MRI_seg

1.能够对简单瑕疵点进行检测：正常线、断线、重线、跳线、平纹面料、平纹面料疵点、水印疵点、染色疵点 能够对不同的纹理布匹进行检测：平纹、斜纹等(翻译 1. Be able to perform simple point defect detection: the normal lines, broken, heavy lines, jumpers, plain weave, plain weave fabric faults, watermark defects, dyeing defects Able to detect different textures of cloth: plain weave, twill)

内容包括高斯混合概率假设密度滤波器仿真程序，GM-PHD滤波器提供了一种PHD滤波器工程化的方法，在无数据关联的情况下进行目标跟踪。(This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters.)

John Wiley & Sons - JPEG2000 Standard for Image Compression Concepts Algorithms and VLSI Architectures 2005(John Wiley )

说明：  labview基础视觉识别的东西，讲的很细(The basic visual recognition course of LabVIEW is very detailed.)

圆阵常规波束形成与MVDR用于方位估计的比较，可以更改信噪比仿真两者的区别联系(uniform circle array based for estimating direct of arrival,CBF and MVDR compared with adjustable SNR)

说明：  基于脉冲耦合神经网络的图像分割，脉冲耦合神经网络是近年研究的热点。(Based on Pulse Coupled Neural Network for Image Segmentation, pulse coupled neural network is the research hotspot in recent years.)

基于蚁群算法的图像边缘检测，将蚁群算法应用于边缘检测,提出了一种图像边缘检测的新方法，具有很大的创新性，最主要是可以仿真，供本研究方向的人参考( Image Edge Detection based on ant colony algorithm)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

运用HOG提取人脸特征与支持向量机算法训练模型，进行人脸识别(Using HOG to extract facial features and support vector machine algorithm training model for face recognition)

进行简单的数字图像处理，用MATLAB进行图像旋转，灰度，亮度调节(Perform simple digital image processing)