形状识别与图像分割方法研究
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摘要
计算机视觉是使用计算机对图像结构和抽象层次的内容进行分析并获取所需要的信息,以赋予计算机与人类相仿的视觉功能的技术。形状分析是计算机视觉研究中非常重要的一环,相关技术被广泛应用于工业、医学、交通、军事等各个领域。自二十世纪六十年代以来,形状图像分析领域的研究发展非常迅速,研究人员已提出了许多形状分析方法,并有部分方法在科学研究领域和工程技术领域中发挥了作用,然而形状分析的研究仍然有许多问题等待研究人员的探索。真实世界的三维物体通过映射在二维图像平面上成像时,不可避免的存在信息损失和几何不变性问题。另外形状的扭曲、遮挡、缺损等变化,加上噪声的影响,使形状识别问题的处理更为复杂。为了获得供高层的图像形状分析理解所需的形状数据,必须首先将图像分割为一系列包含相应视觉意义的区域。而形状描述算法所提取的特征向量需要设计良好的模式分类方法来获得理想的识别效果。因此针对该问题的图像分割、形状特征描述、模式分类算法等环节依旧需要进一步的研究。
本研究首先提出了一种称为Radon组合透射特征(Radon Composite Features,RCF)的形状特征描述新算法。区别于传统方法必须进行形状归一化预处理,并在空间域提取形状特征,本方法使用了一种改进的Radon变换对形状进行几何分析,完全避免了归一化预处理所可能导致的配准误差。该算法在Radon变换平面中通过Fourier频谱变换与结构化分析两种手段来提取形状特征信息。在RCF描述序列中,频谱部分刻画整体形态与分布,结构化特征表达形状中的视觉属性,该过程是一种信息无损的一对多(one-to-many)变换,即每一形状点对其上存在的任意方向的直线进行映射,各方向上形状点的组合可获得描述形状结构的重要视觉特征。在变换的整个过程中形状的所有信息被无损的保存下来,通过Radon逆变换可复原原有形状图像也证明了该特性,本文从理论上论证了RCF特征无需经过归一化的步骤就能满足平移、旋转和缩放不变性,并提出了一种由粗到精的层次性描述和检索方法。同时,使用Radon变换等信号处理方法的显著优点是能极大的提取变换平面所蕴含的形状统计与视觉属性,对形状图像背景噪音,以及形状局部形变扭曲等干扰的抵御能力更强。此外RCF方法保持了规模相对较小的参数,这意味着该方法可以尽量减少在程序运行中参数设置的不确定性与随机因素的干扰。与现有的主要方法的实验对比,证明了Radon组合透射特征方法拥有较为精确的描述能力,以及更鲁棒的分类与检索性能。本文还探索了该方法在基于形状的卫星遥感图像检索等领域的应用。
为了获得供描述和识别的形状数据,本文对图像分割算法进行了探索,提出一种称为协进化群落(Artificial Co-evolving Tribes,ACT)的模型用于分割图像。该方法基于人工生命(Artificial Life)理论,将图像视作为封闭的生态环境,初始状态时每一个像素点放置一个生命个体(Agent),在协进化的过程中个体之间设置相互作用,并依据所定义的协作用权重和状态指示器来控制演化过程,个体通过位置移动和自身图像属性的改变来适应图像结构。该协进化过程所体现出的整体特征将逐步涌现并趋于稳定,并最终形成的群落所生存的区域即是所求的图像分割块。与传统方法不同,该模型无需预先确定分割块数与全局阈值,或建立目标函数用以优化,在协进化群落模型中,所有个体之间的进化行为依循模型规则完全自治,迭代过程中同时整合了区域和边界的信息。按照本研究所证明的两个重要性质:类内差异收缩和能量守恒性,图像相似特征的区域逐步融合,保证了分割过程的稳定性。本文将该方法应用于自然场景图像的分割,并与相关算法进行了性能比较与分析,并探讨了与人工分割的匹配程度,证明了其良好的通用性和可靠性。
为了对所提取特征进行更好的后续分类与识别,本研究还提出了一种局部概率中心(Local Probabilistic Centers,LPC)的改进的κ-近邻(Nearest Neighbor)分类算法。当类样本的分布发生交叠时,理论决策边界错误一侧的训练样本将对分类准确率带来较大影响,同时这些噪声样本使分类器过拟合训练集,从而导致泛化性能下降。局部概率中心方法通过计算样本类概率并对每个样本进行加权,使计算所得的局部中心将该类中心偏移,从而减少集合之间的重叠程度,从而具备更强的抗噪声能力,提高了κ-近邻的分类效果。在此框架基础上本文也研究了两种分类度量尺度:利用查询点到计算所得的各类概率中心的欧式距离,并根据计算所得的查询点的后验概率作为分类依据。论文从理论上分析了算法的期望风险和稳定性,通过UCI数据集上的一系列实验,并与相关方法的对比,验证了该方法良好的分类正确率、简单而可靠的参数设置、对特征维数的鲁棒性等。
Shape is recognized as one of the most fundamental feature used to describe image content.Shape classification and retrieval plays a key role in computer vision and image analysis.Related methods have been broadly applied to target detection,image retrieval, and object recognition.However,to extract and describe shape from an image is still a difficult task.This is because when a 3D real-world object is projected onto a 2D image plane,one dimension of object information is unavoidably lost.To make the problem even more complex,shape is often corrupted with noise,defects,occlusion and arbitrary distortion.Therefore,developing more effective technologies to deal with this problem remains in high demand.
