搜索资源列表
imagetransformationbymatlab
- 1.图像频域处理正交变换的matlab实例 2.含有的频域变换内容如下： 正交变换通用算子 傅立叶变换 傅立叶变换的原理 傅立叶性质 二维离散傅立叶变换（ 2DDFT ） 快速傅立叶变换（ FFT ） 傅立叶变换的研究与应用 离散余弦变换 DCT 变换矩阵 dct2 函数和 dctmtx 函数 Walsh- Hadamard 变换 Radon 变换 -1. Image processing orthogonal freq
a_jpeg_compression_matlab
- It shows what a jpeg compression is all about. the function implements the DCT transform, using a matrix operator. note that matlab has a function for the DCT and iDCT transforms that might be more efficient. -It shows what a jpeg compres
Gl_ShadowsVolumes.ZIP
- The purpose of this demo is to create shadows using a technique called stencil shadow volumes in a 3d scene. To do this we will not need the shadow matrix from previous demo. In fact in order to perform this all we need is to render the sil
yanyan
- 把一个256*256的图像分成8*8小块，然后进行DFT变换，分别比较在空间域和频域内对图像进行二次抽样和差值最后得出的图像比较-1. Get a grey level image which size is N*N. (For example, 256*256, however, N = ), and partition to 8*8 sub images. 2. Apply DFT to these sub images, and get the fourier transfo
Threedimensional
- 这是一个三维重建程序，用于双目视觉中，由两个投影矩阵反求三维点-This is a three-dimensional reconstruction procedure for binocular vision, the projection matrix by the two reverse three-dimensional point
rigid_motion
- 刚性位置变化计算，输入物体在空间坐标系中的坐标，以及旋转向量和平移向量，计算其在摄像机坐标系中的坐标。- [Y,dYdom,dYdT] = rigid_motion(X,om,T) Computes the rigid motion transformation Y = R*X+T, where R = rodrigues(om). INPUT: X: 3D structure in the world coordinate frame (3xN matrix for
1
- 将一幅256*256的图像分成8*8的小块，对其进行DFT变换。分别比较在空间域和频域内对源图像进行二次抽样和差值最后比较不同-1. Get a grey level image which size is N*N. (For example, 256*256, however, N = ), and partition to 8*8 sub images. 2. Apply DFT to these sub images, and get the fourier transfor
1122zhenyan
- 这个是C制作的源代码。。。 对一幅图像进行DFT变换，分别对源图像在空间域和频域里进行二次抽样和差值，最后得出结果比较。- Get a grey level image which size is N*N. (For example, 256*256, however, N = ), and partition to 8*8 sub images. Apply DFT to these sub images, and get the fourier transformed ima
1438
- zju1438一道广度优先搜索题目，三维空间，陷阱很多。-You re in space. You want to get home. There are asteroids. You don t want to hit them. input Input to this problem will consist of a (non-empty) series of up to 100 data sets. Each data set will be formatted ac
calication
- 摄像标定法求T R矩阵的方法 摄像标定法求T R矩阵的方法-how to calculate the matrix R and T using the Camera Calibration method
p4p-problem-matlab-code
- 在P4P问题中,当空间4个点共面时,不仅摄像机坐标系与物体坐标系之间 的旋转矩阵R和平移向量t可以线性求解,而且可以同时确定摄像机的有效焦距f和像素比例，该程序设计模拟已知相机矩阵M情况下由已知三维空间点和图像坐标情况求解相机外参数矩阵-When space four points coplanar P4P problem, not only between the camera coordinate system and the object coordinate system rotatio
gongyu-guanlixitong
- matlab程序语言，图像增强算法的代码- Function [U, minv, SS] = Nonlinear_Diffusion (U_0, tau, eps, p, T, theta, sigma, fig_handle) Performs nonlinear scalar valued and coupled vector/matrix valued diffusion Inputs: U_0 nxmx dxw input field: D = 1, w
clothes-CVPR12
- cvpr2012_oral Street-to-Shop: Cross-Scenario Clothing Retrieval via Parts Alignment and Auxiliary Set-In this paper, we address a practical problem of crossscenario clothing retrieval- given a daily human photo captured in general environment
omp
- 简单的正交匹配追踪算法，应该都能看懂的。s-测量；T-观测矩阵；N-向量大小-The simple orthogonal matching pursuit algorithm should be able to understand the. s-measurement T-observation matrix N-vector size
360_panorama
- 360-panorama，360度柱面图像拼接，程序在VS2008环境下开发，opencv2.4.3版本，程序可以直接运行，以将所需依赖项添加在文件夹中，源图像放在source_pic中，最后生成结果在result_pic中。 实现步骤：1先将图像投影到柱面坐标上，2提取图像SIFT描述子，3将SIFT描述子配对，4用RANSAC算法根据配对成功的描述子统计出变换矩阵T，5图像拼接。 对于拼接后的图像，没有做进一步细化处理，使用者可以在生成的图像上再做进一步编辑。-360-panoram
haarmtx.m
- The Haar transform can be expressed in the matrix form as T = HIHT (inverse transform HTTH) where I is an NxN image matrix, H is an NxN transformation matrix and T is the resulting NxN transform. For Haar transform, the transformation matrix H c
glutEx5
- 一、实验目的和要求 在模型变换实验的基础上，通过实现下述实验内容，掌握OpenGL中三维观察、透视投影、正交投影的参数设置，并能使用键盘移动观察相机，在透视投影和正交投影间切换，验证课程中三维观察的内容； 进一步加深对OpenGL三维坐标和矩阵变换的理解和应用。 二、实验内容和原理 使用Visual Studio C++编译已有项目工程，并修改代码生成以下图形： “桌子和茶壶的正投影和透视投影” 可以使用键盘改变camera位置与观察方向 （按键为a
alpha
- 从所给图形中分别抓取12个点，然后弦长参数化并利用控制顶点与型值点间的关系计算出迭代矩阵，以抓取到的点作为型值点分别用jaccobi和Gauss-seidel迭代法进行迭代，得到控制顶点。然后将给出的控制顶点与算出的控制顶点分别代入中得到P(t)和Q（t）,再采用二范数估计误差(According to the graphics respectively to grab 12 points, then the chord length parameterization and the contr
eight point algorithm
- 3D单目视觉有用： 先求出基本矩阵，再求出R,T，然后就求出深度啦(3D monocular vision is useful: First find out the basic matrix, then find out R, T, and then find out the depth)
ICP
- 用svd的方法最优化求解两个点云的变换矩阵，R和T(Using SVD method to optimize the transformation matrix, R and T of two point clouds)