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The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Roee Diamant - One of the best experts on this subject based on the ideXlab platform.

  • Enhanced Fuzzy-Based Local Information Algorithm for Sonar Image Segmentation
    IEEE Transactions on Image Processing, 2019
    Co-Authors: Roee Diamant

    The recent boost in undersea operations has led to the development of high-resolution Sonar systems mounted on autonomous vehicles. These vehicles are used to scan the seafloor in search of different objects such as sunken ships, archaeological sites, and submerged mines. An important part of the detection operation is the segmentation of Sonar images, where the object’s highlight and shadow are distinguished from the seabed background. In this paper, we focus on the automatic segmentation of Sonar images. We present our enhanced fuzzy-based with Kernel metric (EnFK) algorithm for the segmentation of Sonar images which, in an attempt to improve segmentation accuracy, introduces two new fuzzy terms of local spatial and statistical information. Our algorithm includes a preliminary de-noising algorithm which, together with the original image, feeds into the segmentation procedure to avoid trapping to local minima and to improve convergence. The result is a segmentation procedure that specifically suits the intensity inhomogeneity and the complex seabed texture of Sonar images. We tested our approach using simulated images, real Sonar images, and Sonar images that were created in two different sea experiments, using multibeam Sonar and synthetic aperture Sonar. The results show accurate segmentation performance that is far beyond the state-of-the-art results.

Xiufen Ye - One of the best experts on this subject based on the ideXlab platform.

  • A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex
    Remote Sensing, 2019
    Co-Authors: Xiufen Ye, Haibo Yang, Chuanlong Li, Peng Li

    When side-scan Sonars collect data, Sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and Sonar altitude variations occur, which lead to an uneven gray level in side-scan Sonar images. Therefore, gray scale correction is needed before further processing of side-scan Sonar images. In this paper, we introduce the causes of gray distortion in side-scan Sonar images and the commonly used optical and side-scan Sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan Sonar images based on Retinex given the characteristics of side-scan Sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan Sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

  • ROBIO - A novel segmentation algorithm for side-scan Sonar imagery with multi-object
    2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2007
    Co-Authors: Xingmei Wang, Xiufen Ye, Huanran Wang, Lin Zhao, Kejun Wang

    Automatic detection of underwater objects using side-scan Sonar imagery is complicated by the variability of objects, noises, and background signatures. In recent years, as the resolution of side-scan Sonar is much higher than before, the Sonar imagery can be generated from Sonar signal for processing. The first step of underwater object detection is to segment the underwater objects from Sonar imagery. In typical Sonar imagery, the object contains two parts: high-light areas (echo) and the shadow behind the object. By analyzing the features of the side- scan Sonar imagery, we propose a novel segmentation algorithm for multi-object side-scan Sonar imagery. First we utilize a self- adaptive window to scan the imagery and calculate the variance of the window to segment the high-light areas in Sonar imagery. Then the shadows of the objects are segmented by fractal dimension. At last, the final segmentation results are achieved by combining the results from the above two steps for further analysis. This segmentation algorithm is based on analyzing the structure of objects in Sonar imagery and works well in the multi- object Sonar imagery.

Huuthu Nguyen - One of the best experts on this subject based on the ideXlab platform.

  • study on the classification performance of underwater Sonar image classification based on convolutional neural networks for detecting a submerged human body
    Sensors, 2019
    Co-Authors: Huuthu Nguyen

    Auto-detecting a submerged human body underwater is very challenging with the absolute necessity to a diver or a submersible. For the vision sensor, the water turbidity and limited light condition make it difficult to take clear images. For this reason, Sonar sensors are mainly utilized in water. However, even though a Sonar sensor can give a plausible underwater image within this limitation, the Sonar image’s quality varies greatly depending on the background of the target. The readability of the Sonar image is very different according to the target distance from the underwater floor or the incidence angle of the Sonar sensor to the floor. The target background must be very considerable because it causes scattered and polarization noise in the Sonar image. To successfully classify the Sonar image with these noises, we adopted a Convolutional Neural Network (CNN) such as AlexNet and GoogleNet. In preparing the training data for this model, the data augmentation on scattering and polarization were implemented to improve the classification accuracy from the original Sonar image. It could be practical to classify Sonar images undersea even by training Sonar images only from the simple testbed experiments. Experimental validation was performed using three different datasets of underwater Sonar images from a submerged body of a dummy, resulting in a final average classification accuracy of 91.6% using GoogleNet.

Patrick J. O. Miller - One of the best experts on this subject based on the ideXlab platform.

  • killer whale presence in relation to naval Sonar activity and prey abundance in northern norway
    Ices Journal of Marine Science, 2013
    Co-Authors: Sanna Kuningas, Petter H. Kvadsheim, Patrick J. O. Miller

    In this study, retrospective data on naval Sonar activity and prey abundance were correlated with killer whale sightings within a fjord basin in northern Norway. In addition, passive acoustic and visual marine mammal surveys were conducted before, during, and after a specific navy exercise in 2006. Herring abundance was the main factor affecting killer whale presence. Naval Sonar, either operational navy Sonar exercises (Flotex) or experimental Sonar activity (CEE) alone, did not explain killer whale occurrence. However, naval Sonar activity during a period of low prey availability seemed to have had a negative effect on killer whale presence. We conclude that the level of reaction to Sonar can be influenced by multiple factors, including availability of prey. © 2013 International Council for the Exploration of the Sea.

Ali Allawati - One of the best experts on this subject based on the ideXlab platform.

  • solar radiation estimation using artificial neural networks
    Applied Energy, 2002
    Co-Authors: Atsu S S Dorvlo, Joseph A Jervase, Ali Allawati

    Artificial Neural Network Methods are discussed for estimating solar radiation by first estimating the clearness index. Radial Basis Functions, RBF, and Multilayer Perceptron, MLP, models have been investigated using long-term data from eight stations in Oman. It is shown that both the RBF and MLP models performed well based on the root-mean-square error between the observed and estimated solar radiations. However, the RBF models are preferred since they require less computing power. The RBF model, obtained by training with data from the meteorological stations at Masirah, Salalah, Seeb, Sur, Fahud and Sohar, and testing with those from Buraimi and Marmul, was the best. This model can be used to estimate the solar radiation at any location in Oman.