形状分析新方法及其在图像检索中的应用研究
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摘要
形状描述与分析技术是模式识别、计算机视觉和图像理解领域的研究热点之一，已在目标识别、图像检索、图像配准、产品检测、生物医学工程等多个科学研究和工程技术领域得到广泛应用。本文的主要工作紧紧围绕形状描述与分析方法这一核心问题而展开，在对现有的形状分析方法进行深入研究的基础上，提出了若干形状描述与分析新方法，并将它们应用于形状图像检索。本文的主要工作和贡献包括如下几个方面：
（1）首先简要介绍了若干形状紧凑性描述方法，再从基于轮廓和基于区域两条线索介绍了近年来提出的一些典型形状描述与分析方法，详细介绍了这些方法的构建过程，分析了它们的优缺点，并指出了可能的改进方向。
（2）用目标轮廓上点的相对位置分布关系对其形状进行描述，基于统计的思想，提出了一种极坐标下形状轮廓特征描述符——轮廓点分布直方图，该特征描述符不仅符合人眼的视觉感受、计算简单；而且其本质上具有缩放和平移不变性。然后用动态规划算法来度量轮廓点分布直方图之间的距离，部分地解决了轮廓点分布直方图对于旋转不变性的要求。又将轮廓点分布直方图之间的距离度量问题看作为运筹学中的运输问题，并依据轮廓点分布直方图的特性提出了一种简单而有效的地面距离计算方法，并采用旋转匹配和镜像匹配相结合的方式最终确定轮廓点分布直方图之间的距离。在多个通用形状图像数据库中的实验结果表明，所提算法在单目标封闭轮廓的形状图像检索中取得了良好的效果。
（3）基于矩不变量的形状区域描述方法在模式识别和计算机视觉中有着重要的作用。根据人类视觉往往更依赖于目标的轮廓部分来区分不同的形状这一特性（即形状的轮廓部分在人们识别该形状时所起的作用更大），提出了一种中心矩加权方法。该加权方法特点在于：计算图像的中心矩时，根据处于不同位置的每个像素距离形状轮廓边界的远近程度分配不同的权值，距离边界较近的像素分配较高的权值，反之，距离边界较远的像素则分配较小的权值。以加权中心矩为基础构建7个Hu矩不变量作为图像特征，重构的7个Hu矩不变量特征仍具有平移、缩放、旋转不变性。在多个通用形状图像数据库中的实验结果表明，基于改进后的Hu矩不变量的检索性能要明显优于与基于传统的Hu矩不变量方法。
（4）提出了一种新的基于最小外接圆补偿机制的二值形状图像检索算法，该方法不仅提取目标区域的形状特征，还提取目标最小外接圆内的背景区域的形状特征。提取常用于描述区域特征的Hu不变矩和Zernike不变矩作为图像特征，图像相似度用归一化特征向量的欧氏距离表示。该方法不仅计算简单，而且有效补偿人眼的视觉感受，通过大量实验表明，该方法较仅基于目标区域特征的检索算法取得了更好的检索精度和回召率。
（5）形状签名和傅里叶描述子是常用的形状描述方法，广泛应用于模式识别和计算机视觉应用领域。本文提出了一种新的形状签名技术——多尺度轮廓弹性形状签名，然后对其进行离散傅里叶变换从而得到基于多尺度轮廓弹性的傅里叶描述子。基于轮廓弹性的傅里叶描述子既是一种轮廓线函数，描述了二维形状轮廓的整体形变特征，同时又反映了轮廓采样点的局部形变特征，融合了形状的全局与局部特征。多尺度技术巧妙地解决了轮廓弹性参数选择这一难题，并且提供了一种由粗到细的形状描述方法，且计算简单。在多个通用形状图像数据库中的实验结果表明，与其他典型的基于形状签名的傅里叶描述子相比，基于多尺度轮廓弹性的傅里叶描述子在形状图像检索中取得了最佳效果。
Shape description and analysis techniques are the hot research topics in patternrecognition, computer vision and image understanding which have been widely applied inmany fields of scientific research and engineering technology, such as object recognition,image retrieval, image registration, product detection, biomedical engineering and etc. Themain work in this thesis is closely around the core problem of shape description and analysis.Based on the further research of the existing shape analysis techniques, several novel shapedescription and analysis approaches are proposed, and which are used for shape imageretrieval applications. The main work and contributions in this thesis include the followingaspects:
(1) First, several shape compactness description methods are briefly introduced and thensome typical shape description and analysis methods proposed in recent years are presented.The construction processes of these methods are described in detail, their advantages anddisadvantages are discussed and also the possible improvement directions are looked forward.
