matlabtuxiangfenge

CS-LBP本人毕设设计所做，源代码都是自己写的，可用(CS-LBP I completed the set design done, write your own source code is available)

用matlab画几个六边形。。。。。。。。。。。。。(Draw a few hexagons with MATLAB)

说明：  通过多尺度分解，实现医学图像的图像融合，达到更好的效果(Through multi-scale decomposition, medical image fusion can be achieved to achieve better results.)

说明：  也是水平集方法的一个matlab源代码 基于边缘信息的(level is a collection of source code based on Matlab Edge Information)

自行设计的概率密度函数统计方法，代替ksdensity函数(Probability density function of self-designed statistical methods, instead of ksdensity function)

光流（运动估计）经典算法Horn and Schunck算法MATLAB程序(optical flow (Motion Estimation) classical algorithm Horn and Schunck algorithm MATLAB)

二维图像视觉显著性检测，效果非常好。California Institute of Technology的 Jonathan Harel写的，很经典。(Two-dimensional image saliency detection, the effect was very good. California Institute of Technology' s Jonathan Harel written, very classic.)

AForge.NET-sample

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

边缘检测法和阈值分割法，是图像分割技术应用中两项重要的方法。本文介绍了图像边缘检测和阈值分割的一般原理、典型算法以及研究现状。本文针对边缘检测算子和几种阈值分割技术进行了详细讨论。针对火柴根数统计和钢筋根数统计两个实验，在详细分析检测原理的基础上，编写了相应的实验并给出了实验结果。(Edge detection and thresholding method, image segmentation is an important technology in two ways. This article describes the general principles of image edge detection and thresholding, the typical algorithms and research status. In this paper, several edge detection operator and thresholding techniques are discussed in detail. Statistics for the number of matches and reinforced the number two experimental statistics, based on a detailed analysis of the detection principle, write the corresponding experimental and experimental results are given.)