041156300Medical3D

Implementations of image processing algorithms(This project can be built by Visual Studio 2012. Project description file included. Currently it is for Windows only.)

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.

由VC实现画矩形、椭圆和随手画功能，对图像处理人员会有帮助(paintings by VC rectangular, oval and hand painting functions, image processing staff to be helpful)

数字图像隐藏及提取研究分析论文、文献等。主要研究算法为LSB算法和DCT奇偶量化、DCT均值量化。(Digital Image Information Hiding and Extraction of analytical papers, literature. The main algorithm for the LSB algorithm and DCT parity quantization, DCT mean quantization.)

对图像（Bmp）的智能读入界面设计，源代码(Read image (BMP), intelligent interface design, source code)

利用位图图像中的冗余数据位，将小文件嵌入到大的图像文件中，并保持原图像不变。(The use of bitmap images in the redundant data bit will be embedded in small files to large image files, and to maintain the original image unchanged.)

通过GDAL读取常用遥感影像，如img，tiff，可实现读取显示和雷达影像处理的一些基本算法(Read through GDAL common remote sensing images, such as img, tiff, read the display and radar can achieve some of the basic image processing algorithms)

有关数字图像处理的一些源码，可以实现一些处理图像的功能，但要配合数字摄像机使用(Digital image processing of the number of source, you can achieve the functions of image processing, but with the use of digital cameras)

说明：  该程序实现的功能是：图像反色，线形变换，对于图像处理初学者非常实用。(The realization of the program)

采用了Harrtraining和离子滤波算法实现了拥挤人群人头计数，非常有用。VC++和Opencv开发，采用Oepncv中的Harrtraining训练一个分类器即可运行(Human detection and tracking in high density crowds is an unsolved problem. Standard preprocessing techniques such as background modeling fail when most of the scene is in motion. We integrate human detection and tracking into a single framework, and in- troduce a conrmation-by-classication method to estimate condence in a tracked trajectory, track humans through occlusions, and eliminate false positive detections.)