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Papers
20 resultsShowing papers similar to Application of Pattern Recognition and Computer Vision Tools to Improve the Morphological Analysis of Microplastic Items in Biological Samples
ClearA Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics
An open-source computer vision application was developed to automatically count and classify microplastics in microscopy images, achieving accuracy comparable to manual counting while processing samples orders of magnitude faster, offering the scientific community a free tool to reduce the bottleneck of tedious visual microplastic enumeration.
Connected Component Labelling in the determination of morphometric features of microplastic particles in samples of different matrices
Researchers developed a Connected Component Labeling (CCL) method using the Union-Find algorithm in C++ to automate morphometric analysis of microplastic particles in Baltic Sea fish tissues, enabling automatic measurement of particle area, size, and shape without manual microscope software input.
Connected Component Labelling in the determination of morphometric features of microplastic particles in samples of different matrices
Researchers applied Connected Component Labeling (CCL) image analysis to optimize microscopic quantification of microplastic particles in Baltic Sea fish tissues and organs, combining optical microscopy for size and shape determination with FT-IR spectrometry for polymer identification.
An Image-Processing Tool for Size and Shape Analysis of Manufactured Irregular Polyethylene Microparticles
Scientists developed a free, automated image-processing tool that can quickly analyze microscope images to count and measure irregularly shaped microplastic particles, calculating their size, shape, and distribution. Traditional methods require manually counting particles under a microscope, which is slow and impractical for large samples. Better tools for measuring microplastic contamination help researchers more accurately assess how much plastic pollution exists in water and soil that affects human exposure.
Implementation of an open source algorithm for particle recognition and morphological characterisation for microplastic analysis by means of Raman microspectroscopy
An automated particle recognition algorithm was implemented to speed up the identification and measurement of microplastics in Raman spectroscopy images. Automated analysis reduces the time and subjectivity involved in manual microplastic counting, improving research efficiency.
Identification Tools of Microplastics from Surface Water Integrating Digital Image Processing and Statistical Techniques
This study demonstrated that digital image analysis can automate and improve the characterization of microplastic particles collected from river water, capturing detailed shape, color, and size data that manual microscopy cannot easily achieve at scale. Better identification tools like this are essential for standardizing microplastic monitoring across different waterways and research groups.
Automatic Counting and Classification of Microplastic Particles
Researchers developed an automatic system for counting and classifying microplastic particles in marine samples, applying image analysis techniques to address the growing problem of plastic debris entering the food chain via marine species ingestion.
An Open-Source Computer-Vision-Based Method for Spherical Microplastic Settling Velocity Calculation
Researchers developed an open-source computer vision method to measure the settling velocity of spherical microplastics, replacing subjective manual methods with automated image analysis. The tool provides a standardized, accessible approach for predicting microplastic transport and fate in aquatic environments.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
2D imaging tools for harmonisation in plastic pollution data
Researchers evaluated four 2D imaging and image segmentation methodologies for measuring the morphological characteristics -- size, shape, and colour -- of mesoplastics and microplastics, aiming to harmonize physical characterization data across studies conducted in different marine environments.
A practical primer for image-based particle measurements in microplastic research
This paper provides a practical guide for researchers to measure the size and shape of microplastic particles using image-based methods, proposing standardized metrics and workflows aligned with international guidelines. The authors recommend specific measurements for particle size and three shape descriptors that capture all relevant dimensions of particle shape. Standardizing how microplastics are measured is important because consistent data is needed to accurately assess the health risks these particles pose to ecosystems and humans.
Implementing Edge Based Object Detection For Microplastic Debris
This study developed an edge-based object detection algorithm for identifying microplastic debris in images. Automated detection methods are important for scaling up microplastic monitoring, particularly in field settings where manual visual inspection of thousands of particles is impractical.
Deep learning based approach for automated characterization of large marine microplastic particles
A deep learning approach using Mask R-CNN was trained on 3,000 images of marine microplastic particles to automatically locate, classify, and segment particles by shape categories including fiber, fragment, pellet, and rod. The model achieved high accuracy and outperformed manual visual inspection for characterizing large marine microplastic datasets.
Microplastic and nanoplastic analysis methods, tests and reference materials
Researchers described a workflow combining a streamlined experimental setup with automated image analysis to quantify marine microplastic debris, addressing the limitations of labor-intensive manual counting methods that currently prevent scalable and consistent global plastic monitoring.
Digital Image Analysis and Multivariate Data Analysis as Tools for the Identification of Microplastics in Surface Waters: The Case of the Vistula River (Central Europe)
Researchers demonstrated digital image analysis combined with microscopy as a tool for identifying and characterizing microplastic particles from Vistula River surface water samples, performing exhaustive quantitative and qualitative evaluation of 2D and 3D morphology to characterize MP abundance and composition.
Proceeding the categorization of microplastics through deep learning-based image segmentation
Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
Image processing tools in the study of environmental contamination by microplastics: reliability and perspectives
Researchers assessed the reliability of image processing tools for studying microplastic contamination, finding that while these tools offer efficiency gains, inconsistent methodologies limit comparability between studies and call for standardization.
An Open-Source Computer Vision-Based Method for Microplastic Settling Velocity Calculation
Researchers developed an open-source computer vision method to measure microplastic settling velocities from video recordings, enabling low-cost quantification of how particles of different sizes and densities sink in water columns with implications for predicting MP fate in aquatic environments.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.