Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images (†).
Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images (†).
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DOI：
10.3390/s17091953
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发表时间：
2017-08-24
期刊：
影响因子：
--
通讯作者：
Choi HT
中科院分区：
文献类型：
--
作者：
Lee Y;Choi J;Ko NY;Choi HT
本文提出了一种基于概率的框架，利用声纳图像识别水下地标。目前的识别方法都是采用单帧图像，由于声纳图像具有声源不稳定、斑点噪声多、分辨率低、单通道等缺点，识别结果不可靠，而采用连续的声纳图像，如果在连续的情况下，通过随机方法连续评估对象的存在和身份（或名称），识别方法的结果可用于计算不确定度，更适合各种应用。我们提出的框架包括三个步骤：（1）候选选择，（2）连续性评估，（3）贝叶斯特征估计。采用粒子滤波和贝叶斯特征估计两种概率方法对连续图像中目标的连续性和特征进行反复估计。因此，通过随机方法重复预测和更新对象的状态。此外，我们开发了一种人工地标，以增加可探测性的成像声纳，我们适用于声波的特性，如不稳定性和反射取决于反射器表面的粗糙度。所提出的方法进行了验证盆地实验，结果。
This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status—i.e., the existence and identity (or name)—of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods—particle filtering and Bayesian feature estimation—are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented.
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影响因子：
4.4
作者：
Chaillan, Fabien;Fraschini, Christophe;Courmontagne, Philippe
通讯作者：
Courmontagne, Philippe
影响因子：
5
作者：
Park, Jin-Yeong;Jun, Bong-huan;Oh, Junho
通讯作者：
Oh, Junho
影响因子：
4.1
作者：
Fallon, Maurice F.;Folkesson, John;Leonard, John J.
通讯作者：
Leonard, John J.
DOI：
10.1049/ip-rsn:20045015
发表时间：
2005-08-01
期刊：
IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION
影响因子：
--
作者：
Kim, K;Neretti, N;Intrator, N
通讯作者：
Intrator, N
影响因子：
2.5
作者：
Kim, Donghoon;Lee, Donghwa;Choi, Hyun-Taek
通讯作者：
Choi, Hyun-Taek