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图像特征检测与匹配研究
其他题名A study on image feature detection and matching
王志衡
学位类型工学博士
导师吴福朝
2008-12-28
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词特征检测 特征匹配 伪球滤波器 内积能量 局部方向分布 均值标准差描述子 直线匹配 曲线匹配 Feature Detection Feature Matching Pseudosphere Filter Local Orientation Distribution Mean Standard Deviation Descriptor Line Matching Curve Matching
摘要图像特征检测与匹配是计算机视觉中的一个关键问题,在物体识别、三维重建、图像配准、视频理解等诸多领域具有非常重要的应用。尽管近些年来对该问题的研究取得了一些突破性进展,但由于不同图像成像条件复杂、场景多样和存在各种几何形变,图像特征检测与匹配依旧是一个极具挑战性的热点研究课题。本文针对这一问题进行了深入研究,论文工作的主要创新之处有: (1)通过将数学上的拽物线和伪球函数引入图像处理领域,本文提出了一种新的滤波器—伪球滤波器。在伪球滤波器中,除了尺度参数外,还引入了边缘保持参数,它能较好地解决传统滤波器的平滑性能与边缘定位精度之间的矛盾问题。以伪球滤波器取代经典Canny边缘检测算子中使用的高斯滤波器,得到了一种基于伪球的边缘检测算子。实验结果表明,与经典的Canny边缘检测算子相比,在具有相当平滑性能的条件下,基于伪球的边缘检测算子具有更高的边缘定位精度。 (2)引入了图像梯度内积能量的概念,从理论上证明了在抑制图像噪音和细节方面,内积能量比梯度幅值具有更好的性能。利用内积能量取代经典Canny算子中使用的梯度幅值,提出了一种基于内积能量的边缘检测算子,与经典的Canny边缘检测算子相比,在具有相当边缘定位精度的条件下,基于内积能量的边缘检测算子对图像噪音和细节具有更强的抑制能力。 (3)针对大多数常见角点检测算子检测出的角点定位不准确问题,提出了一种基于局部方向分布的角点检测及亚像素定位算法。相对于常见角点检测算法,基于局部方向分布的算法不仅具有更高的定位精度,同时还具有更强的鲁棒性。 (4)提出了一种通过直线描述子来进行自动直线匹配的新思路,将直线描述子的建立分为以下3个主要步骤:首先将直线的邻域划分为一系列平行线或者相互重叠的子区域的形式,然后通过选择图像特征建立直线的描述矩阵;最后通过计算描述矩阵列向量的均值和标准差获得直线描述子。基于两种直线邻域的划分方法和不同的图像特征,提出了4种直线描述子FMSD、MMSD、GMSD和MSLD。并将直线描述子MSLD推广到曲线描述子MSCD。实验结果表明提出的各种描述子具有出色的匹配能力,能够有效地解决直线曲线的自动匹配问题。
其他摘要Image feature detection and matching is a key problem in many applications of computer vision, such as object detection, 3D reconstruction, image registration and video understanding. Though great progress has been made in this filed recently, it is still a challenging problem, due to complexed imaging conditions, a large family of image scene types, various shape distortions. The main contributions of this dissertation can be summarized as follows: (1) By introducing the tractrix and pseudosphere in mathematics into the field of image processing, a novel image filter called the pseudosphere filter is presented. Besides a scale parameter, an edge-preserving parameter is introduced in the pseudosphere filter, and thus a better trade-off between image smoothing and edge locating can be obtained using it. A pseudosphere-based edge detector is formed by replacing the Gaussian filter in the classic Canny edge detector with the Pseudosphere filter. Compared with the classic Canny edge detector, in the case of having the same smoothness, the pseudosphere-based edge detector offers a better precision for edge locating. (2) The concept of gradient inner product energy is introduced, and it is proved mathematically the gradient inner product energy can overperform the gradient magnitude in restraining the noise and tiny edges. A novel image edge detector called the inner product energy-based edge detector is presented by replacing the gradient magnitude in the Canny edge detector with the inner product energy. Compared with the classic Canny edge detector, in the case of offering the equivalent precision for edge locating, the inner product energy-based edge detector performs better in de-noising and tiny edges controlling. (3) Focusing on the problem of corner localization, a novel algorithm for corner detection and localization is proposed, which is based on local orientation distribution (LOD). The LOD-based algorithm can provide higher localization accuracy and perform more robust than most popular detectors. (4) A novel ideal to automatically match lines based on line descriptors is presented. The main steps of constructing a line descriptor are following as: firstly, the line neighborhood is decomposed into several parallel line segments or overlapped sub-regions; then, a line description matrix is formed by selecting an image feature; finally, a line descriptor is obtained by computing the mean and standard deviation of the column vectors of the description matrix. Based on the two partition methods of the line neighbourhood and different features, four line descriptors are proposed for line matching in this paper. Besides, the MSLD line descriptor can be straightforwardly extended for a curve descriptor MSCD. Experiments show that these descriptors can be competent for line and curve automatic matching.
馆藏号XWLW1295
其他标识符200618014628074
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6131
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
王志衡. 图像特征检测与匹配研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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