templateMatcher

这是基于Tent映射粒子群的交叉熵阈值分割程序。(This is code of method based on Tent particle swarm optimization and cross-entropy.)

本程序实现了对指纹图像的预处理、特征提取，效果很好，有图有程序，可以直接使用。(This procedure to achieve the fingerprint image preprocessing, feature extraction, the effect is very good, there are plans to have procedures can be used directly.)

基于稀疏字典和稀疏编码的图像去噪算法，基于低秩约束的高光谱条纹噪声去除，包含论文及代码(Based on sparse dictionary and sparse coding image denoising algorithm, based on low rank constraints of hyperspectral fringe noise removal, including papers and code)

迭代CT重建血管，希望对大家能够有所帮助(Iterative CT reconstruction of blood vessels, we want to be able to help)

说明：  模板匹配，能够准确的找出与所给图像一致的目标。(Template matching, the ability to accurately identify the image with the given goal.)

This matlab code is used to encode and decode digital still images into Jpeg2000 format

提取sift特征，通过K均值聚类形成特征包，进行图像检索。(SIFT features are extracted and image packets are retrieved through K mean clustering.)

背景建模和前景分割的方式把运动车辆提取出来。并进行最近临关联，输出目标轨迹。 MeanShift运动目标跟踪 matlab程序 1.截取跟踪目标矩阵rect 2.求取跟踪目标的加权直方图hist1 3.读取视频序列中的一帧， 先随机取一块与rect等大的矩形，计算加权直方图hist2。 4.计算两者比重函数，如果后者差距过大， 更新新的矩阵中心Y，进行迭代（MeanShift是一种变步长可以迅速接近概率密度峰值的方法），直至一定条件后停止。 这两种算法为处理图像跟踪的基本方法 (Background modeling and foreground segmentation the extracted moving vehicles. Pro associated output and the target locus. Target tracking matlab program MeanShift movement. Interception tracking the target matrix rect strike a tracking target weighted histogram hist1 3. Reads a video sequence, randomly take a with the rect large rectangular, calculate the weighted histogram hist2. 4. Calculate both the proportion of function, if the latter gap is too large to update the new center of the matrix Y conducted the iteration (MeanShift is a variable step size can be rapidly approaching the peak of the probability density) until certain conditions to stop. The basic method of these two algorithms for processing the image tracking)

用matlab编写的区域生长法的程序，通过选择种子和阈值，可以进行图像分割和检查瑕疵(Written by matlab region growing process, by selecting the seeds and thresholds for image segmentation and inspection for defects)

Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.