LBP

利用SFS算法实现3D图像重建，附带有图片实现(3D Image Reconstruction Using SFS Algorithms)

说明：  八值图像连通区域标记，为每个连通区域分配一个唯一的标号，处理后的图像按照从左到右，从上到下的顺序获得连续的标号(Binary image connected region eight marks, for each connected region a unique label distribution deal with in accordance with the image from left to right, from top to bottom order of access to continuous labeling)

实现一个高精度指针仪表读数自动识别系统．用数码摄像头采集仪表的图像，然后利用数字图像处理技术识别刻度和指针，根据指针和刻度的相对位置计算指针读数值．使用条件霍夫变换（Constrained HOUGH Transfer）和中心投影分析法实现刻度的全自动识别，采用快速中心投影法进行指针检测与识别，识别速度达到68ms．实验结果表明该方法比传统方法速度快、精度高，系统读数平均误差仅为0．016％(To achieve a high precision automatic identification system pointer meter reading. Instrument with a digital camera capture images, then using digital image processing technology to identify scale and pointer, pointer and scale according to the relative position of the pointer readings calculated. Use Hough transform (Constrained HOUGH Transfer) and center projection analysis method to achieve scale automatic identification method using fast center projection target detection and recognition, recognition speed of 68ms. Experimental results show that faster than traditional methods, high accuracy, the system average error of only 0.016 of reading)

检测视频中的运动目标以及阴影，并消除阴影，基于opencv，将RGB颜色空间根据一定的转换关系转换到HSV空间，最后消除阴影， 可以在vc6.0和vs2008上正常运行(Shadow detection algorithm based on HSV color space)

用CPHD的方法实现多目标跟踪，及对CPHD性能的检测。(With CPHD method to achieve multi-target tracking, and performance testing for CPHD.)

说明：  matlab绘制，非线性分叉图，非线性动力学(Matlab drawing, nonlinear bifurcation diagram, nonlinear dynamics)

抽取子图像: 通过一个随机混沌标签序列从原图像中抽取 对子图像加密:小波变换 ,在高频部分加密 重构原图，把加密好的子图像重新放回到原图像中 (from sub-images : a random chaotic sequence label from the original image from a tradition image encryption : wavelet transform, the high-frequency part of remodeling the maximum encryption, encrypted good son to re-up images back to the original image)

数字图像处理第三版，中文版，PDF文档，还算清晰(Chinese version of digital image processing)

SURF算法作为一种新近出现的特征提取方法，在重复度、独特性、鲁棒性3个方面，均超越或接近以往提出的同类方法，并在计算效率上具有明显的优势。本代码采用SURF算法检测图像并进行坐标变换与图像拼接。 采用SURF算法对图像进行检测，其主要是用Hessian矩阵对图像进行检测，对图像的特征提取之后找到图像的特征点。之后采用最近临快速匹配（NN）、随机抽样一致性（RANSAC）算法和最小二乘法参数优化（LM）对特征点进行提纯匹配。最后在两幅图像中进行坐标变换，达到统一坐标系和图像拼接的效果。 (SURF（Speeded Up Robust Features） as a method of feature extraction which newly appeared is over or nearly previous method on duplication, uniqueness, and robustness and have a clear advantage on computational efficiency. This code uses the SURF and coordinate transformation algorithm to detect image and image matching. This code uses the SURF algorithm of image detection, the main is to use the Hessian matrix of image for testing, to find the image after image feature extraction of feature points. After we used Nearest Neighbor (NN), Random Sample Consensus (RANSAC) algorithm and least square parameter optimization for purification of matching feature points. Coordinate transformation in the last two images, to coordinate system to achieve the same image stitching. )

高斯混合模型的源代码。从文件中读取数据，用三个高斯混合模型进行处理(Gaussian mixture model of the source code. Read from the file data, using three Gaussian mixture model for processing)