3253befc9e5c

使用改进的点锐度算法对不同程度的模糊图像进行清晰度评价。(The improved algorithm of point sharpness is used to evaluate the blurred image in different degree.)

一个很有用的SAR图片，可用于SAR图像的目标识别(A very useful SAR image can be used for target recognition SAR image)

用MATLAN实现了FLL（锁频环）的热噪声误差、动态应力误差、总的跟踪误差与带宽、预检积分时间等的关系图(MATLAN achieve FLL (frequency-locked loop) thermal noise error and dynamic stress error, the total tracking error and bandwidth, preflight integration time diagram)

说明：  边缘特征的提取就是求图像梯度的局部最大值和方向。实际计算中，以微分算子的形式表示，并采用快速卷积函数来实现。常用的算子有微分算子，拉普拉斯算子，Canny算子等。其中Canny边缘检测是一种较新的边缘检测算子，具有较好的边缘检测性能，得到越来越广泛的应用。Canny边缘检测法利用高斯函数的一阶微分，它能在噪声抑制和边缘检测之间取得较好的平衡(Edge feature extraction is to seek the local maximum of image gradient and orientation. The actual calculation to the form of differential operator representation, and using fast convolution function to achieve. Commonly used operators are differential operators, Laplace operator, Canny operator and so on. Canny edge detection which is a relatively new edge detection operator, and has good edge detection performance, get more and more widely used. Canny edge detection method using first derivative of Gaussian function, it can in the noise suppression and edge detection to achieve a better balance between)

说明：  遥感信息工程学院遥感原理与应用课程实习监督分类(Supervised of Remote Sensing Principle and Application Course Practice)

sift和surf特征点提取，然后进行配准，通过ransac进行提纯(Sift and surf feature points are extracted, then registered and purified by RANSAC)

C# SCADA源码修复，完全可用

迭代阈值方法进行图象分割的函数,有测试程序和图片(threshold iterative method of image segmentation function, test procedures and Photo)

利用遗传算法进行图像分割的源程序.基于matlab平台,适合于一维和二维分割.(using genetic algorithms for image segmentation of the source. Matlab-based platform, in a fit- and two-dimensional segmentation.)

说明：  使用pca方法对图像进行特征提取，对训练集的20个人的共一百张人脸进行训练，使用adaboost算法生成强分类器，可以对测试集的人脸图片进行识别，且识别率较高(The PCA method is used to extract the features of the image, and the training is carried out for a total of 100 faces of 20 people in the training set. The AdaBoost algorithm is used to generate a strong classifier, which can recognize the face images in the test set with a high recognition rate)