基于图像内容的水下目标识别技术研究
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摘要
本文是结合国防“十五”预研项目——军用智能水下机器人中“水下目标声探测与识别技术”的工作而进行的。内容涉及声图像的预处理、纹理和形状特征的提取，以及分类器的设计等。本文在解决王程问题的同时，对基于声图像的纹理特征和形状特征的识别方法，进行了深入的研究。具体而言，本文的主要内容包括：
(1)综述了国内外水下目标识别技术的研究现状和发展趋势。将图像数据库检索中广泛使用的“基于内容的图像检索(CBIR)”技术，引入到水下声图像的识别中。
(2)在水下声图像的预处理部分，针对声图像所受噪声干扰大、边缘模糊的缺点，深入研究了形态学滤波的相关理论和方法。设计了一种广义多结构元受控形态滤波器，该滤波器具有平移不变性、递增性、对偶性和幂等性等重要性质。由于该滤波器采用受控算子和多结构元结构对形态学运算的程度进行控制，因此不仅有效地抑制了图像中的噪声，而且较好地保持了图像的几何结构特征。
(3)针对传统直方图只包含灰度的总体信息而没有体现空间关系的不足，提出了一种灰度—空间直方图的识别方法，该直方图在不失传统直方图鲁棒性和简单快捷的前提下，将灰度和空间信息有机地融合起来，从而提高了识别精度。
(4)分析了小波分解的特点，将传统的塔式小波分解和树式小波分解进行了比较，利用小波变换的方法对声图像进行了树式分解。根据小波系数移变的特点，分别采用方差、三阶中心矩、四阶中心矩和熵作为统计量提取了图像的纹理特征，并根据特征本身的离散程度对其进行规范化处理。
(5)对图像处理中几种典型的小波基的选择进行了比较分析。此外，比较了各级分解小波系数在作为纹理特征集进行图像识别时的识别效果，结果
哈尔滨工程大学博士学位论文
表明多级分解的识别率不仅没有显著提高，反而由于特征集过大，造成“特
征灾难”从而使识别率下降。
(6)提出了一种将多重分形理论和小波分析相结合的声图像识别方法。
在特征提取上，运用图像变换和小波分解的思想，对各种图像变换结果进行
估计，得到了基于多重分形维数的特征集。
(7)采用填充算法和Canny算子相结合的方法来提取声图像的轮廓。该
方法较好地消除声图像内部由于回波信号的强弱变化，导致的灰度突变，所
以边缘轮廓较完整，图像内部的孤立点得到了抑制。
(8)分析现有模板匹配算法存在问题的基础上，提出一种基于可变形模
板的声图像识别新方法。该算法在Snake模型的能量最小思想的基础上，对
原有的能量函数重新进行了定义，加入了形状约束，对噪声的敏感程度也相
应下降，从而提高了算法的鲁棒性和识别效率。利用免疫算法求取了能量函
数的最优解。
(9)设计了一种模糊分类器，由于模糊判决分类器通过引入隶属度函数
对特征进行模糊化，反映了各类样本间由于随机噪声等畸变因素造成的抽取
特征值存在的不确定性，从而提高了分类器的鲁棒性。对BP神经网络分类器
设计和应用中的一些问题和技巧，进行了归纳总结
(10)采用由“粗”到“精”的设计思想，构建了基于图像内容的水下
目标识别系统。
This paper carries out with the tenth five-year national defense study-in-advance project named "underwater target acoustic detection and recognition" , which is part of the military intelligent underwater vehicle. The contents in the paper include acoustic image preprocessing, feature extraction of texture and shape, and classifier design. While solving the engineering problems, the paper makes a deep study on the recognition methods based on texture and shape features of acoustic images. The major contents are as follows in general:
(1) The paper gives a survey of the internal and external current status of research and progress trends on underwater target recognition. The CBIR(Content-based image retrieval)technique, which is widely used in digital image database retrieval, is brought into the underwater acoustic image recognition.
