读TIFF

【实例简介】

CBCT图像fdk重建,预投影、投影滤波、重建演示，功能强大(it s about the fdk rec for CBCT，forward projection、filter projection、rec demo)

模糊聚类的图像分割,用matlab语言编写实现模糊聚类的图象分割。(Fuzzy clustering image segmentation, matlab language used to achieve fuzzy clustering image segmentation.)

基于surf的图像复制篡改检测matlab实现(surf image detect matlab)

N-FINDR算法，高光谱图像无先验知识下的端元提取算法，最后计算每种端元的丰度(N-FINDR algorithm for hyperspectral image without prior knowledge of endmember extraction, and calculate the abundance)

运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。(Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection.)

（1）计算图像中每个像素点的LBP模式(等价模式,或者旋转不变+等价模式)。 （2）然后计算每个cell的LBP特征值直方图，然后对该直方图进行归一化处理(每个cell中，对于每个bin,h[i]/=sum,sum就是一副图像中所有等价类的个数)。 （3）最后将得到的每个cell的统计直方图进行连接成为一个特征向量，也就是整幅图的LBP纹理特征向量； 然后便可利用SVM或者其他机器学习算法进行分类识别了。((1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode). (2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images). (3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture. Then, SVM or other machine learning algorithms can be used for classification and recognition.)

说明：  基于surf算法的复制移动检测，此方法适合图像伪造研究者初级实验(Replication mobile detection based on surf algorithm)

视频图像序列中分的运动目标检测matlab源码，分割运动目标(carve the moving target detection Matlab source, Moving Target Segmentation)

区域生长法的图像分割，可以较好地提取脑部图像。(The region growing method can extract brain image well.)