信号检测与估计理论

图像处理标准测试图片库，用于数字图像处理各种仿真实验。(Standard test image gallery image processing to digital image processing all kinds of simulation experiments.)

从原始图像产生噪声图像，使用db1执行2层小波，使用wpbmpen进行全阈值选择，采用硬阈值和软阈值处理方式,保存低频,进行图像降噪，并绘制各分解系数图像。(Generated noise image from the original image, using db1 wavelet perform two layers using wpbmpen full-threshold selection, using hard threshold and soft threshold approach, saving low frequency, image noise reduction, and draw the decomposition coefficients images.)

运用matlab在遥感图象上运用神经网络方法进行图像分类(Using matlab to classify remote sensing images using neural network method)

说明：  特征选择算法，防止分类结果出现过拟合，提前对多维的特征向量进行选择筛选(A feature selection algorithm)

说明：  用于图像处理的图像降噪领域，有需要的可以自己进行下载。(In the field of image noise reduction for image processing, you can download it yourself if necessary.)

用VC++视觉检测程序，可以打开PCB板的照片文件，对其进行编辑、点运算、几何变换、正交变换、图像增强、形态变换、边缘与轮 廓检测、图像分析与复原、图像编码等功能。其中点运算包括图像反色、线性变换、阀值变换等功能；边缘检测包括Robert算子、 Sobel算子、Gauss-Laplace算子等；正交变换包括傅里叶变换、离散余弦变换、沃尔什变换；图像编码包括哈弗曼编码表、香农弗 曼编码表、行程编码、LZW编码等。 对于研究电子封装技术，视觉检测及电机精确控制的同学学习参考使用。(visual detecting for PCB.rar)

图像处理模式识别一种分类算法：svm，对于所提取的图像的特征进行训练学习然后分类。(Image processing, pattern recognition of a classification algorithm: SVM trained to learn the characteristics of the extracted image and then classified.)

基于SUSAN特征检测算子的边缘提取，添加图片即可运行，可自行根据图片特征设置参数得到较好的边缘检测效果(Edge detection feature extraction SUSAN operator, add images to run, you can set your own parameters to get a better edge detection based on image characteristics)

基于knn算法的遥感图像分类 源代码 算法测试(Remote sensing image classification based on knn algorithm Source code Algorithm testing)

手持式摄像机在使用时常常会受到使用者有意无意抖动的影响,从而影响成像效果，造成录制视频的不稳定及跳动问题，尤其是在使用者在一场景中特写或者跟踪某一具体目标时，使用者通常不能准确定位到或者估计出运动目标的位置，从而造成目标在视频中位置的不稳定，造成视频的主观效果变得不理想。 为了解决这一问题，我们需要设计一种算法来识别这种无意义的运动并设法通过补偿的方式来使得场景中的目标物体保持位置稳定的状态。 手持式摄像机捕获的视频通常都会受到抖动的影响，这严重的影响视频的主观效果。 这里提出的算法可以可靠的用于数字视频的去抖动。这个算法通过识别这些意外的抖动并且利用运动补偿的方法来获得一个较好的视频输出。 这个系统可以分为三个模块：（1）运动估计模块；（2）抖动识别模块；（3）运动补偿模块。 注：代码在matlab 7.7.0(R2008b)运行通过 [1] F Vella, A Castorina, M Mancuso. Digital image stabilization by adaptive block motion vectors filtering, IEEE Transactions on Consumer Electronics, 2002(Handheld cameras are often used by the user intentionally or unintentionally, the influence of jitter affect the imaging results, resulting in instability and beat the problem of recording video, and especially in the user in a scene close-up or tracking a specific target,users often can not accurately navigate to estimate the location of the moving target, resulting in the subjective effects of target location of the instability in the video, resulting in video not ideal. In order to solve this problem, we need to design an algorithm to identify such a meaningless exercise and try to make the object in the scene to keep the position stable state by way of compensation. The handheld camera captured video usually subject to the influence of jitter, the subjective effect of which seriously affect the video. The algorithm proposed here can be reliably used for digital video to jitter. This algorithm by identifying the jitter of these accidents and the use of motion compensation m)