This paper first proposes a novel feature-based invariant descriptor called Radon Composite Features(RCF) to represent and identify shapes.Instead of normalization and direct analysis in the spatial domain,this algorithm uses Radon transform to parameterize the generalized morphological properties of groups of shapes.A modified Radon transform is proposed in order to make the transform matrix invariant to the scaling of the shape.To extract the shape information encoded in the Radon transformation plane, both spectral and structural means are proposed.Fourier coefficients and three structural features which have strong descriptive abilities are extracted.The proposed method overcomes the drawbacks of existing shape representation techniques since it accomplishes the invariants to common geometrical transformations without any normalization process, which usually causes inaccuracies.A novel hierarchical strategy with RCF can achieve low complexity and coarse-to-fine retrieval,and perform accurately when retrieving shapes, while remaining robustness under variations.Experiments demonstrate that compared with some state-of-the-art approaches,RCF provides a higher degree of discrimination. The proposed method has also been successfully applied to SAR image classification.
To extract an object and acquire its shape from a give image,an image segmentation stage is necessary beforehand.Deriving from the Artificial Life(Alife) theory,this paper then proposes an Artificial Co-evolving Tribes(ACT) model and applied it to solve the image segmentation problem.In this model a given image can be seen as a living environment. Unit that initially resides on each pixel of the image is considered as a living individual.There is interaction among them during the co-evolving process.Each agent intends to find its congeners according to both spatial and feature distances,immigrate to the area with suit best for it,and alter itself according to the local environment.The individuals in this model making up the tribes effect communication cooperatively from one agent to the other in order to increase the homogeneity of the ensemble of the im age regions they represent.Two remarkable properties,that is,the monotone contraction and the conservation of the system are proved.Stability and scale control of the proposed method are carefully analyzed.Experimental results are presented and compared with two related segmentation methods,both quantitatively and visually.This paper also includes the discussions of the results matching with human visual perception.Being viewed as a novel application for self-organized complex systems,the proposed approach breaks a new path to the treasure trove for image segmentation.
