PHD

卡尔曼滤波是一种高效率的递归滤波器(自回归滤波器), 它能够从一系列的不完全及包含噪声的测量中，估计动态系统的状态。(Calman filtering is an efficient recursive filter (autoregressive filter). It can estimate the state of dynamic system from a series of incomplete and noisy measurements.)

图像分割关系到目标识别的精度，是目标检测的关键一步，其中提供了一些图像分割的代码，主要为otsu算法的分割方法，很实用(s related to the accuracy of target recognition, image segmentation is a critical step in target detection, provides some image segmentation code, very useful)

说明：  利用matlab计算时间序列间的DTW距离(Using MATLAB to calculate DTW distance between time series)

说明：  基于小波变换的图像融合，可实现两幅图像的融合，融合规则可自定义(Image fusion based on wavelet transform can realize the fusion of two images, and the fusion rules can be customized)

抗旋转攻击的数字水印方法，其鲁棒性和不可视性都有很强改进，尤其在抗旋转攻击方面。(Digital watermarking anti-rotation attack, its robustness and visibility has a very strong improvement, especially in the anti-rotation attacks.)

压缩感知基追踪算法，实现二维图像的小波变换以及图像重构(Compressed sensing based tracking algorithm, wavelet transform two-dimensional image, and image reconstruction)

本程序应用相位一致性进行边缘检测，较以往应用灰度信息进行边缘检测算法，该算法与光照无关，且抗噪声性能强(phase of the application process consistency edge detection, gray than in the past Application information edge detection algorithm, The algorithm has nothing to do with illumination, and strong anti-noise performance)

对于原始图像 < f5.tif > ，实现下列结果。 a.用平均滤波器平滑原始图像得到的模糊图像 b.用拉普拉斯算子增强上述a得到的模糊图像 c.用拉普拉斯算子增强原始图像 (For the original image <f5.tif>, to achieve the following results. a. smoothing filter with an average original image was blurred b. using Laplacian enhancement get a blurred image above c. using Laplacian enhancement of the original image)

基于边缘特征的图像配准算法源码 基于边缘特征的图像配准算法是将不同时间、不同的传感器（成像设备）或不同条件下（天候、照度、摄像位置和角度等）获取的同一场景的两幅或多幅图像根据其边缘特征进行匹配、叠加的处理，最终生成一幅全景图像的方法。该方法具有抗噪性强，匹配速度快，误匹配率低，适用于多种类型的图像等优点，主要可以运用于以下领域： （1）军事研究领域，如飞行器辅助导航系绞、武器投射系统的末制导以及寻地等应用研究； （2）医学图像分析，如数字剪影血管造影DSA血管造影术、肿瘤检测、白内障检测、CT、MRI、 PET、 SPECT图像结构信息融合； （3）模式识别，如特征识别、波形分析、签名证实、目标物运动跟踪、序列图像分析等； （4）计算机视觉，可用于目标定位、自动质量控制等； （5）遥感数据分析，多电磁波段图像信息融合，如多谱段的等场景分类、定位和识别定义好的或已知特征的场景、自然资源监控、核生长监控、市区增长检测等。 本算法实现使用VC++6.0为开发环境，目前只支持灰度图像，感兴趣的朋友可以进一步扩展。(Image registration is a fundamental problem in image processing, which is a process to match two or more images of the same scene taken at different times, from different viewpoints, or by different sensors. Image registration is an important technique for a great variety of applications such as medical image analysis, computer vision, remote image data analysis, image information fusion, and the recovery of the 3–D characteristics of a scene.)

数字图像处理中对分类器优劣的判定常用的ROC/ACU源码。希望对你有用。(The ROC/ACU source code commonly used in the determination of the quality of the classifier in digital image processing. I hope useful to you.)