基于轮廓特征的目标识别研究
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摘要
目标识别是图像处理、模式识别和计算机视觉领域的研究热点之一,广泛应用于日常生活、工业应用和军事活动中的各个领域。从人类认知事物的角度来看,目标的特征描述和相似性评价是目标识别中两个关键环节。轮廓作为描述目标特征的一种主要方式,在目标识别中发挥着重要作用。因此,如何有效描述轮廓特征以及合理评价特征之间相似程度对于获得目标识别的最佳结果至关重要。
本论文围绕轮廓特征的提取、描述和评价这一主题展开研究,重点关注了轮廓特征在轮廓平滑、轮廓起始点配准和轮廓识别三个方面的应用。对于目标轮廓,现有轮廓平滑算法很难在保留轮廓特征和抑制噪声之间取得平衡,现有轮廓起始点配准算法无法适用于仿射目标或者具有较高的计算复杂度,现有仿射目标识别算法很难满足实时性的要求,现有部分遮挡目标识别算法没有考虑轮廓局部特征与目标轮廓之间的关系及其对目标识别结果的影响。本论文的主要工作和所取得的研究成果具体如下：
1.提出了一个轮廓特征描述框架。在该框架下,从全局特征、局部特征和结构特征三个层面对现有轮廓特征的描述方法进行总结和分类,并且从可信度、计算复杂度和适用范围三个方面分析了各类特征描述方法的特点,为不同应用环境下选取最佳的特征描述方法以及评价准则提供了借鉴。
2.提出了基于分段轮廓平滑的仿射目标识别算法。为了抑制噪声对目标轮廓的干扰从而准确描述轮廓特征,预先对含噪轮廓进行分段平滑处理。利用曲率将目标轮廓划分为特征区域和非特征区域,对两类区域分别采用不同的高斯平滑滤波器进行差异化处理,在保留轮廓特征和抑制噪声之间取得了较好的平衡。在此基础上,利用仿射不变矩和最小距离分类器对仿射变换下含噪轮廓进行识别。仿真实验结果验证了分段轮廓平滑算法改善识别结果的有效性。
3.提出了基于轮廓起始点配准的快速仿射目标识别算法。针对现有轮廓起始点配准算法存在计算代价较高的不足,提出了基于联合仿射不变弧长的轮廓起始点配准算法,实现了起始点的高效配准。在此基础上,利用离散小波变换的尺度系数构造了满足平移不变性的级联仿射不变函数。与同类基于小波变换的识别算法相比,大大降低了算法的整体复杂度。仿真结果表明,所提出的轮廓起始点配准算法和仿射目标识别算法对噪声具有较好的鲁棒性,并且满足了实时性的要求。
4.提出了基于轮廓特征的部分遮挡目标识别算法。从目标的轮廓特征入手,提出了基于局部曲率分布的轮廓划分方案,建立了完整描述轮廓局部特征的轮廓分段数据库。在此基础上,提出了基于轮廓特征完整描述的部分遮挡目标识别算法,采用形状上下文距离衡量轮廓特征之间的相似度,并结合轮廓分段的可信度评价了部分遮挡目标的匹配程度。仿真实验表明,所提出的识别算法所需训练样本相对较少,能够高效、准确地识别不同遮挡情况下的遮挡目标。更进一步,为了增强在不同遮挡情况下描述局部特征的稳定性,结合目标的结构信息对轮廓分段进行分类,得到了轮廓局部特征的多层描述模型。借鉴哲学中“整体与部分之间关系”的概念,论文深入探讨了整体轮廓与轮廓分段之间的关系,给出了评价轮廓分段的两个参数：重要性和局部性。在此基础上,提出了基于轮廓特征多层描述和评价的部分遮挡目标识别算法。为了准确衡量遮挡目标的匹配程度,将两个评价参数与轮廓特征之间的相似度相结合提出了加权部分相似度。与现有相似性评价准则相比,加权部分相似度合理反映了局部特征相似性与整体目标相似性之间的关系,可以获得遮挡目标的最佳匹配结果。仿真实验表明,所提出的识别算法在不同遮挡情况下均能获得稳定的识别结果。
Object recognition is always one of the hot topics in the field of image processing, pattern recognition and computer vision, and it is widely used in every area of the daily lives, industrial applications and military activities. Contour is one of the apporaches to describe the object and plays an important role in object matching and recognition. From the angle of human being's cognitive things, the description and evaluation of object features are two key parts for object recognition. Therefore, it is very important to obtain the optimal object matching that how to effectively describe contour features and properly evaluate the similarity between different features.
