tuxianggongcheng

说明：  基于背景差分法和帧间差分法的目标跟踪 应用了二维滤波，效果较好(Based on background difference and frame difference method applied to two-dimensional target tracking filter, better)

说明：  计算全息算法，使用matlab算法计算出图像的相位分布(Computational holographic algorithm)

说明：  本人自己编写的用matlab实现彩虹程序，(I have written with matlab Rainbow realize procedures,)

提取二值图像中的所有非零点坐标，进行矩阵标准化，最后进行二次曲线拟合(Extracting the binary image of all non-zero coordinates, matrix standardized last quadratic curve fitting)

程序是基于DSP的数字图像处理技术，可以实现军事滤波，中值滤波和维纳滤波，实现效果较好(The program is a DSP-based digital image processing technology, the military filtering, median filtering and Wiener filtering, better )

检测鼠标信息，C++Builder精选编程学习源码，很好的参考资料。(Detecting mouse information, C++Builder select learning programming source code, a good reference.)

多维尺度不变性的Ｃ＋＋实现，必须联合ＩＴＫ一起用，享受吧(Multi-dimensional scale invariance of the C to achieve, we must co-itk together, and enjoy it)

在图像处理过程中盲解卷积也是一个很好地图像去噪的方法。(In the image processing method of blind deconvolution is also a good image denoising.)

SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。[1] 该方法于1999年由David Lowe[2] 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004年再次经David Lowe整理完善后发表于International journal of computer vision（IJCV）。截止2014年8月，该论文单篇被引次数达25000余次。(SIFT, namely Scale Invariant Feature Transform (Scale-invariant feature transform, SIFT), it is a description for image processing. This description has scale invariance, can detect critical point in the image, it is a local feature descriptor. [1] The method in 1999 by David Lowe [2] First published in the International Conference on Computer Vision (International Conference on Computer Vision, ICCV), 2004 was again perfect finishing after David Lowe published in the International journal of computer vision (IJCV) . As of August 2014, the paper cited the number of times a single article of more than 25,000 times.)

计算机视觉经典著作《计算机视觉中的多视图几何》配套代码(MATLAB and Octave Functions for Computer Vision and Image Processing)