3,684 research outputs found
Automatic plankton quantification using deep features
The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with handdesigned local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using Convolutional Neural Networks (CNNs) trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms
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 “good” 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
Robust particle outline extraction and its application to digital on-line holography
Peer reviewedPostprin
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 Málaga / CBUA.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
The authors wish to thank Alonso Hernández-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 DESAFÍO
(PID2020-118118RB-I00)
RAPID : research on automated plankton identification
Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.When Victor Hensen deployed the first
true plankton1 net in 1887, he and his
colleagues were attempting to answer
three fundamental questions: What
planktonic organisms are present in
the ocean? How many of each type are
present? How does the plankton’s composition
change over time? Although
answering these questions has remained
a central goal of oceanographers, the
sophisticated tools available to enumerate
planktonic organisms today offer
capabilities that Hensen probably could
never have imagined.This material
is based upon work supported by
the National Science Foundation under
Grants OCE-0325018, OCE-0324937,
OCE-0325167 and OCE-9423471,
and the European Union under grants
Q5CR-2002-71699, MAS3-ct98-0188,
and MAS2-ct92-0015
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 validation‐by‐sample, 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
Data acquisition from digital holograms of particles
A technique for data acquisition from digital holograms of particle ensembles, including preprocessing of the digital hologram, construction of a two-dimensional display of the holographic image of investigated volume, and segmentation and measurement of particle characteristics is considered. The proposed technique is realized in automatic regime and can work in real time. Results of the technique approbation using digital holograms of sand, plankton particles in water, and air bubbles in oil are presented
Digital holographic camera for plankton monitoring
A submersible digital holographic camera for measuring plankton and other particles is described. The camera provides underwater recording of digital holograms of water volume containing plankton followed by automatic restoration of holographic images of plankton species, determination of their sizes, shapes, and concentrations, and their recognition and classification. Particles with sizes of 200 μm and larger are analyzed. The water volume registered per exposure is about 1 L. The special features of the software for automatic information retrieval from digital holograms are discussed. Examples of application of the camera as an integral part of the hardware-software complex for field measurements are given. Prospects for application of this complex for ecological monitoring are discussed. The recognition criterion of the digital holographic camera and the data volume and the averaging time required for obtaining statistically reliable data on plankton species are also given
- …