(2) To solve the disadvantages of heavy noisy disturbance and edge blurring, we thoroughly do research on relative theories and methods of morphological filtering in the pre-processing part of acoustic images. The generalized and regulated morphological filter is constructed by using multiple structuring elements. The filter possesses some important properties such as translation invariance, increasing, duality and idempotence. As the regulated operator and multiple structuring elements are used to control the morphological operation in the filter, it can not only efficiently suppress noise in images but also preserve the geometrical features of images.
(3)The demerit of gray histograms is they only records the overall
gray compositions of images and no any spatial information is included. To solve that, gray-spatial histograms are proposed, which incorporate spatial information with gray compositions without sacrificing the robustness and simplicity of traditional gray histograms.
(4) We analyze the character of wavelet decomposition and compare the pyramid-structured wavelet decomposition with the tree-structured wavelet decomposition. In accordance to the difference between the two, the tree-structured decomposition of acoustic images has been done in the wavelet domain. Since the wavelet coefficients are shift variant, they are not suitable for direct use as texture features, which must be shift-invariant. Variance, the third moment, the fourth moment and entropy are used as texture features of images. According to the degree of dispersion, the features are normalized.
(5) Comparison and analysis have been done in the selection of several typical wavelet bases which are often used in image processing. In addition, the effectiveness of texture feature sets that are made of wavelet coefficients of every level is compared. It shows that the recognition rate is not improved magnificently by using multi-level decomposition. On the contrary, the recognition rate reduces because the feature sets contain too many features and result in feature disasters.
( 6 ) The recognition method of acoustic image, which takes advantages of both multi-fractal theory and wavelet analysis, is presented . In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is assembled.
(7) The filled-in algorithm and Canny operator are incorporated to extract the contours of acoustic images. This method can eliminate gray burst caused by uneven echo signals. As a result, it can get integral contours and get rid of isolated points.
(8) On the basis of deformable template matching, a new approach based on the deformable template is presented. Compared with the energy minimization of the Snake model, the energy function is redefined by adding a shape restriction. This improves the noise-resistance ability so that robustness and high recognizing rate are acquired. The energy minimization problem is tackled using the Immune Algorithm.
(9) By introducing membership function of the features, the fuzzy classifier is designed and it reflects the uncertainty of the feature values made by random noise. This increases the robus
(1)综述了国内外水下目标识别技术的研究现状和发展趋势。将图像数据库检索中广泛使用的“基于内容的图像检索(CBIR)”技术，引入到水下声图像的识别中。
(2)在水下声图像的预处理部分，针对声图像所受噪声干扰大、边缘模糊的缺点，深入研究了形态学滤波的相关理论和方法。设计了一种广义多结构元受控形态滤波器，该滤波器具有平移不变性、递增性、对偶性和幂等性等重要性质。由于该滤波器采用受控算子和多结构元结构对形态学运算的程度进行控制，因此不仅有效地抑制了图像中的噪声，而且较好地保持了图像的几何结构特征。
(3)针对传统直方图只包含灰度的总体信息而没有体现空间关系的不足，提出了一种灰度—空间直方图的识别方法，该直方图在不失传统直方图鲁棒性和简单快捷的前提下，将灰度和空间信息有机地融合起来，从而提高了识别精度。
(4)分析了小波分解的特点，将传统的塔式小波分解和树式小波分解进行了比较，利用小波变换的方法对声图像进行了树式分解。根据小波系数移变的特点，分别采用方差、三阶中心矩、四阶中心矩和熵作为统计量提取了图像的纹理特征，并根据特征本身的离散程度对其进行规范化处理。
(5)对图像处理中几种典型的小波基的选择进行了比较分析。此外，比较了各级分解小波系数在作为纹理特征集进行图像识别时的识别效果，结果
哈尔滨工程大学博士学位论文
表明多级分解的识别率不仅没有显著提高，反而由于特征集过大，造成“特
征灾难”从而使识别率下降。
(6)提出了一种将多重分形理论和小波分析相结合的声图像识别方法。
在特征提取上，运用图像变换和小波分解的思想，对各种图像变换结果进行
估计，得到了基于多重分形维数的特征集。
(7)采用填充算法和Canny算子相结合的方法来提取声图像的轮廓。该
方法较好地消除声图像内部由于回波信号的强弱变化，导致的灰度突变，所
以边缘轮廓较完整，图像内部的孤立点得到了抑制。
(8)分析现有模板匹配算法存在问题的基础上，提出一种基于可变形模
板的声图像识别新方法。该算法在Snake模型的能量最小思想的基础上，对
原有的能量函数重新进行了定义，加入了形状约束，对噪声的敏感程度也相
应下降，从而提高了算法的鲁棒性和识别效率。利用免疫算法求取了能量函
数的最优解。
(9)设计了一种模糊分类器，由于模糊判决分类器通过引入隶属度函数
对特征进行模糊化，反映了各类样本间由于随机噪声等畸变因素造成的抽取
