提出了基于边缘特征的亚像素投影配准算法,法引入新的图像相似性评价函数 ― 均方误差函数(Mean Sequare Er ron ,MSE)...

ransac，随机抽样一致性算法，应用在图像处理特征点匹配中。-ransac, random sample of the consistency algorithm used in image processing feature point matching.

此源码,对图像的自动阈值,与局部阈值是很有效果,希望对大家有所帮助-This source of images automatically threshold, and the local threshold is very effect, and they hope to help everyone

用于高维数据的检索或最临近索搜索 高维数据处理一直是计算机数据挖掘, 图像检索方向的难点, 该算法是一种近似最临近方法, 效率提高比较多的-For high-dimensional data retrieval or the cable near the search deal with high dimensional data has always been a computer data mining, image retrieval difficult direction, the algorithm is a method similar to the most close to, improve the efficiency of a relatively large number of

数字图像处理编程源代码，对做数字图像处理的会有帮助。-Making draw program about in Visual C++.

包含一些常见的遥感图象处理的算法,有中值滤波,拉普拉斯算法等-Contains a number of common remote sensing image processing algorithms, have median filter, Laplacian algorithm

c语言编程风格和推荐标准，是一本有用的c语言编程参考书-c language programming style and the recommended standard, is a useful reference book c language programming

一个超强悍的数字图象处理程序，包括几乎所有常用的处理功能．-A super-powerful digital image processing procedures, including almost all commonly used functions deal.

RGB与YUV格式的互相转换, 支持YUV420、YUV444、YUV4-RGB and YUV format conversion with each other to support the YUV420, YUV444, YUV422

These are the codes in "A note on two-dimensional linear discrimant analysis", Pattern Recognition Letter In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem on the other hand, we compare several distance measures when the feature matrices and feature vectors are adopted.

使用互信息进行2D图像自动配准源代码,应用平均互信息-The use of mutual information for automatic registration 2D image source code, the application of the average mutual information