flash site, often used in navigation bar, here to provide some very fine example...

在labview图形化编程软件中利用vision中的工具来做图像处理，这些例子对大家一定有用。-in LabVIEW graphical programming software used vision tools to do the image processing, these examples are certainly useful to everyone.

细化算法，正在做毕业设计的同学可以参考一下，有较高的参考价值-Thinning algorithm, is designed to do graduate students for reference, have a high reference value

RGB24向RGB15的转换-RGB24 to RGB15 conversion

sift的c++算法，不是基于opencv的，我自己没有看懂。-sift the c++ algorithm, is not based on the opencv, as I do not understand.

在内存中位图的对称位图(镜像)(8KB)。此程序是老外写的经典程序，可以拜读一下。-in memory bitmap symmetric Bitmap (mirrored) (8KB). This procedure is a foreigner to write the classic procedure could read about.

应用于计算机视觉领域，拍摄等像素大小的两张图片，其中一张是细节图，我们可以将我们关注的这张细节图（拍摄得清楚）‘粘贴’到大图中，起到细化局部的作用-Applied to computer vision field, shooting and other pixel size of two images, one of which details of the map, we can be of concern to us the details of this map (taken a very clear) " paste" into the big picture, the play of local refinement role

图像分割常用的主动轮廓模型中的一个经典算法――贪心算法的C++实现-Image segmentation of the active contour model commonly used in a classic algorithms- greedy algorithm C++ Achieve

SIFT特征点检测算法是一种有效的图像特征检测法，对旋转、尺度，光照变化都具有一定的鲁棒性。-SIFT feature detection algorithm is an effective method of image features, rotate, scale, the light changes robustness。

1>图像处理系列之一－－读写篇 涵盖内容： 1>图像处理简介 2>常用图像文件格式说明 3>BMP图像文件读写 涉及VC知识主要有： 1>消息添加 2>文件读写 3>串行化 实验环境： Win+VC6.0 实验图片在Imagetest子目录下Data文件夹中.-1

：由于许多传统的去噪方法在强背景噪声情况下提取声音信号的能力变弱甚至失效， 提出 应用独立成分分析( I C A) 方法对声音信号进行特征提取， 并证明了这种 I C A 变换能增强语音和音 乐信号的超高斯性． 在此基础上， 应用 I C A基函数作为滤波器， 通过阈值化的去噪方法对含有强高 斯背景噪声的声音信号进行去噪仿真实验． 结果表明， 本方法明显优于传统的均值滤波和小波去噪 方法， 为强背景噪声下弱信号的检测提供 了新的途径．-: As many of the traditional de-noising method in case of strong background noise, the ability to extract the voice signal even weaker failure, the application of independent component analysis (ICA) method of voice signal feature extraction, and prove that this transformation can be enhanced voice ICA and music of super-Gaussian signals. On this basis, the application of ICA basis function as a filter, through the threshold of the denoising method of Gaussian background noise contains strong voice signal denoising simulation. The results show that this method is obviously superior to the traditional mean filtering and wavelet denoising methods for the strong background noise under the weak signal detection provides a new way.