特征值存在的不确定性，从而提高了分类器的鲁棒性。对BP神经网络分类器
设计和应用中的一些问题和技巧，进行了归纳总结
(10)采用由“粗”到“精”的设计思想，构建了基于图像内容的水下
目标识别系统。
This paper carries out with the tenth five-year national defense study-in-advance project named "underwater target acoustic detection and recognition" , which is part of the military intelligent underwater vehicle. The contents in the paper include acoustic image preprocessing, feature extraction of texture and shape, and classifier design. While solving the engineering problems, the paper makes a deep study on the recognition methods based on texture and shape features of acoustic images. The major contents are as follows in general:
(1) The paper gives a survey of the internal and external current status of research and progress trends on underwater target recognition. The CBIR(Content-based image retrieval)technique, which is widely used in digital image database retrieval, is brought into the underwater acoustic image recognition.
(2) To solve the disadvantages of heavy noisy disturbance and edge blurring, we thoroughly do research on relative theories and methods of morphological filtering in the pre-processing part of acoustic images. The generalized and regulated morphological filter is constructed by using multiple structuring elements. The filter possesses some important properties such as translation invariance, increasing, duality and idempotence. As the regulated operator and multiple structuring elements are used to control the morphological operation in the filter, it can not only efficiently suppress noise in images but also preserve the geometrical features of images.
(3)The demerit of gray histograms is they only records the overall
gray compositions of images and no any spatial information is included. To solve that, gray-spatial histograms are proposed, which incorporate spatial information with gray compositions without sacrificing the robustness and simplicity of traditional gray histograms.
(4) We analyze the character of wavelet decomposition and compare the pyramid-structured wavelet decomposition with the tree-structured wavelet decomposition. In accordance to the difference between the two, the tree-structured decomposition of acoustic images has been done in the wavelet domain. Since the wavelet coefficients are shift variant, they are not suitable for direct use as texture features, which must be shift-invariant. Variance, the third moment, the fourth moment and entropy are used as texture features of images. According to the degree of dispersion, the features are normalized.
(5) Comparison and analysis have been done in the selection of several typical wavelet bases which are often used in image processing. In addition, the effectiveness of texture feature sets that are made of wavelet coefficients of every level is compared. It shows that the recognition rate is not improved magnificently by using multi-level decomposition. On the contrary, the recognition rate reduces because the feature sets contain too many features and result in feature disasters.
( 6 ) The recognition method of acoustic image, which takes advantages of both multi-fractal theory and wavelet analysis, is presented . In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is assembled.
(7) The filled-in algorithm and Canny operator are incorporated to extract the contours of acoustic images. This method can eliminate gray burst caused by uneven echo signals. As a result, it can get integral contours and get rid of isolated points.
(8) On the basis of deformable template matching, a new approach based on the deformable template is presented. Compared with the energy minimization of the Snake model, the energy function is redefined by adding a shape restriction. This improves the noise-resistance ability so that robustness and high recognizing rate are acquired. The energy minimization problem is tackled using the Immune Algorithm.
(9) By introducing membership function of the features, the fuzzy classifier is designed and it reflects the uncertainty of the feature values made by random noise. This increases the robus
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