【作者】 陈潇；

【导师】 李建勋；

【作者基本信息】 上海交通大学 ， 控制科学与工程， 2011， 博士

【摘要】 基于图像的物体识别、目标跟踪技术是计算机视觉研究的重要方向,在社会生产、生活的各个领域有着广泛的应用。从图像中提取能够表征目标物体几何结构本质特征的三维射影不变量,不受成像角度、位置、光照条件、空间几何变换等因素的影响,将有效改善后续的识别、跟踪等高级应用。目前关于图像目标三维射影不变量研究中,可用于构造不变量的几何特征十分有限,仅限于图像点特征,适用范围也局限于可进行三维重建的图像几何结构,或者特殊几何结构,相关工作侧重于理论推导,在实践中仍面临着很多问题。本文正是在这一背景下对图像目标三维射影不变量的构造与计算工作进行了深入的研究。本文主要完成了以下几个方面的工作。提出了图像目标三维射影不变量构造与计算的一般方法。本文在基于图像点特征的目标三维射影不变量非齐次解法基础上,给出了齐次性解法,齐次解法可以改善不变量计算的稳定性和可靠性。在此基础上,本文将图像直线特征引入三维射影不变量计算,提高了图像目标三维射影不变量计算的稳定性和可靠性。更重要的是,直线特征的使用使得在已知信息不足以进行三维重建的情况下,仍然可以获取目标的部分三维信息,这对于识别、跟踪等后续应用有着重要的意义。本文给出了根据图像中点特征、直线特征计算三维射影不变量的一般方法,对如何利用图像特征、如何获取独立等式等问题进行了详细的论述。根据本文提出的方法,使用者可以自行构造与计算适用于特定应用的几何结构所具有的三维射影不变量,而不仅仅局限于已有文献中介绍的几何结构。提出了基于前景估计的图像检索方法。在参考了关于人类视觉注意机制的生理学研究与心理学研究,以及计算机视觉领域中关于图像显著性检测的研究后,本文归纳总结了确定图像前景的几条基本原则。根据这些基本原则定义了图像区域的前景权重,前景权重的确定一方面为图像目标三维射影不变量计算提供约束条件,提高了不变量计算的可靠性;另一方面可以为不变量计算提供可靠的图像信息。改进了谱匹配中邻接矩阵的构造方法。本文中使用的图像点特征均为具有明确几何意义的角点特征,但是角点特征没有成熟的特征描述方法,缺乏分辨能力,匹配一般仅利用角点之间的几何关系。本文在利用角点间几何关系构建邻接矩阵的基础上,充分利用角点周围邻域的颜色、纹理等特征进行匹配,取得了更好的匹配效果。更多还原

【Abstract】 Image based object recognition and tracking are important research fields in computer vision, which are widely applicable in industry or daily lives. Extracted from images, the three dimensional invariants are characteristic representations of the object of interest and invariant to variations in viewpoints, position, illumination, transformations and so on. High level applications such as object recognition and tracking can further benefit from 3D invariants as well. Studies on three-dimensional projective invariants show that there are limited geometric features which 3D projective invariants can be constructed from, such as points. In recent works, the constructed invariants tend to be mainly used in 3D reconstruction and many practical problems remain unsolved. In the context, further researches have carried out in this thesis to the construction and calculation of 3D projective invariants.The main works are summed in follows:Firstly, Methodologies to construct and calculate 3D projective invariants are proposed. Based on the inhomogeneous solution from image point invariants, a homogeneous solution is deduced, which provides a more stable and reliable solution. Moreover, the line features are introduced into the construction of 3D invariants, by which the stability and reliability of constructed 3D invariants are further improved. In addition, extraction of 3D features is made possible even when 3D construction taking place with insufficient information, which profoundly contributes the following applications such as recognition and tracking. In this thesis, the method to construct 3D invariants from point and line invariants is given. Specifically, image features and the establishment of independent equations are described in detail. The proposed method provides a more flexible way to construct and calculate 3D invariants from specially designed geometric structures, instead of the structures introduced in the references.Secondly, image retrieval based on foreground prediction has been proposed. Principles for foreground prediction are established based well studied human attention mechanism and neuron psychology as well as previous image saliency research in computer vision. According to these principles, weights are assigned to image regions to form the most salient foreground. The foreground prediction can therefore constrain the calculation of 3D invariants and provide reliable image information as well.Thirdly, an improved affinity matrix is formed in spectral matching. In this thesis, corners which well defined geometric meaning are used to construct 3D invariants, but corners themselves are lack of discriminate descriptions, therefore the matching can only make use of the geometric relations between corners. We, on the other hand, take full advantage of the color, texture information in their neighboring areas besides geometry and further constrain the matching procedure by forming an informative affinity matrix. A better matching is presented.更多还原