This dissertation focuses on the extraction, description and evaluation of contour features, and studies the effect of contour feature applied to the following aspects:the smoothing of noisy contour, the alignment of the contour starting-point and the recognition of object contour. As for the object contour, current contour smoothing algorithms cannot achieve a better tradeoff between feature preservation and noise suppression. And, existed starting-point alignment algorithms cannot be applied to the affine-transformed objects or are of high computational complexity. Moreover, state of art of the affine object recognition algorithms cannot meet the requirement of real-time applications. Furthermore, many occluded object recognition algorithms did not consider the influence on object recognition of the relationship between local contour features and the whole object. The main content and researching results of this dissertation are listed as follows:
1. Constructing a kind of framework of describing contour features. Within this framework, current contour feature description methods are summarized and classified into three levels:global features, local features and primitive features. Further, those methods are analyzed in three aspects:reliability, computational complexity and the application range. And it is beneficial for choosing the optimal manner of describing contour feature in the cases of various applications.
2. Proposing the affined object recognition algorithm based on segmented contour-smoothing. To suppress the noise and obtain the accurate feature description, the noisy contour is smoothed in advance. The contour is divided by curvature into two categories:feature zone and non-feature zone. By smoothing contour parts located in those zones by the Gaussian filters with different variance, the tradeoff between noise suppression and feature preservation is well achieved. After the contour-smoothing processing, the affined contour can be recognized by utilizing the affine invariant moment and minimum distance classifier. Simulation results testify the improvement by the segmented contour smoothing on recognition results.
3. Proposing the fast affined object recognition algorithm based on the alignment of contour starting-point. To decrease the high computational complexity of existed starting-point alignment algorithms, the algorithm based on the joint affine invariant arc-length is proposed and aligns the starting-point of contour successfully and efficiently. Then, the cascaded affine invariant function with translation invariance is constructed by using the multi-level approximation coefficients of discrete wavelet transform. Compared with similar recognition algorithms, the proposed algorithm greatly reduces the overall computational complexity. Simulation results show that the proposed alignment algorithm and recognition algorithm are robust to the noise and meet the requirement of real-time application.
4. Proposing the partially occluded object recognition algorithm based on contour features. Starting with the analysis of contour feature, the contour-splitting scheme based on the distribution of local curvature is proposed and builds the contour fragment database, which describe integrally local contour features. Then, the partially occluded object recognition algorithm based on features description integrity is proposed. The shape context distance is introduced to measure the similarity between contour features. And, the matching degree between occluded objects is measured by the combination of shape context distance and the reliability of contour fragments Simulations show that the proposed recognition algorithm needs less training samples and achieves the goal efficiently and accurately. Further, to strengthen the stability of local feature description under various occluded cases, contour fragments obtained by the contour-splitting scheme are classified into different categories with the help of object structure. Then the multi-level description model of local contour features is built. Quoted from the philosophical issue that "the relation between the wholeness and the part", this dissertation explores in depth the relation between the whole contour and contour fragments. And two parameters, importance and partiality, are presented to evaluate contour fragments. On that basis, the partially occluded object recognition algorithm based on multi-level description and evaluation of contour features is proposed. To measure the matching degree, the two evaluation parameters and the similarity between local contour features are combined to introduce the weighted partial similarity. Compared with current similarity measurements, the weighted partial similarity reflects properly the relation of the similarity between the local contour features and that between the corresponding whole contours. So, the partially occluded object recognition algorithm based on multi-level description and evaluation of local contour features is proposed. Simulations show that the proposed recognition algorithm achieves the stable recognition results under various occluded cases.
