第 23 章 基于光流场的交通汽车检测跟踪

将图片通过灰度值转化为文字符号，并排列为原来图像的样式。 (Convert image by gray scale value into text symbols, side by side as the style of the original image. )

说明：  利用最大间类差，最优阈值，迭代阈值等方法实现图像分割(The maximum class difference, optimal threshold and iterative threshold are used to realize image segmentation)

使用kmeans算法对图像进行分割，先对图像进行了rgb到lab的转换(Use kmeans algorithm for image segmentation, the first image of rgb to lab conversion)

这是细胞边缘检测和找出细胞轮廓线的功能代码，可以很好的对细胞进行分割(This is the cell edge detection and contour lines to identify cell function code, a good division of cells)

SVM算法的线性分类，图像分类单幅遥感图像特征提取分类(Linear classification of SVM algorithm, image classification, feature extraction and classification of single remote sensing image)

BMP图像边缘提取后 根据边缘点集直线拟合(BMP image edge extraction fitting a straight line)

APG算法，加速近端临近点算法，通过对子问题的调整，计算速度很快(APG algorithm, accelerated near the proximal point algorithm, by adjusting the sub-problems, fast calculation)

图像 自动 标注 通过分析 训练集 ，学习 图像 和标注词 之间的关系，得出 之间的关系，得出 一些规则， 然后使用这些规则 自动 推导出 图像 最适合的标注词 ，这些标注词代表了图像的 ，这些标注词代表了图像的 高层 语义信息 。(Automatic image annotation by analyzing the training set, learning image and mark the relationship between words, the relationship between the draw, draw some rules, and then use these rules to automatically derive the most suitable image annotation words, these words represent the image annotation , these words represent the image annotation level semantic information.)

本程序可以实现对图像的中值滤波，从而除去图像中的某些干扰(This procedure can be achieved on the image median filter, thereby removing some of the interference image)

用SAE 深度学习方法做的高光谱图像分析的一个例子。里面有sae方法还有高光谱图像的特征提取过程。(One example of the depth of learning to do with SAE hyperspectral image analysis. There are sae method has hyperspectral image feature extraction process.)