dianziwenxiang

打开一个图像或者视频文件，相应的轮廓检测功能，视频运动物体的跟踪功能(applications on image，tracking objects in the video)

图像增强，包括图像的平滑、锐化、边缘识别等操作(Image enhancement, including image smoothing, sharpening, edge detection and other operations)

机器视觉,计算机视觉,人脸识别,形态学,图像采集,压缩编码,数字水印,神经网络,人工智能,模式识别,特征提取,图像检索,视频检索,计算机图形学(machine vision, computer vision, face recognition, morphology, image acquisition, compression, digital watermarking, neural network, artificial intelligence, pattern recognition, feature extraction, image retrieval, video retrieval, computer graphics)

VC development motion control routines, vb development 12 axis motion control routines

这个程序demo了怎样使用glCopyToTexture函数绘制。这个纹理是应用在一个立方体上的。(This app demonstrates how to render to a texture using the glCopyToTexture function. I have used one of my old projects to create a interesting effect (Blending colors). This is rendered to a viewport and then copied to a texture. The texture is then applied to a cube.)

这是一个应用VC++编写的基于摄像头读取人脸的人脸识别和人脸检测软件，其中引用了MATLAB的系统函数，是一个非常不错的人脸识别软件，这个系统在我校论文答辩审核中获得了优秀的成绩。希望对大家有用(This is an application-based VC prepared to read camera Face to Face Recognition and face detection software. cited in the MATLAB system function, is a very good face recognition software. The system in my school examination papers reply obtained outstanding achievements. We hope to useful)

实现用帧差法画质心轨迹 高斯背景建模 可以运行的程序 opencv(Picture frame difference method implemented by the loci of Gaussian background modeling)

1.打开左右两张影像2.Harris算法提取同名点3.显示提取同名点（若要进行下一步匹配则不建议显示，这里只是用于查看是否提取出）4.相关系数匹配（只在找出的同名点内匹配）5.LSM最小二乘匹配（先进行粗匹配，再精匹配）(1 Open the left and right images 2.Harris algorithm to extract two tie points 3 shows the extraction of corresponding points (not recommended to proceed to the next match show, here is used to see if extracted) 4. Correlation coefficient matching (just looking match within a point of the same name) 5.LSM least squares matching (rough first match, then fine match))

Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.

图像分割，完整程序，验证可行。新手上传，望支持。(image direct)