本论文围绕轮廓特征的提取、描述和评价这一主题展开研究,重点关注了轮廓特征在轮廓平滑、轮廓起始点配准和轮廓识别三个方面的应用。对于目标轮廓,现有轮廓平滑算法很难在保留轮廓特征和抑制噪声之间取得平衡,现有轮廓起始点配准算法无法适用于仿射目标或者具有较高的计算复杂度,现有仿射目标识别算法很难满足实时性的要求,现有部分遮挡目标识别算法没有考虑轮廓局部特征与目标轮廓之间的关系及其对目标识别结果的影响。本论文的主要工作和所取得的研究成果具体如下：
1.提出了一个轮廓特征描述框架。在该框架下,从全局特征、局部特征和结构特征三个层面对现有轮廓特征的描述方法进行总结和分类,并且从可信度、计算复杂度和适用范围三个方面分析了各类特征描述方法的特点,为不同应用环境下选取最佳的特征描述方法以及评价准则提供了借鉴。
2.提出了基于分段轮廓平滑的仿射目标识别算法。为了抑制噪声对目标轮廓的干扰从而准确描述轮廓特征,预先对含噪轮廓进行分段平滑处理。利用曲率将目标轮廓划分为特征区域和非特征区域,对两类区域分别采用不同的高斯平滑滤波器进行差异化处理,在保留轮廓特征和抑制噪声之间取得了较好的平衡。在此基础上,利用仿射不变矩和最小距离分类器对仿射变换下含噪轮廓进行识别。仿真实验结果验证了分段轮廓平滑算法改善识别结果的有效性。
3.提出了基于轮廓起始点配准的快速仿射目标识别算法。针对现有轮廓起始点配准算法存在计算代价较高的不足,提出了基于联合仿射不变弧长的轮廓起始点配准算法,实现了起始点的高效配准。在此基础上,利用离散小波变换的尺度系数构造了满足平移不变性的级联仿射不变函数。与同类基于小波变换的识别算法相比,大大降低了算法的整体复杂度。仿真结果表明,所提出的轮廓起始点配准算法和仿射目标识别算法对噪声具有较好的鲁棒性,并且满足了实时性的要求。
4.提出了基于轮廓特征的部分遮挡目标识别算法。从目标的轮廓特征入手,提出了基于局部曲率分布的轮廓划分方案,建立了完整描述轮廓局部特征的轮廓分段数据库。在此基础上,提出了基于轮廓特征完整描述的部分遮挡目标识别算法,采用形状上下文距离衡量轮廓特征之间的相似度,并结合轮廓分段的可信度评价了部分遮挡目标的匹配程度。仿真实验表明,所提出的识别算法所需训练样本相对较少,能够高效、准确地识别不同遮挡情况下的遮挡目标。更进一步,为了增强在不同遮挡情况下描述局部特征的稳定性,结合目标的结构信息对轮廓分段进行分类,得到了轮廓局部特征的多层描述模型。借鉴哲学中“整体与部分之间关系”的概念,论文深入探讨了整体轮廓与轮廓分段之间的关系,给出了评价轮廓分段的两个参数：重要性和局部性。在此基础上,提出了基于轮廓特征多层描述和评价的部分遮挡目标识别算法。为了准确衡量遮挡目标的匹配程度,将两个评价参数与轮廓特征之间的相似度相结合提出了加权部分相似度。与现有相似性评价准则相比,加权部分相似度合理反映了局部特征相似性与整体目标相似性之间的关系,可以获得遮挡目标的最佳匹配结果。仿真实验表明,所提出的识别算法在不同遮挡情况下均能获得稳定的识别结果。
Object recognition is always one of the hot topics in the field of image processing, pattern recognition and computer vision, and it is widely used in every area of the daily lives, industrial applications and military activities. Contour is one of the apporaches to describe the object and plays an important role in object matching and recognition. From the angle of human being's cognitive things, the description and evaluation of object features are two key parts for object recognition. Therefore, it is very important to obtain the optimal object matching that how to effectively describe contour features and properly evaluate the similarity between different features.
