【作者】 刘萍萍；

【导师】 赵宏伟；

【作者基本信息】 吉林大学 ， 计算机应用技术， 2009， 博士

【摘要】 图像的局部不变性特征方法符合人类视觉处理机制,抗多种图像变换,并且具有冗余性低,无需预先对图像分割等特点,因而成为机器视觉和模式识别领域的研究热点。在大规模数据库和实时处理中应用局部不变性特征方法已经成为发展趋势。本文的目标在于提出高效的且可用于实时应用的局部不变性特征检测和描述算法。同时,本文将局部不变性特征方法与生物物体识别机制结合并应用于图像分类中,拓展了该方法的应用领域。本文的主要研究内容包括:(1)综述了局部不变性特征方法的研究现状,并分类讨论了当前主要的检测子和描述子算法及其评价标准;(2)提出快速旋转不变的局部不变性特征检测子算法,实验表明该检测子算法可在减少计算时间的前提下,提高特征检测在图像旋转变换中的性能;(3)提出了一种新型的基于分布的描述子算法,仅利用邻域像素的线性计算关系建立特征描述,实验表明该算法可有效减少特征描述时间而性能没有损失;(4)基于局部梯度的仿射不变矩提出了仿射不变的描述子算法,实验表明该算法可在图像的视角变换中提高特征描述的稳定性并且计算高效;(5)提出基于局部不变性特征与仿生视皮层识别机制结合的图像分类识别方法,实验表明其性能超过对比的机器视觉算法。更多还原

【Abstract】 Feature matching among images is the foundation of most applications in machine vision. The images may have not only linear geometric deformation, such as scale, rotation and so on, but also illumination condition changes or viewpoint changing as well as located in cluster background or image occlusions. The method of local invariant feature overcomes features expression from image transformations, thus becomes a very useful tool under the visual circumstances changing. At the same time, due to emphasis on some kind of features in local area, it has the characteristics of low redundancy in the efficiency and speed. This method has become a very active research direction, and there are a lot of research results applied in machine vision, for example object recognition, image matching, image retrieval, robot navigation, scene panorama and 3D reconstruction. There are two problems needed to be solved in this method: the first one is location of the characteristics area, called detector algorithm; the second one is information description in the area, called descriptor algorithm. Choosing image matching in robot navigation system as applications background, this thesis concerns with the efficiency of the detector algorithm as well as the robustness in image transformations, and high efficiency and invariance to viewpoint changing of descriptor, then proposes a novel fast detector algorithm and descriptor algorithms, which can all be used in real-time applications. On the other side, this thesis also discusses inspiration the recognition mechanism of biological visual cortex. A new feed forward hierarchy image classification model is proposedFirst, for detecting the local feature points in a accurate, robust and efficient way, this thesis surveys the research status of fast detector algorithm and analysis the several classical fast detector algorithm, such as DoG,FAST as well as Fast-Hessian. According the performance analysis of existed detectors, we know that the detector algorithm based on Hessian matrix is the most time-saving one, which is better than other algorithms on stability and accuracy of features. A novel detector algorithm based Hessian matrix was proposed in this thesis, using mean filter to estimate Gaussian second order partial derivative, avoiding scale space iterative computations. In addition, using trace and determinant of Hessian matrix as criterion simultaneously to extract interest points can get rotation invariance, and can avoid the extracting feature points unstably from noise or texture image.Second, a local feature description method based on image pixel distribution is proposed in this dissertation. The performance of descriptor based on distribution is better than others. From the pixel intensity distribution, through the relatively simple intensity difference compared to build local description, and use the lower-dimensions descriptor to shorten the feature matching time, and establish a new descriptor algorithm based on pixel distribution, which is suitable for scale change, plane rotation and linear illumination change to a certain limit. After normalizing pixel intensity of local neighbor areas surrounding interest points, descriptors could be computed based on the intensity distribution property of local neighbor. During the grayscale differences calculation of based on normalized segmentation point, removing pixel intensity differences caused by the illumination changing, that makes features description satisfy invariant under illumination transformations.In addition, image matching in mobile robot navigation path applications, often face various deformations except objects rotating or scale zooming. This could be estimated by affine transformation. The existing affine invariant mostly depends on detector algorithm extraction local features of affine invariant, which is not suitable for many real-time applications. The reason is extracting affine invariant features from the whole image will causes large computing costs. Studying the description algorithm for affine invariant of local features can decrease greatly the whole time for local feature extraction. As a powerful tool in image processing, moments’research history and the fundamental theory are reviewed in this thesis. Invariant moment as well as the affine invariant moments are introduce later, and then a novel descriptor based on Flusser&Suk moments is proposed——the affine invariant moment based on local gradient. In order to ensure the invariant of description features during illumination transformation, when extracting the central moment in local area, normalized local gradient algorithm is adopted without depending on original pixel values, which could reduce degree of dependence to the original pixels intensity and improve robustness of the description in the illumination transformation.Finally, this thesis applies local features in image classification and recognition. The original source of local features is consistent with identification mechanism of biological vision. The vision receptive proposed by Wiesel and Hubel is a natural processor for local features. So image classification based on local features is in accordance with the features-dealing mechanism of biological visual identification. On the other hand, image classification system based on biological visual cortex of hierarchical model is the hotspot in image classification researching. A new feed forward hierarchy image classification model is proposed in the article, applying the latest neurophysiologic findings about visual cortex recognition and local feature method. In order to enhance the stability of image features in multi image transformations, the primary features are processed in sparseness mechanism. At the same time, this method overcomes the disadvantages existing in formal models, such as poor robustness in rotation as well as illumination changing, a large amount of primary features and calculation complexity.更多还原