Search Results (5)

This study considers the applicability and effectiveness of the Viola–Jones method to automatically distinguish zooplankton particles from the background in images reconstructed from digital holograms obtained in natural conditions. For the first time, this algorithm is applied to holographic images containing coherent noise and residual defocusing. The method was trained on 880 annotated (marked) holographic images of Daphnia magna along with 120 background frames. It was then tested on independent laboratory and field datasets, including morphologically related taxa. With optimized settings, the precision of the algorithm reached ~90% and F1~85% on noisy holographic images, and the algorithm also demonstrated the preliminary ability to recognize similar taxa without retraining. The algorithm is well suited for analyzing holographic data as a fast and resource-efficient pre-filter—it effectively separates particles from the background and thereby allows subsequent classification or its application in real-time aquatic environment monitoring systems. The article presents experimental results demonstrating the efficiency of this algorithm during plankton monitoring in situ. Full article

Water temperature controls the physiology, growth rate, distribution, and behavior of most plankton populations in the sea and thus affects the energy transfer in marine ecosystems. The present study focuses on the influence of seasonal changes in sea surface temperature on phytoplankton and the size distribution of copepods in the Arraial do Cabo Upwelling System (Brazil), where a wind-driven coastal upwelling can lead to multiple distinct bottom-up cascade effects on the food web. To address the potential effect of the seasonal changes, environmental data were obtained and the abundance of plankton determined from monthly samples collected in triplicate from 2010 to 2014. The samples were analyzed on a Benchtop FlowCAM (FC), and copepods (<1000 µm) were classified according to their Ellipses Equivalent Major Axis using image analysis software ImageJ (IJ). For IJ analysis, a batch-processing macro was built to open all FC raw images and then crop each copepod individually into a single picture. Using these images, prosome and urosome lengths were manually measured with the straight-line tool in IJ. With the combinations of measurements obtained in the IJ adjusted as FC measurements, we established a new, faster, and more effective way to measure copepods. With the copepod size classification, we found that there is a cycle in copepod size combined with the upwelling cycle that is related to temperature rather than to phytoplankton growth. Copepod abundance as a whole peaked during the autumn, winter, and spring seasons. The method performed here proved that FC is an effective tool for classifying copepod sizes and detecting seasonal variation. Full article

Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10% more consistent predictions of substructures. Full article

In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection. Full article

In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset. Full article