3,222 research outputs found

    Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey

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    Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc

    Object classification in semi structured enviroment using forward-looking sonar

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    La exploración submarina utilizando robots ha ido en aumento en los últimos años. La automatización de tareas tales como monitoreo, inspección y mantenimiento bajo el agua requiere la comprensión del entorno del robot. El reconocimiento de objetos en la escena se está convirtiendo en un problema crítico para estos sistemas. En este trabajo, se estudia una tubería de clasificación de objetos bajo el agua aplicada en imágenes acústicas adquiridas por Forward-Looking Sonar (FLS). La segmentación de objetos combina el umbral, la búsqueda de píxeles conectados y las técnicas de análisis de picos de intensidad. El descriptor del objeto extrae la intensidad y las características geométricas de los objetos detectados. Se presenta una comparación entre los clasificadores Máquina de vectores de soporte, Vecinos más cercanos a K y Árboles aleatorios. Se desarrolló una herramienta de código abierto para anotar y clasificar los objetos y evaluar su rendimiento de clasificación. El método propuesto segmenta y clasifica eficientemente las estructuras en la escena utilizando un conjunto de datos real adquirido por un vehículo submarino en un área de puerto. Los resultados experimentales demuestran la solidez y precisión del método descrito en este documento.The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot’s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper.• National Institute of Science and Technology - Integrated Oceanography and Multiple Uses of the Continental Shelf and Adjacent Ocean - Integrated Oceanography Center INCT-Mar COI funded by CNPq. Beca 610012/2011-8 • BS-NAVLOC (CAPES no 321/15, DGPU 7523 / 14-9, proyecto MEC PHBP14 / 00083)peerReviewe

    Camera methods for the assessment of coastal biodiversity in low visibility environments

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    Coastal marine environments are important ecological, economic and social areas providing valuable services such as coastal protection, areas of recreation and tourism, fishing, climate regulation, biotic materials and biofuels. Marine renewable energy developments in the coastal environment are becoming a key objective for many countries globally. Assessing and monitoring the impacts of these developments on features, such as coastal biodiversity, becomes a difficult prospect in these environments due to the complexity of marine process at the locations in which these developments are targeted. This thesis explores the main challenges faced when assessing biodiversity in dynamic coastal environments, in particular those susceptible to high levels of turbidity. Various underwater camera techniques were trialled in reduced visibility environments including baited remote underwater video (BRUV), drop-down video and hydroacoustic methods. This research successfully refined BRUV guidelines in the North-East Atlantic region and identified key methodological and environmental factors influencing data collected BRUV deployments. Key findings included mackerel as the recommended bait type in this region and highlighting the importance of collecting consistent metadata when using these methods. In areas of high turbidity, clear liquid optical chambers (CLOCs) were successfully used to enhance the quality of information gathered using underwater cameras when monitoring benthic fauna and fish assemblages. CLOCs were applied to both conventional BRUV camera systems and benthic drop-down camera systems. Improvements included image quality, species and habitat level identification, and taxonomic richness. Evaluations of the ARIS 3000 imaging sonar and its capability of visualising distinguishing identifying features in low visibility environments for motile fauna showed mixed results with morphologically distinct species such as elasmobranchs much clearer in the footage compared to individuals belonging to finfish families. A combined approach of optical and hydroacoustic camera methods may be most suitable for adequately assessing coastal biodiversity in low visibility environments

    Algorithms for propagation-aware underwater ranging and localization

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    Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

    Emerging technologies for reef fisheries research and management [held during the 56th annual Gulf and Caribbean Fisheries Institute meeting in Tortola, British Virgin Islands, November 2003]

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    This publication of the NOAA Professional Paper NMFS Series is the product of a special symposium on “Emerging Technologies for Reef Fisheries Research and Management” held during the 56th annual Gulf and Caribbean Fisheries Institute meeting in Tortola, British Virgin Islands, November 2003. The purpose of this collection is to highlight the diversity of questions and issues in reef fisheries management that are benefiting from applications of technology. Topics cover a wide variety of questions and issues from the study of individual behavior, distribution and abundance of groups and populations, and associations between habitats and fish and shellfish species.(PDF files contains 124 pages.

    Moderate-depth benthic habitats of St. John, U.S. Virgin Islands

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    The National Oceanic and Atmospheric Administration’s (NOAA) Center for Coastal Monitoring and Assessment’s (CCMA) Biogeography Branch and the U.S. National Park Service (NPS) have completed mapping the moderate-depth marine environment south of St. John. This work is an expansion of ongoing mapping and monitoring efforts conducted by NOAA and NPS in the U.S. Caribbean. The standardized protocols used in this effort will enable scientists and managers to quantitatively compare moderate-depth coral reef ecosystems around St. John to those throughout the U.S. Territories. These protocols and products will also help support the effective management and conservation of the marine resources within the National Park system
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