(2) Second, using the relative distribution of points’ position of the contour to describe ashape, a novel shape contour descriptor for shape description is proposed, named contourpoints distribution histogram (CPDH), under polar coordinate according to the thinking ofstatistics. This descriptor is not only satisfactory to the human’s visual feelings but also isvery simple to be calculated. And it essentially has the properties of invariant to scaling andtranslation. The dynamic programming algorithm is suggested to measure the distancebetween CPDHs, and that the dynamic programming algorithm can partly fulfill the needs ofthe CPDH’s invariant to rotation. The problem of the distance measurement between CPDHsis treated as the transportation problem in the operational research. According to thecharacteristics of CPDH an effective ground distance calculation method is suggested and thefinal distance between two CPDHs is obtained through the combination of the shift matchingand mirror matching in the process of distance easement. By a great deal experiments inseveral common shape databases, it is shown that the proposed algorithms, used in imageretrieval of shape with a single closed contour, can get favorable results.
(3) Third, the shape region descriptors based on the moment invariants play an importantrole in pattern recognition and computer vision. According to the visual characteristic thathuman tend to discriminate different shapes more dependent on the contour parts of the object(i.e. the contour parts play the major role when people identifying different shapes), a kind ofcentral moment weighted method is put forward. This weighting method has the followingcharacteristics. When computing the image’s moment invariants, according to the differentdistance between the points and contour different weights are assigned to the points indifferent position. If the point in the shape is close to the boundary then it will has the biggerweight. And on the other hand, if the point in the shape is far away from the boundary then itwill has the smaller weight. The seven Hu moment invariants reconstructed based on theweighted central moment are selected as the image features. The reconstructed seven Hu moment invariants are still have properties of invariance to translation, scaling and rotation.By a great deal experiments in several common shape databases, it is shown that retrievalperformance based on the improved Hu moment invariants is much better than that based onthe traditional Hu moment invariants.
(4) Fourth, A novel shape image retrieval algorithms based on compensation mechanismunder minimum circumscribed circle is proposed, which not only extracts the features of theobject area, and extracts the features of the background within the minimum circumscribedcircle area. Extracting the Hu moment invariants and Zernike moment invariants, commonlyused as features for describing the region, as image features. Similarity between two images isgiven by the Euclidean distance of the normalized moment invariants vectors. This method isnot only implemented simply, but compensates the human eye's visual perception effectively.By a large number of experiments, it is shown that this approach can achieve better retrievalaccuracy and recall rates than that of an object area based only.
(5) Fifth, Shape signature and Fourier descriptors are common techniques for shapedescription and they are widely used in pattern recognition and computer vision applications.A novel shape signature is proposed in this thesis, namely, multiscale contour flexibility shapesignature. After the discrete Fourier transform is performed on the multiscale contourflexibility shape signature, the Fourier descriptors are obtained. Contour flexibility basedFourier descriptor is a contour line function, which not only describes the whole deformationcharacteristics of the two dimensional shape profiles, but also reflects the local deformationcharacteristics of the contour sampling points. And it incorporates the global and localfeatures of the shape. Multiscale technique has solved the problem of elastic parameterselection and describes the shape features from coarse to fine. It is also easy to be calculated.By a great deal experiments in several common shape databases, it is shown that the bestretrieval results are achieved by the multiscale contour flexibility based Fourier descriptorcompared with other typical shape signatures based Fourier descriptors.
