608089openalex-snapshot
Underwater pipeline and cable inspection using autonomous underwater vehicles
inferredcites →https://doi.org/10.1109/tpami.2006.244openalex-snapshot
Face Description with Local Binary Patterns: Application to Face Recognition
inferredcites →https://doi.org/10.1134/s0965542511080112openalex-snapshot
Comparative study of texture detection and classification algorithms
inferredcites →https://doi.org/10.1134/s1054661813020028openalex-snapshot
Species classification of aquatic plants using PSVM and ANFIS
inferredcites →https://doi.org/10.11648/j.acm.20150401.12openalex-snapshot
Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension
inferredcites →https://doi.org/10.1590/2175-7860201566408openalex-snapshot
Update of the Brazilian floristic list of Algae and Cyanobacteria
inferredcites →https://doi.org/10.4028/www.scientific.net/amm.303-306.1552openalex-snapshot
A Simple Algorithm for Video Object Tracking
inferredcites →https://doi.org/10.4236/ajcm.2016.64035openalex-snapshot
Improved Video Moving Target Tracking Based on Camshift
inferredcites →https://doi.org/10.48550/arxiv.1306.4345openalex-snapshot
An Overview of the Research on Texture Based Plant Leaf Classification
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.5220/0006128204760483openalex-snapshot
LBP Histogram Selection based on Sparse Representation for Color Texture Classification
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4679260openalex-extracted
Bespoke Sequence of Transformations for an Enhanced Entropic Wavelet Energy Spectrum Discernment for Higher Efficacy Detection of Metamorphic Malware
inferred← citeshttps://doi.org/10.1002/cpe.8128openalex-snapshot
Detection of algal tiny objects based on morphological features
inferred← citeshttps://doi.org/10.1007/978-3-031-59057-3_5openalex-snapshot
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection
inferred← citeshttps://doi.org/10.1007/s11831-021-09639-xopenalex-snapshot
Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments
inferred← citeshttps://doi.org/10.1007/s12530-023-09523-yopenalex-snapshot
Enhancing fault detection and diagnosis systems for a chemical process: a study on convolutional neural networks and transfer learning
inferred← citeshttps://doi.org/10.1007/s13201-023-01919-0openalex-snapshot
Comparative assessment of artificial intelligence (AI)-based algorithms for detection of harmful bloom-forming algae: an eco-environmental approach toward sustainability
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2021.109214openalex-snapshot
The application of fully unmanned robotic systems for inspection of subsea pipelines
inferred← citeshttps://doi.org/10.1109/access.2023.3280410openalex-snapshot
A Systematic Review of Deep Learning Microalgae Classification and Detection
inferred← citeshttps://doi.org/10.1109/besc59560.2023.10386562openalex-snapshot
MAgic: A Morphable Attention Based Algal Tiny Object Detection Model
inferred← citeshttps://doi.org/10.1109/csecs60003.2023.10428252openalex-snapshot
Algae Object Detection Algorithm Based on Improved YOLOv5
inferred← citeshttps://doi.org/10.1109/iciprob54042.2022.9798730openalex-snapshot
Multiclass-Classification of Algae using Dc-GAN and Transfer Learning
inferred← citeshttps://doi.org/10.1109/icsgteis60500.2023.10424230openalex-snapshot
AI-Facilitated Ambient Factor-Based Annealment and Resiliency Sufficiency in Austere Locales
inferred← citeshttps://doi.org/10.1109/ictss.2018.8550012openalex-snapshot
Moving Image Interpretation Models to Support City Analysis
inferred← citeshttps://doi.org/10.1109/mosicom59118.2023.10458750openalex-snapshot
Performance Analysis of Subsea Pipeline Defect Classification Using Supervised Machine Learning
inferred← citeshttps://doi.org/10.1109/ocit53463.2021.00058openalex-snapshot
RBC Classification using Deep Learning
inferred← citeshttps://doi.org/10.20855/ijav.2022.27.11820openalex-snapshot
Research on Underwater Acoustic Target Depth Classification Based on Modal Filtering Characteristics of Long Horizontal Line Array
inferred← citeshttps://doi.org/10.2139/ssrn.5183492openalex-snapshot
AI-centric Proxy Design Synthesis for Non-Obvious Link/Entity and Higher-Order Network Discernment
inferred← citeshttps://doi.org/10.29220/csam.2024.31.3.309openalex-snapshot
Classification of algae in watersheds using elastic shape
inferred← citeshttps://doi.org/10.3389/fmars.2022.1070638openalex-snapshot
An improved algae-YOLO model based on deep learning for object detection of ocean microalgae considering aquacultural lightweight deployment
inferred← citeshttps://doi.org/10.3390/agriculture10110529openalex-snapshot
Applicability Evaluation of the Hydrological Image and Convolution Neural Network for Prediction of the Biochemical Oxygen Demand and Total Phosphorus Loads in Agricultural Areas
inferred← citeshttps://doi.org/10.3390/ijgi14090350openalex-snapshot
Lightweight Deep Learning Approaches for Lithological Mapping in Vegetated Terrains of the Vălioara Valley, Romania
inferred← citeshttps://doi.org/10.3390/w11071338openalex-snapshot
Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network
inferred← citeshttps://doi.org/10.3390/w12082292openalex-snapshot
Hydrological Image Building Using Curve Number and Prediction and Evaluation of Runoff through Convolution Neural Network
inferred← citeshttps://doi.org/10.3390/w12123534openalex-snapshot
Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image
inferred← citeshttps://doi.org/10.3390/w13020239openalex-snapshot
Application of Convolution Neural Networks and Hydrological Images for the Estimation of Pollutant Loads in Ungauged Watersheds
inferred← citeshttps://doi.org/10.3390/w14081275openalex-snapshot
Deep Learning-Based Algal Detection Model Development Considering Field Application
inferred← citeshttps://doi.org/10.3390/w14142219openalex-snapshot
Computer Vision Based Deep Learning Approach for the Detection and Classification of Algae Species Using Microscopic Images
inferred← citeshttps://doi.org/10.70675/56333863zfef0z4cdezb74azeab269348fceopenalex-snapshot
Simulation réaliste basée sur des techniques d'apprentissage pour l'amélioration du diagnostic en contrôle non destructif
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