information_fusion

说明：  基于高斯混合模型和隐马尔科夫模型的图像分割算法(Image segmentation algorithm based on Gaussian mixture model and hidden Markov model)

利用小波变换的去噪程序！和中值滤波进行对比！(The use of wavelet transform denoising procedure! And median filtering comparison!)

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)

meanshift算法的车辆跟踪，效果非常好，改进版的，希望对大家有用(Meanshift algorithm of vehicle tracking, the effect is very good, improved version of the hope of the useful)

说明：  matlab函数进行图像轮廓处理，处理效果还是可以的。(matlab function detect the picture and scale the picture)

背景差分法运动目标检测，建立背景模型，然后使用当前模型与背景模型相减，再和阈值相比较，比阈值大的即判断为有运动目标，比阈值小的即判断为没有运动目标，阈值大小可以自己适当调试，根据经验值设置最佳阈值。(Background difference method is used for moving target detection and setting up background model, then using current model and background model subtracting, and comparing with threshold, it is judged to have moving target compared with the threshold value, which is smaller than threshold. The size of threshold can be adjusted properly, and the best threshold is set according to experience value.)

本代码使用引导滤波器去除图像中的雨滴 在对雨雪天气图像进行清晰化处理之前，要对其中的动态成分进行自动辨识和去除。快速有效的自动辨识及去除方法是构建自适应多种低能见度条件视觉增强算法的前提条件(This code USES the guided filter to remove image in the rain Before the clarifications in rain and snow weather image processing, to the dynamic content of the automatic identification and removal.Quick and efficient automatic identification and removal method is to construct the adaptive multiple visual enhancement algorithm is the premise of low visibility conditions)

image segmentation with ICM algoritm

今年刚更新的最新的工具箱SaliencyToolbox2.3.zip，做图像处理视觉注意力机制相关研究必备的工具。(This year has just updated the latest toolbox SaliencyToolbox2.3.zip, do image processing of visual attention mechanisms related research is an essential tool.)

基于条件迭代法与马尔可夫随机场的遥感图像分割的matlab代码(the image segmentation based on the ICM and Markov random field)