This dissertation focuses on the extraction, description and evaluation of contour features, and studies the effect of contour feature applied to the following aspects:the smoothing of noisy contour, the alignment of the contour starting-point and the recognition of object contour. As for the object contour, current contour smoothing algorithms cannot achieve a better tradeoff between feature preservation and noise suppression. And, existed starting-point alignment algorithms cannot be applied to the affine-transformed objects or are of high computational complexity. Moreover, state of art of the affine object recognition algorithms cannot meet the requirement of real-time applications. Furthermore, many occluded object recognition algorithms did not consider the influence on object recognition of the relationship between local contour features and the whole object. The main content and researching results of this dissertation are listed as follows:
1. Constructing a kind of framework of describing contour features. Within this framework, current contour feature description methods are summarized and classified into three levels:global features, local features and primitive features. Further, those methods are analyzed in three aspects:reliability, computational complexity and the application range. And it is beneficial for choosing the optimal manner of describing contour feature in the cases of various applications.
2. Proposing the affined object recognition algorithm based on segmented contour-smoothing. To suppress the noise and obtain the accurate feature description, the noisy contour is smoothed in advance. The contour is divided by curvature into two categories:feature zone and non-feature zone. By smoothing contour parts located in those zones by the Gaussian filters with different variance, the tradeoff between noise suppression and feature preservation is well achieved. After the contour-smoothing processing, the affined contour can be recognized by utilizing the affine invariant moment and minimum distance classifier. Simulation results testify the improvement by the segmented contour smoothing on recognition results.
3. Proposing the fast affined object recognition algorithm based on the alignment of contour starting-point. To decrease the high computational complexity of existed starting-point alignment algorithms, the algorithm based on the joint affine invariant arc-length is proposed and aligns the starting-point of contour successfully and efficiently. Then, the cascaded affine invariant function with translation invariance is constructed by using the multi-level approximation coefficients of discrete wavelet transform. Compared with similar recognition algorithms, the proposed algorithm greatly reduces the overall computational complexity. Simulation results show that the proposed alignment algorithm and recognition algorithm are robust to the noise and meet the requirement of real-time application.
4. Proposing the partially occluded object recognition algorithm based on contour features. Starting with the analysis of contour feature, the contour-splitting scheme based on the distribution of local curvature is proposed and builds the contour fragment database, which describe integrally local contour features. Then, the partially occluded object recognition algorithm based on features description integrity is proposed. The shape context distance is introduced to measure the similarity between contour features. And, the matching degree between occluded objects is measured by the combination of shape context distance and the reliability of contour fragments Simulations show that the proposed recognition algorithm needs less training samples and achieves the goal efficiently and accurately. Further, to strengthen the stability of local feature description under various occluded cases, contour fragments obtained by the contour-splitting scheme are classified into different categories with the help of object structure. Then the multi-level description model of local contour features is built. Quoted from the philosophical issue that "the relation between the wholeness and the part", this dissertation explores in depth the relation between the whole contour and contour fragments. And two parameters, importance and partiality, are presented to evaluate contour fragments. On that basis, the partially occluded object recognition algorithm based on multi-level description and evaluation of contour features is proposed. To measure the matching degree, the two evaluation parameters and the similarity between local contour features are combined to introduce the weighted partial similarity. Compared with current similarity measurements, the weighted partial similarity reflects properly the relation of the similarity between the local contour features and that between the corresponding whole contours. So, the partially occluded object recognition algorithm based on multi-level description and evaluation of local contour features is proposed. Simulations show that the proposed recognition algorithm achieves the stable recognition results under various occluded cases.
引文
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