200719930586404

说明：  应用条件随机场进行图像分割 使用方法：将pydensecrf-py27或pydensecrf-py35重命名为pydensecrf，放入C:Program FilesAnaconda3Libsite-packages 运行：python inference.py im1.png anno1.png out.png(Renameing the file "pydensecrf-py27" or "pydensecrf-py35" to the new name "pydensecrf" Puting this file "pydensecrf" in this directory "C:Program FilesAnaconda3Libsite-packages" cd examples/ python inference.py im1.png anno1.png out.png)

该源码为目标检测和跟踪算法 包括： 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 (The source code for target detection and tracking algorithm include: 1 Static background background prediction target detection 2 Static background frame difference method for target detection 3. Mean Shift tracking method 4 multi-target tracking methods focus)

说明：  四种点云配准算法。FPFH,NDT,3DS…(Four point cloud registration algorithms.FPFH,NDT,3DS.)

单尺度Retinex算法,ssr算法，matlab程序(Single scale Retinex algorithm, SSR algorithm, matlab program)

实现DCT变换和FFT变换的图像压缩， 比较两者性能的差异(The DCT transform and FFT transform image compression)

利用数字图像处理(matlab第二版)中已学过的知识，进行各种图像加噪，去噪处理，复原，增强，以及彩色图像分割处理，(The use of digital image processing (matlab second edition) has been learned in the knowledge, a variety of image noise, denoising, restoration, enhancement, and color image segmentation,)

说明：  用VC开发的图形填充算法，简单实用， 希望对你的图形开发有所帮助， 如果有什么问题，可以与我联系(development of the VC graphics filling algorithm, simple and practical, and I hope to your graphics development will help, if any, can contact me)

彩色位图转黑白位图代码。何东健《数字图像处理》随书光盘提供的源码。(color bitmap to black-and-white bitmap code. Dong- Jian "Digital Image Processing" With the CD source.)

三维激光点云配准是点云三维建模的关键问题之一。经典的 ICP 算法对点云初始位置要求较高且配准 效率较低，提出了一种改进的 ICP 点云配准算法。该算法首先利用主成分分析法实现点云的初始配准，获得较好 的点云初始位置，然后在经典 ICP 算法的基础上，采用 k － d tree 结构实现加速搜索，并利用方向向量夹角阈值去除 错误点对，提高算法的效率。实验表明，本算法流程在保证配准精度的前提下，显著提高了配准效率。 (Three-dimensional laser point cloud registration is one of the key three-dimensional point cloud model. High classical ICP algorithm to the initial position of the point cloud registration requirements and low efficiency, proposed an improved ICP point cloud registration algorithm. Firstly, the use of principal component analysis of the initial point cloud registration, get a better initial position of the point cloud, then the basis of classical ICP algorithm using k- d tree structure to achieve speed up the search, and using the direction vector angle the removal of the threshold point error and improve the efficiency of the algorithm. Experiments show that the algorithm processes to ensure the accuracy of registration under the premise, significantly improve the efficiency of registration.)

首先对指纹图像进行预处理，包括图像的分割，增强，二值化，细化。对细化后的图像进行特征点的提取，最后通过细节特征匹配完成指纹的识别。(Firstly, the fingerprint image is preprocessed, including image segmentation, enhancement, binarization and thinning. The refined image is extracted by feature points, and the fingerprint recognition is completed by minutiae feature matching.)