A framework for the automatic identification of algae (Neomeris vanbosseae M.A. Howe):U&lt;sup&gt;3&lt;/sup&gt;S

Tan, Ching Soon; Lau, Phooi Yee; Phang, Siew‐Moi; Low, Tang Jung

doi:10.1109/iccoins.2014.6868360

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The use of color-based segmentation, species specific features and an underwater camera in study [12] implies that the algae monitoring system cannot be applied on a different hardware platform such as an UAV, USV, or smartphone and is susceptible to occlusion and variations in illumination, which are regular occurrences in an outdoor environment. Similarly, the novel approach of combining the use of a smartphone camera and the inertial sensors present on a robotic fish to detect the shoreline and hence perform image segmentation to detect algal blooms as presented in the paper [11] is optimized for their specific hardware platform (i.e., robotic fish) and is susceptible to variable illumination, occlusion and shadows cast by surrounding vegetation.…”

Section: B Computer Vision Based Techniquesmentioning

confidence: 99%

“…However, current methodologies for algae monitoring require significant manpower, making them economically unfeasible (e.g., it was projected that a budget of $3.5 million was required to monitor the 100 largest lakes in Oklahoma once a month [9]). Alternatively, methods may depend upon the hardware available, which can be in the form of drones [10], robotic fish [11], underwater cameras [12] or satellites [13], which limits their applicability in varying environmental, topological and socio-economic conditions.…”

Section: Introductionmentioning

confidence: 99%

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Algae Detection Using Computer Vision and Deep Learning

Samantaray¹,

Yang²,

Dietz³

et al. 2018

Preprint

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A disconcerting ramification of water pollution caused by burgeoning populations, rapid industrialization and modernization of agriculture, has been the exponential increase in the incidence of algal growth across the globe. Harmful algal blooms (HABs) have devastated fisheries, contaminated drinking water and killed livestock, resulting in economic losses to the tune of millions of dollars. Therefore, it is important to constantly monitor water bodies and identify any algae build-up so that prompt action against its accumulation can be taken and the harmful consequences can be avoided. In this paper, we propose a computer vision system based on deep learning for algae monitoring. The proposed system is fast, accurate and cheap, and it can be installed on any robotic platforms such as USVs and UAVs for autonomous algae monitoring. The experimental results demonstrate that the proposed system can detect algae in distinct environments regardless of the underlying hardware with high accuracy and in real time.

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Section: B Computer Vision Based Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algae Detection Using Computer Vision and Deep Learning

Samantaray¹,

Yang²,

Dietz³

et al. 2018

Preprint

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Classification of Microscopic Algae: An Observational Study with AlexNet

Pardeshi

Deshmukh²

2020

Advances in Intelligent Systems and Computing

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Region Growing for Segmenting Green Microalgae Images

Borges

Oliveira

Silva

et al. 2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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We describe a specialized methodology for segmenting 2D microscopy digital images of freshwater green microalgae. The goal is to obtain representative algae shapes to extract morphological features to be employed in a posterior step of taxonomical classification of the species. The proposed methodology relies on the seeded region growing principle and on a fine-tuned filtering preprocessing stage to smooth the input image. A contrast enhancement process then takes place to highlight algae regions on a binary pre-segmentation image. This binary image is also employed to determine where to place the seed points and to estimate the statistical probability distributions that characterize the target regions, i.e., the algae areas and the background, respectively. These preliminary stages produce the required information to set the homogeneity criterion for region growing. We evaluate the proposed methodology by comparing its resulting segmentations with a set of corresponding ground-truth segmentations (provided by an expert biologist) and also with segmentations obtained with existing strategies. The experimental results show that our solution achieves highly accurate segmentation rates with greater efficiency, as compared with the performance of standard segmentation approaches and with an alternative previous solution, based on level-sets, also specialized to handle this particular problem.

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A framework for the automatic identification of algae (Neomeris vanbosseae M.A. Howe):U<sup>3</sup>S

Cited by 5 publications

References 8 publications

Algae Detection Using Computer Vision and Deep Learning

Algae Detection Using Computer Vision and Deep Learning

Classification of Microscopic Algae: An Observational Study with AlexNet

Region Growing for Segmenting Green Microalgae Images

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