（1）首先简要介绍了若干形状紧凑性描述方法，再从基于轮廓和基于区域两条线索介绍了近年来提出的一些典型形状描述与分析方法，详细介绍了这些方法的构建过程，分析了它们的优缺点，并指出了可能的改进方向。
（2）用目标轮廓上点的相对位置分布关系对其形状进行描述，基于统计的思想，提出了一种极坐标下形状轮廓特征描述符——轮廓点分布直方图，该特征描述符不仅符合人眼的视觉感受、计算简单；而且其本质上具有缩放和平移不变性。然后用动态规划算法来度量轮廓点分布直方图之间的距离，部分地解决了轮廓点分布直方图对于旋转不变性的要求。又将轮廓点分布直方图之间的距离度量问题看作为运筹学中的运输问题，并依据轮廓点分布直方图的特性提出了一种简单而有效的地面距离计算方法，并采用旋转匹配和镜像匹配相结合的方式最终确定轮廓点分布直方图之间的距离。在多个通用形状图像数据库中的实验结果表明，所提算法在单目标封闭轮廓的形状图像检索中取得了良好的效果。
（3）基于矩不变量的形状区域描述方法在模式识别和计算机视觉中有着重要的作用。根据人类视觉往往更依赖于目标的轮廓部分来区分不同的形状这一特性（即形状的轮廓部分在人们识别该形状时所起的作用更大），提出了一种中心矩加权方法。该加权方法特点在于：计算图像的中心矩时，根据处于不同位置的每个像素距离形状轮廓边界的远近程度分配不同的权值，距离边界较近的像素分配较高的权值，反之，距离边界较远的像素则分配较小的权值。以加权中心矩为基础构建7个Hu矩不变量作为图像特征，重构的7个Hu矩不变量特征仍具有平移、缩放、旋转不变性。在多个通用形状图像数据库中的实验结果表明，基于改进后的Hu矩不变量的检索性能要明显优于与基于传统的Hu矩不变量方法。
（4）提出了一种新的基于最小外接圆补偿机制的二值形状图像检索算法，该方法不仅提取目标区域的形状特征，还提取目标最小外接圆内的背景区域的形状特征。提取常用于描述区域特征的Hu不变矩和Zernike不变矩作为图像特征，图像相似度用归一化特征向量的欧氏距离表示。该方法不仅计算简单，而且有效补偿人眼的视觉感受，通过大量实验表明，该方法较仅基于目标区域特征的检索算法取得了更好的检索精度和回召率。
（5）形状签名和傅里叶描述子是常用的形状描述方法，广泛应用于模式识别和计算机视觉应用领域。本文提出了一种新的形状签名技术——多尺度轮廓弹性形状签名，然后对其进行离散傅里叶变换从而得到基于多尺度轮廓弹性的傅里叶描述子。基于轮廓弹性的傅里叶描述子既是一种轮廓线函数，描述了二维形状轮廓的整体形变特征，同时又反映了轮廓采样点的局部形变特征，融合了形状的全局与局部特征。多尺度技术巧妙地解决了轮廓弹性参数选择这一难题，并且提供了一种由粗到细的形状描述方法，且计算简单。在多个通用形状图像数据库中的实验结果表明，与其他典型的基于形状签名的傅里叶描述子相比，基于多尺度轮廓弹性的傅里叶描述子在形状图像检索中取得了最佳效果。
Shape description and analysis techniques are the hot research topics in patternrecognition, computer vision and image understanding which have been widely applied inmany fields of scientific research and engineering technology, such as object recognition,image retrieval, image registration, product detection, biomedical engineering and etc. Themain work in this thesis is closely around the core problem of shape description and analysis.Based on the further research of the existing shape analysis techniques, several novel shapedescription and analysis approaches are proposed, and which are used for shape imageretrieval applications. The main work and contributions in this thesis include the followingaspects:
(1) First, several shape compactness description methods are briefly introduced and thensome typical shape description and analysis methods proposed in recent years are presented.The construction processes of these methods are described in detail, their advantages anddisadvantages are discussed and also the possible improvement directions are looked forward.