Pattern classification is the stage after the features are extracted from shapes.Chosen the proper classification method usually can obtain better recognition results.In this paper,a novel classification algorithm using Local Probabilistic Centers(LPC) is proposed.This method works through reducing the error-prone samples and restricting their influence regions.Especially when the data distributions are overlapping,or when the samples are deeply polluted by noise,traditional k-th Nearest Neighbor(k-NN) algorithm will be severely influenced and generate poor results.Local Probabilistic Centers(LPC) approach employs some prototype-based technology to reduce the outliers and shrink their influence regions.Two different measuring methods are also proposed, one is the distance between query and local probabilistic centers,and the other is the computed posterior probability difference of query and the nearest categorical center.Although both measures are effective,related experiments show the second one achieves the smaller classification error.This paper also investigates the expectation risk and stability of the proposed method.A set of experiments are conducted on both constructed datasets and real world datasets.The classification results are carefully evaluated and compared with some related methods,which demonstrate that LPC successfully avoids the drawbacks of the traditional k-th Nearest Neighbor algorithm and substantially improves the classification performance.
本研究首先提出了一种称为Radon组合透射特征(Radon Composite Features,RCF)的形状特征描述新算法。区别于传统方法必须进行形状归一化预处理,并在空间域提取形状特征,本方法使用了一种改进的Radon变换对形状进行几何分析,完全避免了归一化预处理所可能导致的配准误差。该算法在Radon变换平面中通过Fourier频谱变换与结构化分析两种手段来提取形状特征信息。在RCF描述序列中,频谱部分刻画整体形态与分布,结构化特征表达形状中的视觉属性,该过程是一种信息无损的一对多(one-to-many)变换,即每一形状点对其上存在的任意方向的直线进行映射,各方向上形状点的组合可获得描述形状结构的重要视觉特征。在变换的整个过程中形状的所有信息被无损的保存下来,通过Radon逆变换可复原原有形状图像也证明了该特性,本文从理论上论证了RCF特征无需经过归一化的步骤就能满足平移、旋转和缩放不变性,并提出了一种由粗到精的层次性描述和检索方法。同时,使用Radon变换等信号处理方法的显著优点是能极大的提取变换平面所蕴含的形状统计与视觉属性,对形状图像背景噪音,以及形状局部形变扭曲等干扰的抵御能力更强。此外RCF方法保持了规模相对较小的参数,这意味着该方法可以尽量减少在程序运行中参数设置的不确定性与随机因素的干扰。与现有的主要方法的实验对比,证明了Radon组合透射特征方法拥有较为精确的描述能力,以及更鲁棒的分类与检索性能。本文还探索了该方法在基于形状的卫星遥感图像检索等领域的应用。
为了获得供描述和识别的形状数据,本文对图像分割算法进行了探索,提出一种称为协进化群落(Artificial Co-evolving Tribes,ACT)的模型用于分割图像。该方法基于人工生命(Artificial Life)理论,将图像视作为封闭的生态环境,初始状态时每一个像素点放置一个生命个体(Agent),在协进化的过程中个体之间设置相互作用,并依据所定义的协作用权重和状态指示器来控制演化过程,个体通过位置移动和自身图像属性的改变来适应图像结构。该协进化过程所体现出的整体特征将逐步涌现并趋于稳定,并最终形成的群落所生存的区域即是所求的图像分割块。与传统方法不同,该模型无需预先确定分割块数与全局阈值,或建立目标函数用以优化,在协进化群落模型中,所有个体之间的进化行为依循模型规则完全自治,迭代过程中同时整合了区域和边界的信息。按照本研究所证明的两个重要性质:类内差异收缩和能量守恒性,图像相似特征的区域逐步融合,保证了分割过程的稳定性。本文将该方法应用于自然场景图像的分割,并与相关算法进行了性能比较与分析,并探讨了与人工分割的匹配程度,证明了其良好的通用性和可靠性。
为了对所提取特征进行更好的后续分类与识别,本研究还提出了一种局部概率中心(Local Probabilistic Centers,LPC)的改进的κ-近邻(Nearest Neighbor)分类算法。当类样本的分布发生交叠时,理论决策边界错误一侧的训练样本将对分类准确率带来较大影响,同时这些噪声样本使分类器过拟合训练集,从而导致泛化性能下降。局部概率中心方法通过计算样本类概率并对每个样本进行加权,使计算所得的局部中心将该类中心偏移,从而减少集合之间的重叠程度,从而具备更强的抗噪声能力,提高了κ-近邻的分类效果。在此框架基础上本文也研究了两种分类度量尺度:利用查询点到计算所得的各类概率中心的欧式距离,并根据计算所得的查询点的后验概率作为分类依据。论文从理论上分析了算法的期望风险和稳定性,通过UCI数据集上的一系列实验,并与相关方法的对比,验证了该方法良好的分类正确率、简单而可靠的参数设置、对特征维数的鲁棒性等。
Shape is recognized as one of the most fundamental feature used to describe image content.Shape classification and retrieval plays a key role in computer vision and image analysis.Related methods have been broadly applied to target detection,image retrieval, and object recognition.However,to extract and describe shape from an image is still a difficult task.This is because when a 3D real-world object is projected onto a 2D image plane,one dimension of object information is unavoidably lost.To make the problem even more complex,shape is often corrupted with noise,defects,occlusion and arbitrary distortion.Therefore,developing more effective technologies to deal with this problem remains in high demand.
