FVC2004

基于SVD和混沌加密的DWT。程序里包含了直方图攻击，锐化，剪切，高斯噪声等攻击。(Based the SVD and chaotic encryption DWT. The program contains a histogram attack, sharpening, shear, Gaussian noise attack.)

一个基于帧间差分的目标跟踪matlab源代码，解压后可直接运行，对学习跟踪有重大帮助(Frame difference of a target tracking based on matlab source code, extract can be directly run a major help in learning track)

说明：  道床加速度时程曲线。功率谱密度函数 图。1/3倍频程Z振级频谱图。道床Z振级VLz（dB）。(Acceleration time history curve of ballast. Power spectral density function. Frequency spectrum of 1 / 3 octave Z vibration level. Ballast Z vibration level vlz (DB).)

基于matlab的图像分解重构的源码，基于matlab的图像分解重构的源码。。。(The reconstruction of the image decomposition based on matlab source code, the reconstruction of the image decomposition based on the matlab source code..)

说明：  使用贝叶斯算法进行图像分割，迭代法寻找最佳阈值(Bayesian algorithm is used for image segmentation, and iterative method is used to find the best threshold)

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.)

一个阈值去噪程序，包括经典的去噪方法，如折中阈值，自适应阈值等等。(A threshold denoising procedures, including the classical denoising methods, such as compromise threshold, adaptive threshold and so on.)

首先生成如下形式的图片：图中的白条为7象素宽，210象素高，白条间的宽度为17象素。 应用多种滤波器进行处理：算术均值，几何均值，谐波均值，逆谐波均值，中值滤波，中点滤波，最大值、最小值滤波等。注意，需改变模板大小。详细比较上述的结果，并分析原因。 (First generation following form images: figure IOUs 7 pixels wide, 210 pixels high, the width is 17 pixels between IOUs. Using a variety of filters for processing: the arithmetic mean, geometric mean, harmonic mean, inverse harmonic mean, median filtering, filtering midpoint, maximum, minimum filtering. Note that the need to change the template size. A detailed comparison of these results and analyze the reasons.)

hough变换提取图像中圆的半径和圆心坐标(hough transform to extract the radius and center)

说明：  分析音频，分割进行傅里叶变换，得出声谱图(Analyze the audio and get the spectrogram)