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fractalsSSE__dsthbsrc
- You probably have heard about fractals before. They are beautiful pictures such as the one shown above. Any fractal can be described using iterative formulas. So you can generate a fractal by evaluating these formulas and finding the color of each pi
efficient_registration
- 利用傅里叶变换，在频域上对两幅图像配准，是一种比较配准的新方法，但是对弹性配准的效果不是很好 需要进一步的研究。-Registers two images (2-D rigid translation) within a fraction of a pixel specified by the user. Instead of computing a zero-padded FFT (fast Fourier transform), this code uses selective upsamp
PoissonNeumann
- C Implementation of 2D Poisson Solver using Neumann Boundary Conditions: Based on a direct analytical solution using sine transforms Single iteration, no magic numbers, no convergence issues. Extremely fast, couple of seconds for 1M pixel image-C Imp
Block-matching(Matlab)
- 用块匹配法求超分辨率复原中的运动矩阵!提出了一种快速，半像素的三步法！-Block-matching method using super-resolution recovery of movement in the matrix! Presents a fast, three-step half-pixel!
SUSAN
- SUSAN角点检测算法经典文献 This paper describes a new approach to low level image processing in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely rel
SURF
- SURF角点检测 This paper describes a new approach to low level image processing in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to
NCC-fast-image-match-suanfa
- 在图形中提取图像中的角点，为了提高精度进行亚像素检测并进行图像匹配-Extract images in the graphics in the corners, in order to improve the accuracy of sub-pixel detection and image matching
PIC
- 旋转图片，可以把图片快速旋转，速度快。不是采用像素计算法-Rotate the image, you can rotate the picture fast, fast. Instead of using pixel calculation
subpixel04
- 一种快速结构光条纹中心亚像素精度提取方法.caj 一种亚像素精度的边缘检测方法.caj-A fast structured light stripe center sub-pixel precision extraction. Caj a sub-pixel accuracy of edge detection methods. Caj
003935699yaxiangsuweiyiguji
- 亚像素位移快速匹配方法的代码实现，可以精确到亚像素运动估计。也是图像超分辨率重建的一个重要内容-Fast sub-pixel displacement matching method code can be accurate to sub-pixel motion estimation. Image super-resolution reconstruction is an important element of
Fast-Rotation-invariant-Template
- 图像梯度方向应用干旋转不变性模板匹配时存在计算量较大的问题，由此提出一种改进的基于梯度方向码的旋转不变模板匹配 方法，通过计算积分直方图降低统计直方图的计算填，采用像素跳跃的匹配方法减少大量无效的匹配运算。实验结果表明，该方法在保证 匹配准确性的前提下，匹配速度提高了3倍至6倍，可以达到实时性要求。-it uses integral histogram to reduce the computing COSTS of computer histograms，and uses pixe
cppbgfg_gaussmix2
- 背景建模方法之高斯混合模型，使用到MOG2。算法快，并且可以进行阴影检测。遍历性：对每一个像素进行建模。作者为Z.Zivkovic-The algorithm similar to the standard Stauffer&Grimson algorithm with additional selection of the number of the Gaussian components based on: "Recursive unsupervised learning of fini
Fast-median-filtering-algorithm
- 中值滤波的基本原理是把数字图像或数字序列中一点的值用该点的一个邻域中各点值的中值代替，让周围的像素值接近的真实值，从而消除孤立的噪声点。-The basic principles of the median filter is the value of digital image or sequence of numbers in a neighborhood of the point value of the median instead, so that the surrounding pi
Fast-matching-algorithm
- 匹配过程只要对编码值进行相等比较，而且可以采用快速的比较算法。新算法对像素灰度的变化与噪声具有鲁棒性。-The matching process as long as the coded values are equal, and can quickly compare algorithms. The new algorithm is robust to the change and noise of the pixel gray.
fast-edge-tracking-algorithm
- 刘丽华提出基于多方向形态算子的快速边缘跟踪算法，主要解决了利用边缘方向信息进行边缘像素编码、边缘像素跟踪最小方向计算和非连续点判断方法等算法关键问题。-Liu Lihua proposed morphological operators based on the multi-directional fast edge tracking algorithm to solve the key problems using edge direction information edge pixel e
tuixiangzengqiang
- 均值滤波：把每个像素周围的8个像素来做均值操作，可以平滑图像，速度快，算法简单，但是无法去掉噪声，只能微弱的减弱它。-The mean filtering: 8 pixels around each pixel do mean, you can smooth image, fast and simple algorithm, but can not get rid of the noise, only faint weakened it.
fast-fractal
- Fast Fractal Image Compression Based on Domain-Range Pixel Value Difference
efficient_registration
- 利用傅里叶变换，在频域上对两幅图像配准，是一种比较配准的新方法，但是对弹性配准的效果不是很好 需要进一步的研究。-Registers two images (2-D rigid translation) within a fraction of a pixel specified by the user. Instead of computing a zero-padded FFT (fast Fourier transform), this code uses selective upsamp
m9
- 基于像素的背景建模方法速度较快但不能很好地描述背景运动，光流能准确描述物体运动但计算量大，难以满足实时的要求．提出一种结合基于像素的背景建模方法速度快以及光流描述物体运动准确优点的背景建模和目标检测方法．具体来说，为静止背景建立传统基于像素的灰度背景模型，为运动背景建立光流背景模型，通过２种背景模型的有效结合快速准确地实现目标检测．实验结果表明，提出的方法建模速度与基于像素背景建模方法相当，同时，又有光流准确描述背景运动的优点，综合性能超越上述２种方法．-Faster but not a goo
m10
- 背景建模是实现运动目标检测与跟踪的关键技术之一。在实时视频监控系统中，对背景建模算法的运行时间及所提取出的背景图像的实时性有很高的要求，针对这一问题，提出了一种基于切比雪夫不等式的自适应阈值背景建模算法。算法利用切比雪夫不等式计算像素点色度变化的概率估计值，提出了一种自适应阈值分类方法，它将像素点快速分类为前景点、背景点及可疑点，再利用核密度估计方法对可疑点进行进一步分类，最后利用背景更新算法提取实时背景图像。实验结果证明，该算法能快速有效地区分特征明显的背景点与前景点，提高了背景图像提取的速