(2) Second, using the relative distribution of points’ position of the contour to describe ashape, a novel shape contour descriptor for shape description is proposed, named contourpoints distribution histogram (CPDH), under polar coordinate according to the thinking ofstatistics. This descriptor is not only satisfactory to the human’s visual feelings but also isvery simple to be calculated. And it essentially has the properties of invariant to scaling andtranslation. The dynamic programming algorithm is suggested to measure the distancebetween CPDHs, and that the dynamic programming algorithm can partly fulfill the needs ofthe CPDH’s invariant to rotation. The problem of the distance measurement between CPDHsis treated as the transportation problem in the operational research. According to thecharacteristics of CPDH an effective ground distance calculation method is suggested and thefinal distance between two CPDHs is obtained through the combination of the shift matchingand mirror matching in the process of distance easement. By a great deal experiments inseveral common shape databases, it is shown that the proposed algorithms, used in imageretrieval of shape with a single closed contour, can get favorable results.
(3) Third, the shape region descriptors based on the moment invariants play an importantrole in pattern recognition and computer vision. According to the visual characteristic thathuman tend to discriminate different shapes more dependent on the contour parts of the object(i.e. the contour parts play the major role when people identifying different shapes), a kind ofcentral moment weighted method is put forward. This weighting method has the followingcharacteristics. When computing the image’s moment invariants, according to the differentdistance between the points and contour different weights are assigned to the points indifferent position. If the point in the shape is close to the boundary then it will has the biggerweight. And on the other hand, if the point in the shape is far away from the boundary then itwill has the smaller weight. The seven Hu moment invariants reconstructed based on theweighted central moment are selected as the image features. The reconstructed seven Hu moment invariants are still have properties of invariance to translation, scaling and rotation.By a great deal experiments in several common shape databases, it is shown that retrievalperformance based on the improved Hu moment invariants is much better than that based onthe traditional Hu moment invariants.
(4) Fourth, A novel shape image retrieval algorithms based on compensation mechanismunder minimum circumscribed circle is proposed, which not only extracts the features of theobject area, and extracts the features of the background within the minimum circumscribedcircle area. Extracting the Hu moment invariants and Zernike moment invariants, commonlyused as features for describing the region, as image features. Similarity between two images isgiven by the Euclidean distance of the normalized moment invariants vectors. This method isnot only implemented simply, but compensates the human eye's visual perception effectively.By a large number of experiments, it is shown that this approach can achieve better retrievalaccuracy and recall rates than that of an object area based only.
(5) Fifth, Shape signature and Fourier descriptors are common techniques for shapedescription and they are widely used in pattern recognition and computer vision applications.A novel shape signature is proposed in this thesis, namely, multiscale contour flexibility shapesignature. After the discrete Fourier transform is performed on the multiscale contourflexibility shape signature, the Fourier descriptors are obtained. Contour flexibility basedFourier descriptor is a contour line function, which not only describes the whole deformationcharacteristics of the two dimensional shape profiles, but also reflects the local deformationcharacteristics of the contour sampling points. And it incorporates the global and localfeatures of the shape. Multiscale technique has solved the problem of elastic parameterselection and describes the shape features from coarse to fine. It is also easy to be calculated.By a great deal experiments in several common shape databases, it is shown that the bestretrieval results are achieved by the multiscale contour flexibility based Fourier descriptorcompared with other typical shape signatures based Fourier descriptors.
引文
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