VesselTest

求图像峰值信噪比，可以用于图像质量的评估。(Peak signal to noise ratio for images that can be used for image quality assessment.)

数据库是由10个人的7种正面表情组成的213幅灰度图像，图像是以大小为256256的8位灰度级存储的，格式为.tiff型，平均每个人每种表情有2到4张。( the JAFFE database[11] which is composed of 213 images of female facial expression corresponding to 10 distinct subjects. Each image is stored at a resolution of 256×256 pixels and 8-bit gray level. Each subject in the database is represented with 7 categories of expression (angry, disgust, fear, neutral, sadness, happiness and surprise). )

这是一个数字图像处理的程序，能够实现目标检测与跟踪，事项了EM算法、是对原有的算法改进。(This is a program of digital image processing, target detection and tracking can be achieved, the matter of the EM algorithm, the improved algorithm is the original.)

介绍了DAB 接收机中的OFDM 同步算法，重点对OFDM 接收机方案中基于保护间隔的符号定时估计、频 率偏移估计进行了分析，提出了适用于Eureka147DAB 系统的同步算法；并对各种算法进行了计算机仿真，本文介绍的算法满 足DAB 接收机的要求，有很大的应用价值。(Introduced the DAB the OFDM receiver synchronization algorithms, focusing on OFDM receiver scheme based on the protection of the interval estimation, frequency symbol timing Rate migration estimates are analyzed and some appropriate Eureka147DAB system in the synchronization algorithms And kinds of the computer simulation algorithm, this paper introduces the algorithm of the full The requirements of the foot DAB receiver, a large application value. )

有关于求取图像信息熵的matlab M 文件；虽然很简单，但可以为你省去那么一点时间。 共享改变未来！(Image information on the strike entropy matlab M documents although very simple, but it can save you some time. Share changes in the future!)

基于HSV与小波包分析的遥感图像融合，首先多光谱图像转换到HSV空间，再与全息图像分别进行小波包分解，然后低频与高频各自采用不同的融合方法，最后得到融合图像。(image fusion)

使用统一资源定位符类URL实现文件下载，同时ImageIO也可以实现(class URL download file)

说明：  利用线性规划问题求解稀疏恢复问题的基追踪（basis pursuit，BP）算法，研究了在含有脉冲噪声干扰的原始信号环境下对经典基追踪算法的改进。(basis pursuit (BP) algorithm)

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.)

Matlab图像处理中的Harris角点提取算法， 代码中有详细的解说，方便阅读。 (Matlab Image Processing of Harris corner extraction algorithm, the code has a detailed explanation, easy reading.)