Segmentation of complex objects＇ sonar images using parameter-fixed MRF model

The effective method of the recognition of underwater complex objects in sonar image is to segment sonar image into target, shadow and sea-bottom reverberation regions and then extract the edge of the object. Because of the time-varying and space-varying characters of underwater acoustics environment, the sonar images have poor quality and serious speckle noise, so traditional image segmentation is unable to achieve precise segmentation. In the paper, the image segmentation process based on MRF （Markov random field） model is studied, and a practical method of estimating model parameters is proposed. Through analyzing the impact of chosen model parameters, a sonar imagery segmentation algorithm based on fixed parameters＇ MRF model is proposed. Both of the segmentation effect and the low computing load are gained. By applying the algorithm to the synthesized texture image and actual side-scan sonar image, the algorithm can be achieved with precise segmentation result.     

基于自适应迭代阈值的声呐图像增强算法

水下声呐成像探测技术日益成为各国普遍重视的技术。声呐图像具有对比度低、成像质量差的特点，对声呐图像进行增强处理是进行声呐图像判读和提高视觉效果的基础之一。针对声呐图像增强算法研究较少等问题，提出了一种迭代阈值图像增强算法，并采用评价准则算法和频域响应法对常用的几种图像增强算法进行比较分析。结果表明，该方法具有算法简单、易于实现等特点，且增强效果可较好地满足视觉效果的需求。     

一种隧洞探测型AUV及其图像拼接方法研究

裂缝是输水隧洞最普遍的缺陷和潜在威胁,AUV巡检与拍摄输水隧洞中的裂缝,通过拍摄的水下图像进行处理,以确定裂缝位置。但由于水下环境极大限制了光视觉图像可视范围与分辨率,单幅水下图像所获得的视角范围有限,所以本文提出了基于SURF算法的AUV水下图像拼接方法研究。该方法首先在图像预处理阶段对水下图像进行去畸变与限制对比度自适应直方图均衡化处理,用于解决水下图像存在的畸变、对比度低、噪声严重等问题。接着将SURF特征点检测算法应用于水下图像配准当中,并与RANSAC算法相结合对特征点进行精确匹配,然后应用线性渐变融合算法实现水下图像融合,有效去除拼接缝隙,完成水下图像拼接,最终通过水池与外场实验拼接图像来验证该方法的正确性。     

Application of A* algorithm for real-time path re-planning of an unmanned surface vehicle avoiding underwater obstacles

This paper describes path re-planning techniques andunderwater obstacle avoidance for unmanned surface vehicle （USV）based on multi-beam forward looking sonar （FLS）. Near-optimalpaths in static and dynamic environments with underwaterobstacles are computed using a numerical solution procedure basedon an A algorithm. The USV is modeled with a circular shape in 2degrees of freedom （surge and yaw）. In this paper, two-dimensional（2-D） underwater obstacle avoidance and the robust real-time pathre-planning technique for actual USV using multi-beam FLS aredeveloped. Our real-time path re-planning algorithm has beentested to regenerate the optimal path for several updated frames inthe field of view of the sonar with a proper update frequency of theFLS. The performance of the proposed method was verifiedthrough simulations, and sea experiments. For simulations, theUSV model can avoid both a single stationary obstacle, multiplestationary obstacles and moving obstacles with the near-optimaltrajectory that are performed both in the vehicle and the worldreference frame. For sea experiments, the proposed method for anunderwater obstacle avoidance system is implemented with a USVtest platform. The actual USV is automatically controlled andsucceeded in its real-time avoidance against the stationary underseaobstacle in the field of view of the FLS together with the GlobalPositioning System （GPS） of the USV.     

