对有雾的图像运用大气散射模型进行处理，通用的大四散射模型与图像的饱和度有一定关系，于是利用HSV模型中的饱和度H进行去雾处理(On a foggy image processing using atmospheric scattering model, saturation senior general scattering model have a certain relationship with the image, so the use of HSV model saturation H were treated to fog)
ISAR点目标RD成像算法,距离向包络对齐，多普勒中心相位补偿(Algorithm for ISAR point target imaging)
matlab处理火焰视频，基于火焰亮度阈值，计算火焰高度宽度和间歇性。(Matlab processes the flame video and calculates the flame width and intermittency based on the flame brightness threshold.)
利用积分图的方式提取图像hog特征，用于人脸、行人检测。(Using the integral plot to extract the image hog feature, for human face, pedestrian detection.)
针对CT 医学图像和MR I 医学图像成像特点, 提出了基于快速整数提升小波变换的融合方法。在CT 和 MR I 两幅医学图像配准的前提下, 利用提升小波变换把图像分解成低频和高频子图像, 对于小波变换后的高频 子图像, 选择区域标准差大的作为融合后的子图像 对于低频子图像, 采用加权融合, 最后进行小波逆变换, 得到 融合后的图像, 并对融合后图像用信息熵、平均梯度、相关系数的指标进行评价。实验结果表明, 基于快速整数提 升小波变换融合中, 小波高低频系数采用不同的规则能够取得更好的融合效果, 其轮廓清晰。该算法能够提升融 合后的医学图像信息量, 同时有效地保护图像的细节信息, 在执行时间和图像质量上均优于普通小波算法。(A imed at the characterist ics of CT andMR Imedical images, a new image fu sion al2 go rithm based on the fast in t lif t ing w avelet t ran sfo rm s is p ropo sed. Two o riginal images are decompo sed by the fast in t lif t ing w avelet t ran sfo rm s. The fu sion ru le based on the max imum standard deviat ion value variance is u sed to fu se the h igh f requency sub2image, w h ile a w eigh t2 ed average fu sed ru le is app lied by coeff icien t s of the low f requency. F inally, the fu sion image is recon st ructed fo r perfo rm ing inverse fast in t lif t ing w avelet t ran sfo rm s. The fu sion image is evaluated by en t ropy, average gradien t, and co rrelat ion coeff icien t s. Experimen tal resu lt show s that the fu sion image hasmo re info rmat ion than o riginal images and imp roves the quali2 ty of o riginal images. M eanw h ile, the fu sion image p ro tect s detail characterist ics of the image, thu s the real2t ime p rocess and image qualit ies are w ell than tha)
一种新型的基于Tucker张量分解的高光谱图像降噪算法，性能优于传统的维纳滤波(a novel method based on Tucker decomposition for HSI denoising)
一个基于Gabor滤波器组的多特征融合的图像检索算法（CBIR based on Multi-Features Combining Multi-parameters from Gabor Filter-bank, 简称multi法），融合了基于Gabor 小波系数的标准方差，峰态，能量，平滑度和一致性度量五个特征量度，实验表明，该算法是一种更加有效的图像检索方法。(we propose a CBIR algorithm based on Multi-Features Combining Multi-parameters from Gabor Filter-bank(called multi algorithm for short), which use Gabor filter banks and combine five features including standard deviation, kurtosis, energy, smoothness and uniformity together. The results of experiments show that this algorithm is a more effective method used for image retrieval.)
sift算法在cv领域的重要性不言而喻，该作者的文章引用率在cv界是number1.本篇博客只是本人把sift算法知识点整理了下，以免忘记。本文比较早的一篇博文opencv源码解析之(3)：特征点检查前言1 中有使用opencv自带的sift做了个简单的实验，而这次主要是利用Rob Hess的sift源码来做实验，其实现在的opencv版本中带的sift算法也是Rob Hess的，只是稍微包装了下。 下面来做下试验,试验sift代码采用Rob Hess的代码，opencv目前版本中的sift源码也是采用Rob Hess的。代码可以在他的主页上下载：http://blogs.oregonstate.edu/hess/code/sift/ 这里我下载的是windows版本的，并采用Qt做了个简单的界面。(sift algorithm is self-evident the importance of the cv field, the author of the article referenced in the cv sector is number1 this blog I just sift algorithm knowledge compiled under, so as not to forget. This article compares the earlier blog post opencv source code analysis (3): check the preface of the feature points 1 the opencv bring their own sift to do a simple experiment, but this time Rob Hess' s sift source to experiment. In fact, with the opencv version sift algorithm is Rob Hess, only slightly packing the next. Below to do the next test, test sift code using the code of Rob Hess, sift source opencv version of Rob Hess. The code can be downloaded on his home page: http://blogs.oregonstate.edu/hess/code/sift/ here I downloaded the windows version, and using Qt to do a simple interface.)
获得目标的颜色直方图，并利用巴氏距离比较两颜色直方图间的相似性。(Get color histogram)
螺旋轨迹重建的一种实现方法，是解析算法的典型案例(An implementation method of spiral trajectory reconstruction is an analytical algorithm)
说明： 基于卡尔曼滤波的camshift算法，实现目标跟踪(CAMSHIFT algorithm based on Kalman filter for target tracking)
几个将图像分块，将分块图像拼接，和裁减图像的实用小程序(several image block to block image mosaic, and the reduction of small images of practical procedures)
图像工程作业3：基于视词袋模型的场景识别 (Scene recognition with bag of words)(Image Engineering Job 3: Scene Recognition Based visual bag of words (Scene recognition with bag of words))
基于K均值聚类的图像分割，灰度图像分割案例，真彩图像分割案例(K-means clustering-based image segmentation, image segmentation case, the true color image segmentation case)
使用Tensoflow实现图卷积神经网络模型(Use Tensoflow Implementation Figure convolution neural network model)
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