【作者】 雷明；

【导师】 杨丹； 张小洪；

【作者基本信息】 重庆大学 ， 应用数学， 2008， 硕士

【摘要】 图像的特征提取和描述是基于特征的图像处理和计算机视觉的基础环节,特征检测算子的检测性能和描述算子的表针性能直接决定了图像处理的效率和精度。在实际问题中图像可能受到噪声、背景的干扰,也可能发生视角、光照、尺度、平移、旋转、仿射等变化,选择合理的图像特征和描述算子,使得这些特征不仅具有良好的表针性而且具有良好的鲁棒性是一个十分关键的问题。本文针对以上难点做了如下工作:使用B-样条函数构造尺度空间代替传统的高斯尺度空间。B-样条函数的特殊性产生了卷积的高效算法,其计算复杂度与B-样条函数的尺度无关,只与信号或者图像本身有关。B-样条函数对高斯函数有良好的逼近效果,因而它继承了高斯函数的大部分优良性质。在B-样条尺度空间下定义了平面轮廓在其支撑区域内的协方差矩阵的多尺度表示,将协方差矩阵中引入尺度因子,减弱了噪声和由于数据离散而产生的影响,矩阵的最大特征值对应的向量表示轮廓切线方向。将多尺度乘积的思想引入到角点检测中,各个尺度下的切线方向变化率的乘积定义为多尺度积,即为角点的响应函数。随着尺度的增加,角点的响应会在不同尺度下保留,尺度增加的B-样条函数能够抑制噪声的影响。而角点就定义为多尺度积大于给定阈值的局部极大值所对应的点。本文对算法的性能进行了比较系统的评价。通过实验证明了算法具有旋转不变性,并对微小的尺度变化不敏感,而且与其他经典的角点检测器进行了对比,实验结果也表明新的算法具有良好的检测和定位性能,算法效率高。使用多尺度Harris算子检测图像的角点作为初始兴趣点。针对自适应非极大值抑制排除了大量潜在匹配点的缺陷,引入条件理论对初始兴趣点进行控制,排除病态点,减少后续过程的计算量,提高算法效率,同时最大限度的保留了匹配点。实验结果表明提出的特征匹配算法效率改进明显,匹配效果良好,对图像的几何变换、噪声及光照变化等具有较强的鲁棒性。更多还原

【Abstract】 Image feature detection and description are fundamental tasks of many image procession and computer vision. The performance of feature detectors and descriptors directly determines the efficiency and precision of image procession. It is important that image local feature should be as distinctive as possible while also be robust to occlusion, background clutter and noise, invariant to various image transformations due to translation、rotation、scale、affine deformation, difference in illumination, object movement, and change in viewpoint. To these difficulties, this paper is made several matters following:Traditional Gaussian-derived scale space was substituted by B-spline derived scale-space. Convolution algorithm of B-spline function has an efficiency solution because of the specialty of the way B-spline convolved and the specialty of discrete B-spline of order zero. Computation complexity of the convolution algorithm has nothing to do with the scale of B-spline function, but only depends on the signal or image data itself. B-splines are good approximations of the Gaussian kernel, so B-spline derived scale-space inherits most of the nice properties of the Gaussian-derived scale-space. The multi-scale representation of covariance matrix for planar curve over its region of support was defined in the framework of B-spline scale space. Covariance matrix was expanded to scale space, which reduces the influence of computation of covariance matrix caused by noise. The eigenvector corresponding to the largest eigenvalue of covariance matrix indicates contour tangent orientation. Multi-scale space product was introduced into corner detection. Multi-scale space response function of cornerness was defined. The multiplication of change rate of contour tangent orientation at different scales was defined as multi-scale product. As scale becoming larger, the response of corner of different scales was remained. A local extreme of the product was reported as corner when the value of the product exceeded a threshold. Systematic evaluation of the proposed corner detection algorithm was made in this paper. Experiments demonstrated that our algorithm is rotation-invariant and insensitive to slight scale transform. Moreover, this algorithm was compared with other classic detectors, and the experimental results also showed that the new algorithm has good detection, localization performance and good efficiency.Corners in an image were detected by multi-scale Harris operator, and they were taken as initial interest points. Since adaptive non-maximal suppression eliminated lots of potential matching points, condition theory was applied to control the number of initial interest points. The bad conditioned points were eliminated, so the computational complexity of the following process was decreased and the efficiency of the algorithm was improved. At the mean time, matching points were mostly kept. Experimental results showed that the efficiency is improved significantly, the proposed algorithm is good at feature matching, and it has good robustness to geometrical transformations, image noise and illumination change.更多还原