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鲁棒性图像曲线匹配方法研究
其他题名A Study On Robust Image Curve Matching Techniques
赵训坡
学位类型工学博士
导师胡占义
2005-04-01
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
关键词曲线匹配 证据积累 Fourier-mellin变换 选择性注意 感兴趣区域 Curve Matching Accumulation Of Evidence Fourier-mellin Transform
摘要曲线匹配是模式识别、计算机视觉和图像理解中许多任务的关键步骤之一。本论文围绕存在刚体变换或者可以认为近似存在刚体变换的曲线匹配问题进行了比较深入、系统的研究,主要工作有:1、提出了一种实用的基于证据积累的图像曲线粗匹配方法。该方法比较有效地解决了将图像中提取的一条曲线(较短)与一条参考曲线(较长)相匹配的问题。具有如下特点:一、通过实验发现曲线上两点之间的直线距离较它们之间的曲线距离对噪声等误差更鲁棒;基于此,在参考曲线上选取可能的匹配曲线段时,利用首尾点的直线距离作为主要匹配标准,大大提高了算法的鲁棒性;二、将证据积累的思想应用到控制点的匹配上,有效地去除了错误的控制点和不必要进行匹配的曲线段,在提高算法鲁棒性的同时极大地降低了算法的计算复杂度;三、在传统的Hausdorff距离计算中引入高斯概率统计模型,使其更适合作为实际应用中评价两条曲线匹配程度优劣的一种度量。大量卫星影像及数码相机照片实验证明了该匹配方法的有效性和实用性。2、提出了一种基于Fourier-Mellin变换的图像曲线粗匹配方法。该方法将两条图像曲线间的匹配问题转化为两幅二值图像间的配准问题,由图像间的配准关系间接地获得曲线间的变换参数。本文方法不需要提取曲线的局部特征,而是直接用曲线的整体频域特征实现匹配,从而避免了提取特征点以及特征点匹配等问题,同时还大大提高了算法的鲁棒性。大量的模拟数据和真实数据实验表明,该方法能够比较有效地解决存在(或近似存在)相似变换关系的两条曲线间的匹配问题。另外,本文还研究探讨了一种基于“感知物体”的选择性注意模型。该注意模型将“感知物体”看作注意的基本单元,并给出了一种计算感知物体显著度的算子。该模型包含两个步骤:(1)在给定的图像中如何选择第一个注视点;(2)注视点在给定图像中的有效转移。与文献中其它注意模型相比较,我们所提出的基于“感知物体”的选择性注意模型有以下特点:(1)该模型是完全基于物体的,其输出结果可以方便的用于物体检测、图像分割和场景分析中;(2)该模型是多尺度的,可以根据实际任务的需要对参数进行调整。
其他摘要The main contributions are two-fold:1. A practical coarse image curve matching method based on accumulation of evidence is proposed. The method can efficiently and robustly find the coarse location of a usually short extracted image curve into a long reference curve. The main characteristics of the proposed method are: Firstly, it is shown by experiments that the distance between two curve points is more reliable than the curve length itself to be used as a matching invariant in the presence of noise, hence in our work, the distance from the start point to the end point of the extracted image curve is primarily used for the selection of matching candidates in the reference curve. Secondly, the evidence accumulation concept is introduced in our matching algorithm, which not only significantly eliminates the incorrect matching of control points, but also substantially decreases the proportion of curve segments to be verified finally, a time consuming process. As a result, the computational efficiency and robustness of the proposed method is largely increased; Thirdly, the statistic Gaussian model is introduced in the classical Hausdorff distance calculation, which makes it more fit for the matching of curves with significant partial deformations. Extensive experiments with simulated data and images of satellite and digital camera show that our proposed curve matching method is practical, efficient and robust.2. A new planar curve matching method based on Fourier-Mellin Transform is proposed. In this method, the two curves to be matched are converted into two binary images at first, then the Fourier-Mellin based image registration method is used to register these two binary images to estimate the matching parameters of the two curves. The advantage of the proposed curve matching method is that it does not need extracting features nor establishing feature correspondences, the two difficult steps generally used in other curve matching methods. Instead it matches the two curves in frequency domain using their global similarity, and is inherently insensitive to random noise and local distortion. Experiments show that if the two curves to be matched are related by a similarity transformation, or its reasonable approximation, the proposed method can achieve satisfactory matching results. Besides, a concrete application, i.e., the coarse localization of remote sensing images, is reported to illustrate its applicability and usefulness.Besides the above curve matching problem, the present author also spent nearly two years on the selective attention problem, and proposed a “perceptual object” based selection model. Under this model, perceptual objects, which are defined as a connected region with homogenous gray level distribution, are hypothesized as the primary attention unit and an associate operator is proposed for computing the saliency of objects.
馆藏号XWLW886
其他标识符200118014604903
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5838
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
赵训坡. 鲁棒性图像曲线匹配方法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2005.
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