templateMatcher

圆心检测算法，使用matlab根据sci文章仿写，在图像质量不高的情况下检测效率较高。(Center of a circle detection algorithm, the use of matlab under the sci article仿写, image quality is not high in the case of a higher detection efficiency.)

滑窗计算刀具磨损量，拟合刀具边界，自动计算磨损量(sliding window based tool wear calculation)

基于PCNN脉冲耦合神经网络的图像增强算法，改进了PCNN基本算法，相比传统图像增强算法更有效(Image enhancement algorithm based on PCNN)

说明：  快速磁共振成像领域的GRAPPA（generalized auto-calibrating partially parallel acquisitions）算法实现(GRAPPA realization in fast MRI)

实现不规则形状碎纸片的拼接，并展示出原图像(The splicing of irregular shaped pieces of paper is realized and the original image is displayed.)

指纹灰度图像的matlab代码，文件附带指纹图像(Gray-scale fingerprint image matlab code, documents attached to the fingerprint image)

Visual Tracking via Locality Sensitive Histograms.基于局部敏感直方图的目标跟踪算法，该算法在CVPR2013中出现，算法较简单但却非常有效，值得各位研究视觉跟踪算法的同行研究(Visual Tracking via Locality Sensitive Histograms)

说明：   针对雾天下拍摄图像的退化现象，远景部分是一块灰度分布比较集中且接近天空亮度的区域，而近景部分可以分辨，其灰度分布比较分散。提出了一种基于遗传算法阈值分割的雾天图像增强方法；该方法首先用遗传算法求出近远景的分割阈值将图像分割[1]出来，然后对两个部分分别进行清晰化处理。(make the picture clear)

图像检测的算法，Faster R-CNN算法，先对整张图像进行卷积计算，然后通过感兴趣区域池化层(RoI Pooling Layer)将选择性搜索算法推荐出来的候选区域和卷积网络计算出的特征映射图进行融合，得到候选区域对应的特征矢量，这种共享卷积计算的操作极大地减少了卷积计算的次数。而且这些特征矢量的维度统一，方便后续的分类工作。通过感兴趣区域池化层处理卷积特征，并将得到的特征送往两个并行计算任务进行训练，分类和定位回归。通过这些方法和改进的框架，Fast R-CNN 用更短的训练和测试时长，取得了比 R-CNN 更好的效果(Faster R-CNN algorithm first convolutes the whole image, then fuses the candidate regions recommended by the selective search algorithm and the feature mapping maps calculated by the convolution network through the RoI Pooling Layer to get the corresponding feature vectors of the candidate regions, which greatly reduces the number of convolution calculations. Moreover, the dimension of these feature vectors is unified, which facilitates the subsequent classification work. The convolution feature is processed by the pooling layer of the region of interest, and the obtained feature is sent to two parallel computing tasks for training, classification and positioning regression. Through these methods and improved framework, Fast R-CNN uses shorter training and testing time and achieves better results than R-CNN.)

数字图像处理图像复原算法源代码（维纳滤波，最小二乘方法，L-R算法）(Digital image processing, image restoration algorithm source code (Wiener filtering, least squares method, the LR algorithm))