165 research outputs found
Vision-based techniques for automatic marine plankton classification
Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.Funding for open access charge: Universidad de MĂĄlaga / CBUA.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
The authors wish to thank Alonso HernĂĄndez-Guerra for his frm support in the
development of oceanographic technology. Special thanks to Laia Armengol for her help in the domain
of plankton. This study has been funded by Feder of the UE through the RES-COAST Mac-Interreg pro ject (MAC2/3.5b/314). We also acknowledge the European Union projects SUMMER (Grant Agreement
817806) and TRIATLAS (Grant Agreement 817578) from the Horizon 2020 Research and Innovation
Programme and the Ministry of Science from the Spanish Government through the Project DESAFĂO
(PID2020-118118RB-I00)
Validation methods for plankton image classification systems
In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validationâbyâsample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates
Deep learning for Plankton and Coral Classification
Oceans are the essential lifeblood of the Earth: they provide over 70% of the
oxygen and over 97% of the water. Plankton and corals are two of the most
fundamental components of ocean ecosystems, the former due to their function at
many levels of the oceans food chain, the latter because they provide spawning
and nursery grounds to many fish populations. Studying and monitoring plankton
distribution and coral reefs is vital for environment protection. In the last
years there has been a massive proliferation of digital imagery for the
monitoring of underwater ecosystems and much research is concentrated on the
automated recognition of plankton and corals. In this paper, we present a study
about an automated system for monitoring of underwater ecosystems. The system
here proposed is based on the fusion of different deep learning methods. We
study how to create an ensemble based of different CNN models, fine tuned on
several datasets with the aim of exploiting their diversity. The aim of our
study is to experiment the possibility of fine-tuning pretrained CNN for
underwater imagery analysis, the opportunity of using different datasets for
pretraining models, the possibility to design an ensemble using the same
architecture with small variations in the training procedure. The experimental
results are very encouraging, our experiments performed on 5 well-knowns
datasets (3 plankton and 2 coral datasets) show that the fusion of such
different CNN models in a heterogeneous ensemble grants a substantial
performance improvement with respect to other state-of-the-art approaches in
all the tested problems. One of the main contributions of this work is a wide
experimental evaluation of famous CNN architectures to report performance of
both single CNN and ensemble of CNNs in different problems. Moreover, we show
how to create an ensemble which improves the performance of the best single
model
Semi-automated image analysis for the identification of bivalve larvae from a Cape Cod estuary
Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography: Methods 10 (2012): 538-554, doi:10.4319/lom.2012.10.538.Machine-learning methods for identifying planktonic organisms are becoming well-established. Although similar morphologies among species make traditional image identification methods difficult for larval bivalves, species-specific shell birefringence patterns under polarized light permit identification by color and texture-based features. This approach uses cross-polarized images of bivalve larvae, extracts Gabor and color angle features from each image, and classifies images using a Support Vector Machine. We adapted this method, which was established on hatchery-reared larvae, to identify bivalve larvae from a series of field samples from a Cape Cod estuary in 2009. This method had 98% identification accuracy for four hatchery-reared species. We used a multiplex polymerase chain reaction (PCR) method to confirm field identifications and to compare accuracies to the software classifications. Image classification of larvae collected in the field had lower accuracies than both the classification of hatchery species and PCR-based identification due to error in visually classifying unknown larvae and variability in larval images from the field. A six-species field training set had the best correspondence to our visual classifications with 75% overall agreement and individual species agreements from 63% to 88%. Larval abundance estimates for a time-series of field samples showed good correspondence with visual methods after correction. Overall, this approach represents a cost- and time-saving alternative to molecular-based identifications and can produce sufficient results to address long-term abundance and transport-based questions on a species-specific level, a rarity in studies of bivalve larvae.This project was supported by an award to S. Gallager
and C. Mingione Thompson from the Estuarine Reserves Division, Office
of Ocean and Coastal Resource Management, National Ocean Service,
National Oceanic and Atmospheric Administration and a grant from
Woods Hole Oceanographic Institutionâs Coastal Ocean Institute
Application of statistical learning theory to plankton image analysis
Submitted to the Joint Program in Applied Ocean Science and Engineering
in partial fulfillment of the requirements for the degree of Doctor of Philosophy
At the Massachusetts Institute of Technology
and the Woods Hole Oceanographic Institution
June 2006A fundamental problem in limnology and oceanography is the inability to quickly
identify and map distributions of plankton. This thesis addresses the problem by
applying statistical machine learning to video images collected by an optical sampler,
the Video Plankton Recorder (VPR). The research is focused on development
of a real-time automatic plankton recognition system to estimate plankton abundance.
The system includes four major components: pattern representation/feature
measurement, feature extraction/selection, classification, and abundance estimation.
After an extensive study on a traditional learning vector quantization (LVQ)
neural network (NN) classifier built on shape-based features and different pattern
representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method
outperforms the traditional shape-based-NN classifier method by 12% in classification
accuracy. Subsequent plankton abundance estimates are improved in the regions of
low relative abundance by more than 50%.
Both the NN and SVM classifiers have no rejection metrics. In this thesis, two
rejection metrics were developed. One was based on the Euclidean distance in the
feature space for NN classifier. The other used dual classifier (NN and SVM) voting as
output. Using the dual-classification method alone yields almost as good abundance
estimation as human labeling on a test-bed of real world data. However, the distance
rejection metric for NN classifier might be more useful when the training samples are
not âgoodâ ie, representative of the field data.
In summary, this thesis advances the current state-of-the-art plankton recognition
system by demonstrating multi-scale texture-based features are more suitable
for classifying field-collected images. The system was verified on a very large realworld
dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.This work was supported by National Science Foundation Grants OCE-9820099
and Woods Hole Oceanographic Institution academic program
Efficient Unsupervised Learning for Plankton Images
Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect
into consequent morphological and dynamical modifications. Nowadays, the availability of advanced
automatic or semi-automatic acquisition systems has been allowing the production of an increasingly
large amount of plankton image data. The adoption of machine learning algorithms to classify such
data may be affected by the significant cost of manual annotation, due to both the huge quantity of
acquired data and the numerosity of plankton species. To address these challenges, we propose an
efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms.
We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the
learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art
unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of
plankton images. The proposed pipeline outperforms the benchmark algorithms for all the plankton
datasets included in our analysis, providing better image embedding properties
Multimodal Image and Spectral Feature Learning for Efficient Analysis of Water-Suspended Particles
apan Science and Technology Agency SICORP and Natural Environment Research Council (JST-NERC SICORP Marine Sensor Proof of Concept Grant JPMJSC1705, NE/R01227X/1); JSPS KAKENHI Grant (18K13934 and 18H03810); Sumitomo Foundation: Grant for environmental Research Project (203122). Acknowledgments. The authors thank Dr. T. Fukuba for the support for building the experimental setup. The authors also thank Dr. H. Sawada for providing samples for this work.Peer reviewedPublisher PD
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