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一个比例和方向自适应均值漂移跟踪算法（SOAMST） 提出本文所要解决的问题，如何估计的规模和方向 改变均值漂移下的目标跟踪框架。在原来的均值偏移 跟踪算法，可以很好地估计目标的位置，规模的同时， 方向的变化，不能自适应估计。考虑到图像（重量） 是来自于目标运动模型和候选模型可以代表的可能性，一个 像素属于目标，我们证明了原来的均值漂移跟踪算法可以 推导出的重量图像的零阶和一阶矩。随着零阶 矩和目标模型和候选模型之间的Bhattacharyya系数， 提出了简单而有效的方法来估计的规模为目标。然后一种方法， 利用估计的区域和第二阶中心矩，提出 自适应地估计目标的宽度，高度和方向的变化。广泛 实验来证实所提出的方法，并验证其可靠性 规模和方向变化的目标。(A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this paper to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, while the scale and orientation changes can not be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, we show that the original mean shift tracking algorithm can be derived using the zeroth and the first order moments of the weight image. With the zeroth order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilizes the estimated area and the second order center moment, is proposed to adaptively e)

包含拉东正向变换和反拉东变换及使用双曲拉东变换去除多次波(Radon contains forward transform and inverse Radon transform and the use of hyperbolic Radon transform to remove multiple wave)

高级数据呈现技术,是一门比较先进的技术,希望对大家有所帮助.(dfgdfgdfsdfsdafasgvasdfgsafasdgfdgasdfsdafsdfsdfs)

说明：  IPL库是Intel为了配合其MMX,SSE,SSE2以及将来的VLSW等技术发布的图像处理库 支持1,8,16,位有/无符号，32位有符号，32位浮点类型数据类型。 支持RGB,CMYK,YCACB,YUV,XYZ,色彩空间，支持alpha通道 支持矩形ROI,通道ROI，遮罩 支持分块图像，错误处理，用户定义函数 支持图像数学，几何，滤波，图象统计，色彩空间变换操作。(IPL is the database to accommodate its Intel MMX, SSE, SSE2 and future technologies such as VLSW issued by the image processing support for 1, 8, 16, spaces with/without symbols, and symbols are 32, 32-bit floating-point type data types. Support for RGB, CMYK, YCACB, YUV, XYZ color space, support alpha channel support rectangular ROI, ROI access, support shielding block images, error handling, user-defined functions to support image mathematics, geometry, filtering, image statistics, color space conversion operations .)

说明：  对偶四元数应用于空间坐标变换，和对偶四元数和齐次矩阵的转换(Dual quaternion coordinate transformation applied to space, and dual quaternion and homogeneous matrix conversion)

用于求取三维点云数据的法向量，我的人脸建模就是用的这个，效果很好，但必须保证点云没有重叠的，否则会出现严重错误。(Strike a 3D point cloud data for normal vectors, and my face is to use this model, the effect is very good, but must ensure that there is no overlap of the point cloud, there would be a serious mistake.)

说明：  灰度图像分割，三维大津法或三维最大类间方差法(3D Maximum Between Cluster Variance)

四步相移法程序和最小二乘法相位解包裹程序，已经验证，此方法比较不错。(Four step phase-shift method program and least squares phase solution package, have validation, this method is good. )

计算Gabor滤波器函数. Gabor1.m:4个方向的Gabo滤波器模板的图像显示. Gabor2.m:4个方向的Gabor滤波器对lena进行滤波的顶层调用模块. gabor.m:绘制一个Gabor滤波器的空域和频域函数图. compute.m:计算Gabor滤波器函数（要被上面3个模块调用，这3个模块彼此独立）(Gabor filter function calculation. Gabor1.m: 4 directions Gabo filter template image display. Gabor2.m: 4 directions of the Gabor filter for filtering lena call top-level module. Gabor.m: Drawing of a Gabor filter browser s airspace and frequency domain function Fig. compute.m: Calculation of Gabor filter function (to be above three modules call, this three modules independent of each other))

说明：  基于SIFT的图像配准程序 SIFT特征匹配算法是目前国内外特征点匹配研究领域的热点与难点,其匹配能力较强,可以处理两幅图像之间发生平移、旋转、仿射变换情况下的匹配问题,甚至在某种程度上对任意角度拍摄的图像也具备较为稳定的特征匹配能力(SIFT-based image registration procedure is the SIFT feature matching algorithm for matching feature points at home and abroad a hot area of research and the difficulties in matching their ability to deal with translation between two images, rotation, affine transformation to match the case of problems, and even to some extent in any angle images will also be able to match the characteristics of a more stable capacity)