20 基于帧间差法进行视频目标检测

利用几种阈值法从给定的图像中分割出目标，去除背景(Use several thresholds to segment the target a given image and remove the background)

sift特征点检测及图像拼接，matlab7.0实现的，我从别人那里整过来的，测试的话，用在matlab里面直接调用match( b1.jpg , b2.jpg )即可，我测试成功，里面也有说明文档，为了科研，共享，希望大家有好的代码都0分共享，与己方便，与人方便，中国科研才会发展更迅速(sift feature point detection and image stitching of, matlab7.0 realized, I come from others where the whole test, used in matlab inside direct calls to match (' b1.jpg' , ' b2.jpg' ) can I test success, which also has documentation, in order to research, share, and hope everyone has a good code sharing, and has convenient, and accommodating, the more rapid development of China' s scientific research will)

对于叶片面积的计算，进行图像预处理，灰度转换、直方图、去噪声等，最终计算出叶片面积的像素和。(For the calculation of leaf area, image pre-processing, gray-scale conversion, histogram, to noise, etc., and ultimately calculate the leaf area and the pixels.)

提取GIST特征的matlab实现源码，初学者实用(Extract GIST features matlab achieve source beginners practical)

这是关于农业方面的遥感SAR图像数据，做SAR处理的可以下载使用(This is the SAR remote sensing image data on agriculture, do SAR processing can download)

八种图像置乱方法：希尔伯特变换、采样置乱、ZIGZIG置乱、等等等等(Eight kinds of image scrambling method)

可以对一幅图像进行Arnold进行置乱加密，另外还有解密的程序(Arnold can be carried out on an image scrambling encryption, decryption program in addition to)

说明：  SAR合成孔径雷达的点目标仿真报告，含matlab代码(SAR synthetic aperture radar point target simulation report, including Matlab code)

数字图像处理中的平滑和锐化（边缘检测）。包括1、添加椒盐、高斯噪声。2对噪声污染的图像分别使用邻域平均法、中值滤波法、K邻近平均法进行平滑。3对一幅图像利用Roberts梯度法、Sobel算子和拉普拉斯算子进行锐化，并比较结果。附处理源图像和处理结果截图。 (Digital image processing smoothing and sharpening (edge detection). Including 1, add salt and pepper, Gaussian noise. Two pairs of images were used noise pollution neighborhood average, median filtering, K close to the average method to smooth. 3 on an image using Roberts gradient method, Sobel operator and Laplacian sharpening, and compare the results. Laws dealing with the source image capture and processing results.)

说明：  用于图像去噪处理Tikhonov模型，仅供用于学习参考(Tikhonov model for image denoising)