Academic literature on the topic 'Plankton image segmentation'

In China, sea cucumber cultivation is developing rapidly, but sea cucumber catching still relies on inefficient manual work. Nowadays, the acquisition of underwater sea cucumber images and locating of sea cucumber target have provided technical support for underwater sea cucumber catching robots. However, there are still some problems to be solved, such as the degradation, edge blur and low contrast of underwater sea cucumber images due to the uneven light underwater and the absorption and scattering of light by water; and the cusp noises in the sea cucumber images produced by shells, gravers, planktons and other things in natural environment. Aiming at these problems, the underwater sea cucumber rapid locating method based on morphological opening reconstruction and max-entropy threshold algorithm (OR-META) is proposed in this paper. Firstly, the morphological opening reconstruction is adopted to smooth the original sea cucumber gray image; next, the max-entropy threshold segmentation algorithm is employed to segment the smoothed sea cucumber image, and the sea cucumber region in binary image is recognized according to area characteristics; finally, the position of sea cucumber region in the image is determined utilizing its centroid. In order to obtain the best result of sea cucumber image segmentation, three typical image segmentation algorithms OR-2DMETA, OR-OTSU and OR-2DOTSU are selected to compare with the OR-META. It is observed that the OR-META is obviously superior to the other three algorithms in qualitatively analyzing the image segmentation quality and quantitatively calculating the running time. To further analyze the image segmentation quality and locating accuracy, the images segmented by the OR-META are compared with manually segmented images. The comparison shows that although the image segmentation accuracy of the OR-META is low, with average target segmentation correct rate of 68.99%, the position of sea cucumber target in segmentation is predictable, which means that it will be within the directly drawn sea cucumber region. This demonstrates that the locating accuracy of the method is high. In addition, the method also has good real-time performance. It can be concluded from the experiment that for a RGB image with resolution ratio of 1280[Formula: see text][Formula: see text][Formula: see text]720 pixels, the average centroid Euclidean distance error of the OR-META segmented image is only 52.67 pixels, and its average running time is 0.6 s, which are qualified for the requirements of the sea cucumber catching robots.

Plâncton são organismos microscópicos que constituem a base da cadeia alimentar de ecossistemas aquáticos. Eles têm importante papel no ciclo do carbono pois são os responsáveis pela absorção do carbono na superfície dos oceanos. Detectar, estimar e monitorar a distribuição das diferentes espécies são atividades importantes para se compreender o papel do plâncton e as consequências decorrentes de alterações em seu ambiente. Parte dos estudos deste tipo é baseada no uso de técnicas de imageamento de volumes de água. Devido à grande quantidade de imagens que são geradas, métodos computacionais para auxiliar no processo de análise das imagens estão sob demanda. Neste trabalho abordamos o problema de identificação da espécie. Adotamos o pipeline convencional que consiste dos passos de detecção de alvo, segmentação (delineação de contorno), extração de características, e classificação. Na primeira parte deste trabalho abordamos o problema de escolha de um algoritmo de segmentação adequado. Uma vez que a avaliação de resultados de segmentação é subjetiva e demorada, propomos um método para avaliar algoritmos de segmentação por meio da avaliação da classificação no final do pipeline. Experimentos com esse método mostraram que algoritmos de segmentação distintos podem ser adequados para a identificação de espécies de classes distintas. Portanto, na segunda parte do trabalho propomos um método de classificação que leva em consideração múltiplas segmentações. Especificamente, múltiplas segmentações são calculadas e classificadores são treinados individualmente para cada segmentação, os quais são então combinados para construir o classificador final. Resultados experimentais mostram que a acurácia obtida com a combinação de classificadores é superior em mais de 2% à acurácia obtida com classificadores usando uma segmentação fixa. Os métodos propostos podem ser úteis para a construção de sistemas de identificação de plâncton que sejam capazes de se ajustar rapidamente às mudanças nas características das imagens.<br>Plankton are microscopic organisms that constitute the basis of the food chain of aquatic ecosystems. They have an important role in the carbon cycle as they are responsible for the absorption of carbon in the ocean surfaces. Detecting, estimating and monitoring the distribution of plankton species are important activities for understanding the role of plankton and the consequences of changes in their environment. Part of these type of studies is based on the analysis of water volumes by means of imaging techniques. Due to the large quantity of generated images, computational methods for helping the process of image analysis are in demand. In this work we address the problem of species identification. We follow the conventional pipeline consisting of target detection, segmentation (contour delineation), feature extraction, and classification steps. In the first part of this work we address the problem of choosing an appropriate segmentation algorithm. Since evaluating segmentation results is a subjective and time consuming task, we propose a method to evaluate segmentation algorithms by evaluating the classification results at the end of the pipeline. Experiments with this method showed that distinct segmentation algorithms might be appropriate for identifying species of distinct classes. Therefore, in the second part of this work we propose a classification method that takes into consideration multiple segmentations. Specifically, multiple segmentations are computed and classifiers are trained individually for each segmentation, which are then combined to build the final classifier. Experimental results show that the accuracy obtained with the combined classifier is superior in more than 2% to the accuracy obtained with classifiers using a fixed segmentation. The proposed methods can be useful to build plankton identification systems that are able to quickly adjust to changes in the characteristics of the images.

Indiana University-Purdue University Indianapolis (IUPUI)<br>Recently there has been a lot of effort in developing systems for sampling and automatically classifying plankton from the oceans. Existing methods assume the specimens have already been precisely segmented, or aim at analyzing images containing single specimen (extraction of their features and/or recognition of specimens as single targets in-focus in small images). The resolution in the existing systems is limiting. Our goal is to develop automated, very high resolution image sensing of critically important, yet under-sampled, components of the planktonic community by addressing both the physical sensing system (e.g. camera, lighting, depth of field), as well as crucial image extraction and recognition routines. The objective of this thesis is to develop a framework that aims at (i) the detection and segmentation of all organisms of interest automatically, directly from the raw data, while filtering out the noise and out-of-focus instances, (ii) extract the best features from images and (iii) identify and classify the plankton species. Our approach focusses on utilizing the full computational power of a multicore system by implementing a parallel programming approach that can process large volumes of high resolution plankton images obtained from our newly designed imaging system (In Situ Ichthyoplankton Imaging System (ISIIS)). We compare some of the widely used segmentation methods with emphasis on accuracy and speed to find the one that works best on our data. We design a robust, scalable, fully automated system for high-throughput processing of the ISIIS imagery.