High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88% overall accuracy for an independent test set, with individual category accuracies ranging from 68% to 99%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.

https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204

Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry

The Underwater Vision Profiler (UVP) was developed to quantify the vertical distribution of macroscopic particles and zooplankton 100 mu m in size. The smaller size limit is fixed by optical resolution, whereas the larger size limit is determined by the volume of water illuminated per image. The new fifth generation instrument (UVP5) is compact (30 kg in air) and operates either as a stand-alone instrument with an independent power supply for use on a mooring or free-drifting array, or as a component of a Conductivity, Temperature, and Depth (CTD)-rosette package. Images are recorded at a frequency up to 6 Hz. If the UVP5 is interfaced with a CTD, these images are acquired and analyzed in real time. Images are recorded every 20 cm at the 1 m s(-1) lowering speed. The current maximum deployment depth is 3000 m. The recorded volume per image is 1.02 L, and the conversion equation from pixel area to size in mm(2) is S(m) = 0.003S(p)(1.3348) where S(p) is the surface of the particle in pixels and S(m) the surface in mm(2). Comparisons between the earlier UVP versions and UVP5 indicate that images ranging in size from 105 mu m to 2.66 mm are identical so historical and contemporary data sets can be compared.

/pdf/the-underwater-vision-profiler-5-an-advanced-instrument-for-357r456fgr.pdf

The Underwater Vision Profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton

From the Hensen net toward four-dimensional biological oceanography

In our continuing quest for knowledge, robots are powerful tools for accessing environments too dangerous or too remote for human exploration. Early systems functioned under close human supervision, effectively limited to executing preprogrammed tasks. However, as exploration moves to regions where communication is ineffective or unviable, robots will need to carry out complex tasks without human supervision. To enable such capabilities, robots are being enhanced by advances ranging from new sensor development to automated mission planning software, distributed robotic control, and more efficient power systems. As robotics technology becomes simultaneously more capable and economically viable, individual robots operated at large expense by teams of experts are increasingly supplemented by teams of robots used cooperatively under minimal human supervision.

/pdf/robotics-in-remote-and-hostile-environments-gt29ci0igl.pdf

Robotics in remote and hostile environments.

Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The experimental results from a plankton recognition system indicate that our approach often requires significantly less labeled images to maintain the same accuracy level as random sampling.

/pdf/active-learning-to-recognize-multiple-types-of-plankton-1w3wowmf5w.pdf

Active learning to recognize multiple types of plankton

We discuss an in situ marine imaging system based on high-speed digital line scan cameras for collection of a continuous picture of microscopic marine particles ranging in size from 200 /spl mu/m to several centimeters The system is built to operate on a 53-cm-diameter autonomous underwater vehicle or a tethered platform The digital imaging system provides a continuous record of all particles passing through a symmetric 96/spl times/96 mm sampling tube and provides views from two orthogonal directions Data are compressed using a lossless encoding technique and stored onto a disk drive Over 50 h of continuous imaging is possible using the system Data are suitable for studies requiring sizing, identification, quantification, and spatial recording of semi-transparent and opaque particles This paper summarizes the mechanical, optical, and data processing design of this instrument and discusses recent improvements We also present images and quantitative results from recent deployments in the Gulf of Mexico

A system for high-resolution zooplankton imaging

Previous oceanographic imaging systems have relied on analog video or film recording methods to capture images of microscopic marine particles. Video system resolution is limited by the two-dimensional detector array used. High-resolution images are attainable, but typically with reduced imaging area. Recording to analog videotape has the added problem of introducing noise into the data. Unless the frame rate or instrument tow speed can be varied, particles are either missed or repetitively imaged, thereby making particle density calculations difficult or inaccurate. These methods also have the disadvantage of requiring time-consuming postprocessing of the data before the images can be analyzed on a computer. We have developed an imaging system based on high-speed digital line scan cameras to address the above shortcomings. The system is built around the 96 mm/spl times/96mm sampling tube of the University of South Florida's High Resolution Sampler (HRS) towed.

Design and initial results of high-resolution Shadowed Image Particle Profiling and Evaluation Recorder

The instrument of the instant invention provides for a fluidic plankton or other micro-particle analyzer that is capable of data reduction via a novel reduction circuit for better resolution in identification and quantification of the particles. By virtue of using line scan or similar cameras the instant system is capable of high resolution without as much interference as experienced with two-dimensional systems. The data reduction circuit also enables the microscopic material to be analyzed and recorded with better adaptability to current computer storage systems.

Shadowed image particle profiling and evaluation recorder

The oceanographic community is seeing new and substantial advances in the development of underwater vehicles. The future is near when we will witness networked fleets that will acquire synoptic data over wide areas. The potential of multiple, concurrent AUV/UUV deployments promises the ocean research community with increased data accuracy, the elimination of spatial and temporal aliasing, and more efficient and cost-effective means of data dissemination. To complement these advances in AUV/UUV technology, we will need continued development of new and more sophisticated sensor systems, to expand our understanding of the ocean's processes. The University of South Florida's Center for Ocean Technology specializes in developing sensor systems to meet the ever-increasing need of oceanographers to collect high-resolution data relating to the chemical, biological and physical processes of the ocean. This paper presents up-to-date information about three of those instruments.

Sensor development: progress towards systems for AUVs/UUVs MTS Oceans 2000

The systems and methods of the present invention provides for micro-particle analyzer that is capable of data reduction via a novel reduction circuit for improved resolution in identification and quantification of the particles. By virtue of using line scan or similar cameras (11, 12) the instant system (10) is capable of high resolution without as much interference as experienced with two-dimensional systems. The data reduction circuit (50) also enables the microscopic material to be analyzed and recorded with better adaptability to current computer storage systems.

Systems and methods for shadowed image particle profiling and evaluation

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