基于5000系列的dsp的gpio控制工程 是过得很有用

基于拉haar小波的图像增强，与传统直方图均衡化相比较，效果更好。-Pull-based haar wavelet image enhancement, with the traditional histogram equalization compared to better.

OpenCV的特征比较和匹配，使用不同的方法

此软件是用在VB环境下开发的能够实现线段的编辑-This software is used in the VB development environment to realize the editorial line

显示图像渐变效果的一段小代码-graded results showed images of a small section of code

灰度直方图的统计对于一个图像来说是很重要的指标！-histogram statistics for the images is a very important indicator!

颜色变化的进度条控件Demo,有不足之处请大家指出，-Color changes with the process increases

丰富的图形类型（直线，平行四边形，矩形，多边形，圆角矩形，圆，椭圆，二次均匀B样条曲线，三次参数样条曲线，贝齐埃曲线）　　　　　　丰富的图形操作（属性设置，拖动，放大/缩小，删除，拷贝，旋转，图形切割，图形填充，编辑等）软件通用功能（撤消/恢复，打印，文件操作）架构较好，欢迎试用！-rich graphics types (linear, parallelogram, rectangle, polygon, rounded rectangle, circle, oval, secondary uniform B-spline curves, the three parameters spline curves, curve gemfibrozil Egypt) rich graphics operations (attribute settings, drag and enlarge/reduce, to delete, copy, rotate, graphics cutting, filling graphics, editing, etc.) common software functions (withdrawal/recovery, printing, and file manipulation) architecture better, welcomed the trial!

该程序使用了Bresenham算法的思想，将四个象限（考虑到斜率1一共八种情况）的问题通过交换端点的方法合并成一，四象限的问题。-draw line

利用processing2.1软件，做出信息可视化的一个theme river layout，来展示我国人口年龄与受教育程度的关系

基于神经网络的文字识别系统 本目录主要包括：文档和两个源代 其中源代码是识别程序的，另一个是矩阵类库的。已经保存了训练好的网络权值，所以第一次使用识别程序时，可以直接运行Release目录下的可执行文件，然后对图片目录中的测试图片进行读入、识别。当然也可以自行用训练样本训练网络，不过要注意训练样本的选择，否则可能识别率很低。训练样本选择的原则是，尽可能的有代表性，在训练时间不至于太长的情况下训练样本数目尽量多。-Based on neural network character recognition system catalog include: the two source documents and source code generation which is to identify the procedure, and the other is the matrix class library. Has been preserved trained network weights, so the identification procedures used for the first time, you can directly run the Release directory of the executable file, and then on the picture of the test directory to read into the picture, identification. Of course, we can use training samples to train the network, but attention should be paid to training samples, otherwise it may be very low recognition rate. Training sample selection principle is that, as far as possible have representation in the training time not too long a number of cases as many as possible training samples