This paper first proposes a novel feature-based invariant descriptor called Radon Composite Features(RCF) to represent and identify shapes.Instead of normalization and direct analysis in the spatial domain,this algorithm uses Radon transform to parameterize the generalized morphological properties of groups of shapes.A modified Radon transform is proposed in order to make the transform matrix invariant to the scaling of the shape.To extract the shape information encoded in the Radon transformation plane, both spectral and structural means are proposed.Fourier coefficients and three structural features which have strong descriptive abilities are extracted.The proposed method overcomes the drawbacks of existing shape representation techniques since it accomplishes the invariants to common geometrical transformations without any normalization process, which usually causes inaccuracies.A novel hierarchical strategy with RCF can achieve low complexity and coarse-to-fine retrieval,and perform accurately when retrieving shapes, while remaining robustness under variations.Experiments demonstrate that compared with some state-of-the-art approaches,RCF provides a higher degree of discrimination. The proposed method has also been successfully applied to SAR image classification.
To extract an object and acquire its shape from a give image,an image segmentation stage is necessary beforehand.Deriving from the Artificial Life(Alife) theory,this paper then proposes an Artificial Co-evolving Tribes(ACT) model and applied it to solve the image segmentation problem.In this model a given image can be seen as a living environment. Unit that initially resides on each pixel of the image is considered as a living individual.There is interaction among them during the co-evolving process.Each agent intends to find its congeners according to both spatial and feature distances,immigrate to the area with suit best for it,and alter itself according to the local environment.The individuals in this model making up the tribes effect communication cooperatively from one agent to the other in order to increase the homogeneity of the ensemble of the im age regions they represent.Two remarkable properties,that is,the monotone contraction and the conservation of the system are proved.Stability and scale control of the proposed method are carefully analyzed.Experimental results are presented and compared with two related segmentation methods,both quantitatively and visually.This paper also includes the discussions of the results matching with human visual perception.Being viewed as a novel application for self-organized complex systems,the proposed approach breaks a new path to the treasure trove for image segmentation.
Pattern classification is the stage after the features are extracted from shapes.Chosen the proper classification method usually can obtain better recognition results.In this paper,a novel classification algorithm using Local Probabilistic Centers(LPC) is proposed.This method works through reducing the error-prone samples and restricting their influence regions.Especially when the data distributions are overlapping,or when the samples are deeply polluted by noise,traditional k-th Nearest Neighbor(k-NN) algorithm will be severely influenced and generate poor results.Local Probabilistic Centers(LPC) approach employs some prototype-based technology to reduce the outliers and shrink their influence regions.Two different measuring methods are also proposed, one is the distance between query and local probabilistic centers,and the other is the computed posterior probability difference of query and the nearest categorical center.Although both measures are effective,related experiments show the second one achieves the smaller classification error.This paper also investigates the expectation risk and stability of the proposed method.A set of experiments are conducted on both constructed datasets and real world datasets.The classification results are carefully evaluated and compared with some related methods,which demonstrate that LPC successfully avoids the drawbacks of the traditional k-th Nearest Neighbor algorithm and substantially improves the classification performance.
引文
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