基于反蛙人声纳的小目标检测算法

随着蛙人侵扰活动的日益频繁,在一些重要的部位如港口、码头、海岸洞库和锚泊水域中都安装了水下监控系统,在这些系统中,基于反蛙人声纳的小目标检测既是研究的重点也是难点之一。文章针对小目标检测问题进行了讨论,提出反蛙人声纳图像处理和小目标检测算法。由实践结果可以得出,此算法具有较高的小目标检测率,有效提高了水下监控系统的可靠性。     

基于统计量的声呐图像目标检测算法

水下声呐图像目标检测问题是一项重要而困难的工作,采用滑动窗计算图像中各像素点处邻域像素灰度的统计量,利用最大熵图像分割算法确定检测阈值,并利用均值、标准差、偏态和峰度等统计量对算法进行了仿真验证,对声呐图像中的目标回波区和阴影区域均可实现较好的检测效果。结果表明,该方法具有原理简单、运算效率高、实时性好等特点,具有较强的工程应用价值。     

Underwater simultaneous localization and mapping based on forward-looking sonar

A method of underwater simultaneous localization and mapping (SLAM) based on forward-looking sonar was proposed in this paper. Positions of objects were obtained by the forward-looking sonar, and an improved association method based on an ant colony algorithm was introduced to estimate the positions.In order to improve the precision of the positions, the extended Kalman filter (EKF) was adopted. The presented algorithm was tested in a tank, and the maximum estimation error of SLAM gained was 0.25 m. The tests verify that this method can maintain better association efficiency and reduce navigation error.     

基于几何特征的声呐图像人造目标检测算法

针对高频声呐图像目标检测问题,基于数学形态学算子,提出了一种新的快速目标检测算法.该算法通过定义多尺度多形状的形态结构元素,对各像素点进行形态开闭运算,并采用阈值评判方法实现目标检测.仿真结果表明,该算法具有实时性好、准确度高的特点,并且可有效地克服背景中岩石等自然景物的影响,实现人造目标的准确提取.     

基于区域模糊直方图的图像检索

将图像分割算法和模糊理论相结合,提出了基于区域模糊直方图的图像检索方法。用快速聚类算法对图像进行区域分块,以各区域的视觉特征为基础,建立图像模糊直方图并进行图像检索。在1000幅通用图像库上的实验表明,该方法可以得到比较满意的检索结果。     

一种面向水下无人航行器的实效图像增强算法

针对水下图像存在严重模糊、对比度低、噪声多等问题,根据小波的时频特性及多分辨率特点,提出一种基于小波变换的水下模糊图像增强算法。利用小波变换对RGB图像进行二层小波分解,把原图分解为高、低频子带,利用导向滤波算法估计低频子带上的照射分量并进行去除;利用软阈值算法对高频子带上的图像边轮廓信息进行去噪和增强处理;对水下图像进行小波逆构,并进行伽玛校正;最后利用改进的灰度世界算法对水下图像进行颜色校正。试验结果表明,使用文章中算法处理所得到图像的对比度及信噪比都较高,且清晰度较高,满足水下无人航行器的要求。     

一种基于稀疏表示声纳图像识别方法

提出了一种基于灰度-梯度共生矩阵的稀疏表示声纳图像识别方法.该方法采用灰度-梯度共生矩阵对声纳图像进行特征提取,特征提取结果相比全局的特征提取包含了声纳图像的重要的纹理信息;并结合稀疏表示的分类方法对声纳图像进行识别.实验表明,该方法既满足了对声纳图像进行识别实时性,又提高了识别的准确性.     

Underwater image bidirectional matching for localization based on SIFT

For the purpose of identifying the stern of the SWATH （Small Waterplane Area Twin Hull） availably and perfecting the detection technique of the SWATH ship＇s performance, this paper presents a novel bidirectional image registration strategy and mosaicing technique based on the scale invariant feature transform （SIFT） algorithm. The proposed method can help us observe the stern with a great visual angle for analyzing the performance of the control fins of the SWATH. SIFT is one of the most effective local features of the scale, rotation and illumination invariant. However, there are a few false match rates in this algorithm. In terms of underwater machine vision, only by acquiring an accurate match rate can we find an underwater robot rapidly and identify the location of the object. Therefore, firstly, the selection of the match ratio principle is put forward in this paper; secondly, some advantages of the bidirectional registration algorithm are concluded by analyzing the characteristics of the unidirectional matching method. Finally, an automatic underwater image splicing method is proposed on the basis of fixed dimension, and then the edge of the image＇s overlapping section is merged by the principal components analysis algorithm. The experimental results achieve a better registration and smooth mosaicing effect, demonstrating that the proposed method is effective.     

