extract-len-of-a-paper

opencv,图像处理，关于物体粘连问题的检测，基于凹性分析的分割方法(opencv, image processing, object sticking problem on detection, segmentation method based on analysis of the recess)

基于NSCT的几种图像融合算法，并对算法的性能作了比较，该代码有详细的说明。(Several image fusion algorithm based on NSCT, and compares the performance of the algorithm, the code is described in detail.)

用小波变换作为稀疏基，采取OMP算法将图像重建恢复，由于算法计算量大会导致成像时间过长，程序用改进的分块处理缩短了时间，(Wavelet transform as a sparse base, take OMP algorithms to restore the image reconstruction algorithm to calculate the General Assembly led to the long imaging time, the program using a modified block processing to shorten the time)

这是关于pca的文件，说明实现pca的降维(This is the file on pca, achieve pca dimensionality reduction)

这是通过径向偏振贝塞尔高斯光束聚焦一个组合 二进制相光学元件和高数值孔径的镜头。(This is achieved by focusing a radially polarized Bessel–Gaussian beam with a combination of a binary-phase optical element and a high-numerical-aperture lens.)

说明：  基于C#的模糊雾化处理。 高斯雾化 C #-based processing of fuzzy atomization. Gaussian Atomization(C#-based processing of fuzzy atomization. Gaussian atomization C#-based processing of fuzzy atomization. Gaussian Atomization)

图像融合GUI 包含多种方法 均值法 各种区域算法 以及各种融合指标(Image fusion GUI includes a variety of methods-means algorithm as well as various regional integration indicators)

一个散焦模糊自动复原的算法，自动估计散焦半径，在频率域识别参数，并用最小二乘滤波复原(A defocus blur automatic recovery algorithm automatically estimated defocus radius, in the frequency domain identification parameters, and by the least squares filter restoration )

说明：  自己写的fft的应用实例，包括对一维振动信号分析，对二维图像频谱分析。(The application examples of FFT written by myself include one-dimensional vibration signal analysis and two-dimensional image spectrum analysis.)

一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)