代码原理、要求及工具箱

3D人脸建模过程中，重采样的重要一步，缝合面片间的缝隙。(3D face modeling process, an important step in resampling, suture surfaces between the cracks.)

功能很全的车牌识别系统，图像与处理，车牌定位，字符分割和字符识别等(Full license plate recognition system, image processing, license plate location, character segmentation and character recognition.)

基于SVD的DCT域和DWT域的经典水印算法，该算法通过提取水印水印的主成分并把该主成分嵌入到奇异值矩阵，鲁邦性非常高。(the principal components of the watermark are embedded into the host image in discrete cosine transform (DCT) and for the second method, those are embedded into the host image in discrete wavelets transform (DWT). To improve the robustness, the particle swarm optimization (PSO) is used for finding the suitable scaling factors. )

Face recognition system based on Eigenface, written in Matlab, including the paper which has present the structure and each component in detail. Hope it will be useful!

说明：  bp人工神经网络模式识别教程示例，研究人脸识别 手势识别有帮助(bp nervel network trainning and recognition ,e.g:face & dynamic hand gesture detection and face recognition .I hope it would be very useful for researcher !)

经过测试的基于sift程序的图像拼接。以lowe的程序为sift提取核心程序，此外还包括ransac算法，单应矩阵提取，图像融合（包括加权融合以及平均融合）算法，融合效果在testnew中可见。(Image mosaic based on sift program tested. The lowe program sift extract the core program, in addition to including ransac algorithm homography extract image fusion (including fusion weighted average fusion) algorithm, fusion effect is visible in testnew.)

用MTV230做OSD的实际项目源码。希望对您有帮助。(MTV230 do with the actual project OSD source. I hope for your help.)

本文主要介绍遗传算法的基本理论，叙述遗传算法在图像增强的的主要应用，即将原始图像变得更加清晰，特征变得更加明显。 现今关于图像增强的算法有很多，而这些算法大多是基于退化函数或者点扩展函数的知识进行图像处理的。当图像出现模糊或噪声影响大时，设计出的图像清晰化的效果肯定不够理想，因此有必要对图像进行增强处理。于是，可利用到遗传算法这种成熟稳定的仿生物进化的全局寻优算法，进行图像增强，由于遗传算法控制参数少、自适应度高，将选择该方法对图像退化分别进行不同的清晰化处理. 这样既增强了图像的对比度,又克服了传统直方图均衡化处理所造成的灰度级损失等缺点。 通过实验去表明，遗传算法从全局寻优的角度出发，能够较为精确地估计退化系统函数。和传统的线性增强、直方图均衡进行比较。实验结果表明该方法能改善原图像视觉效果，便于之后的图像分析。 (This paper mainly introduces the basic theory of genetic algorithm, genetic algorithm (ga) in the main application of image enhancement, the original image is more clear, characteristic becomes more obvious. Today about the image enhancement algorithm are many, and most of these algorithms is based on the degradation function or the knowledge of the point spread function of image processing. When the image appears fuzzy or noise influence, designed the image clarity of effect must not ideal, therefore it is necessary to enhance the image. Then, genetic algorithm is available to the mature and stable evolution of the global optimization algorithm, for image enhancement, due to less genetic algorithm control parameters, since the high fitness, will choose the method of image degradation, respectively, to manipulate the different motivation. It can enhance the image contrast, and overcome the traditional histogram equalization processing grayscale loss caused by the faults. Throug)

这里的图像增强包括灰度修正、平滑、滤波、锐化等，是图像处理的基本操作，本代码调试通过(Here, including gray-level correction image enhancement, smoothing, filtering, sharpening, etc., is the basic image processing operations, the code debugging by)

OTSU图像分割快速算法。原创，附参考文献。(a faster version of Otsu s method)