贝叶斯假设方法在水声成像声呐阵控制中的应用

在对水中目标轮廓进行水声成像时,可设计声呐阵中各声呐单体对目标分割识别,利用贝叶斯假设方法,解决多声呐单体识别结果的目标最终识别问题,实现声呐阵的有效控制。     

基于尺度不变特征的水下图像双向定位配准(英文)

For the purpose of identifying the stern of the SWATH(Small Waterplane Area Twin Hull) availably and perfecting the detection technique of the SWATH ship's performance, this paper presents a novel bidirectional image registration strategy and mosaicing technique based on the scale invariant feature transform(SIFT) algorithm. The proposed method can help us observe the stern with a great visual angle for analyzing the performance of the control fins of the SWATH. SIFT is one of the most effective local features of the scale, rotation and illumination invariant. However, there are a few false match rates in this algorithm. In terms of underwater machine vision, only by acquiring an accurate match rate can we find an underwater robot rapidly and identify the location of the object. Therefore, firstly, the selection of the match ratio principle is put forward in this paper; secondly, some advantages of the bidirectional registration algorithm are concluded by analyzing the characteristics of the unidirectional matching method. Finally, an automatic underwater image splicing method is proposed on the basis of fixed dimension, and then the edge of the image's overlapping section is merged by the principal components analysis algorithm. The experimental results achieve a better registration and smooth mosaicing effect, demonstrating that the proposed method is effective.     

一种基于PSO优化HWFCM的快速水下图像分割算法

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.     

舰载经纬仪连续帧图像目标快速识别算法研究

为解决舰载经纬仪对连续帧图像快速识别、准确定位的要求,在深入分析连续图像序列特性和舰载经纬仪特点的基础上,利用经纬仪的角度变化信息,对图像序列进行帧间差值运算,提出了一种新的目标识别算法,大大减少了运算量.实验结果证明,该算法具有快速、稳定等优点,能满足舰载经纬仪实时图像处理的要求.     

Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network

1Introduction Sonarself noiseisakeyfactorinfluencingthehiding capabilityofanunderwatervehicleanddetectioncapa bilityofsonar.Decreasingsonarself noisehasimpor tanteffectonenhancingtheprobabilityofsonardetec tionandbattleeffectivenessofunderwatervehicle.An …     

一种基于遗传算法和小波分解的刚性图像配准方法

为了实现快速、高精度的自动图像配准,提出了基于遗传算法和小波分解的图像配准方法。此方法在小波分解的基础上,以互信息为相似性测度利用遗传算法为搜索算法对图像进行一次配准,然后以特定步长在小范围内对配准参数进行修正,对图像进行二次配准。该方法实验结果表明该方法具有较快的速度且显著的改善了配准精度。     

嵌入式零树小波在合成孔径声纳图像压缩中的应用

合成孔径声纳（SAS）图像具有分辨率高、数据量大特点,给数据存储和传输带来困难。由于合成孔径图像像素间具有一定的相关性,采用嵌入式零树小波（EZW）编码算法,利用小波系数的相关特性可以达到压缩图像的目的。文章研究了基于嵌入式零树小波的合成孔径声纳图像的压缩和解码算法,并用合成孔径声纳图像对算法进行了测试,采用峰值信噪比、均方误差、压缩比三项技术指标对图像压缩质量进行了评估。结果验证了基于嵌入式小波零树图像编码算法的合成孔径图像的压缩和解码算法可行性。     

长江口排水板水下成像检测技术

针对长江口深水区域塑料排水板施工检测手段不足的问题,提出水下声学成像检测的新方法,采用高分辨率侧扫声呐和双频识别声呐,实现了水下排水板检测的可视化与可量测。获取水下排水板清晰的影像资料,并且通过双频识别声呐水下定点全环摄像方式能够实现排水板定量检测。新方法弥补了现有检测手段的不足,可作为现有检测手段的重要补充。