实验一

Impulse response, system response, for signal processing

说明：  criminisi算法。传统的criminisi图像修复算法，可对破损图像进行纹理修复(Criminisi algorithm. The traditional criminisi image restoration algorithm can repair the damaged image texture)

说明：  用于物体目标轮廓的提取与跟踪，能更好的识别出物体轮廓。(Target contour for object extraction and tracking, to better identify the contours of objects.)

对图像进行特征提取，构造合适的特征集合，包括SFS,SBS,SFFS等(failed to translate)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)

实现对图像的经验模态分解，得到经验模态分解后的第一层IMF分量，反映了图像的主要信息，去除冗余信息，为识别提供了基础。(Empirical mode decomposition of image, obtain the empirical mode decomposition of the first layer of IMF component, reflected the main information of the image, to remove redundant information, provide the basis for identification. )

蚁群算法实现医学图像的分割，同时对比FCM算法，非常有用，可以直接运行，包含测试MRI图片(Ant-Colony algorithm for MRI image segmentation including MRI image for testing.)

FSIM图像质量评价性能指标算法，衡量图像质量好坏程度(FSIM image quality uation performance index algorithm, to measure the degree of image quality )

易语言图片按钮放大源码，易语言写的图形图像编程，很好的参考。(Easy language source code easy to picture button zoom, image language programming, a good reference.)

1阶HMM模型多个观察值序列的评估和训练问题模型。适合用于图像纹理的去噪，分割等问题。程序用于为网友提供学习之用，由于是自编自用，所以如有不通的地方仅供参考。另由于数据较大，没有上传。(Evaluation and Training of multiObservation sequence Used by 1-order HMM)