targettrackingusingkalman

SRM空间富模型隐写分析算法,选区高维特征，使用集成分类器进行训练(Spatial Rich Model Steganalysis Algorithms, Selection of High-Dimensional Features, Using Integrated Classifier for Training)

1-32是：图形应用篇 33-66是：界面设计篇 67-84是：图形处理篇 85-100是：数值分析篇 (1-32 is: the application of articles 33-66 graphics is: interface design articles 67-84 is: graphics processing chapter 85-100 are: Numerical Analysis Section)

说明：  Simple Thumbnail Viewer PRINT

说明：  可以用于图像的canny滤波处理，直接输入图像，可以保存下来处理后的canny图像(It can be used for canny filtering of images, directly inputting images, and preserving processed canny images.)

通过对图像进行逆透视变化，得到图像的俯视图，即鸟瞰图(Through the inverse perspective change of the image, we can get the top view of the image, that is aerial view.)

三维模型的网格简化算法，通过删除边来减少面片个数，同时保留图形特征(Three-dimensional model of the mesh simplification algorithm to reduce the number of patches by removing the side, while retaining graphic features)

　找出图象的最佳阈值，并显示它的二值图．(find the best image threshold, and it shows the value of Figure 2.)

一个类似ACDSee的看图工具，可以察看很多种格式的图形文件。由ISee开发组提供(ACDSee pictures of a similar tool, you can see a variety of graphics file formats. ISee provided by the development team)

这篇论文提出了一种较好的水下图像增强的方法。首先使用经过端到端训练的卷积神经网络去测量输入图片，同时以自适应双边滤波器对传输图片进行处理。接着提出一种基于白平衡的策略来消除图片的颜色偏差，用拉普拉斯金字塔融合获得无雾和色彩校正图像的融合结果。 最后，输出图像被转换为混合小波和方向滤波器组（HWD）域，用于去噪和边缘增强。 实验结果表明，该方法可以消除颜色失真，提高水下图像的清晰度。(This paper proposes a better underwater image enhancement method. Firstly, an end-to-end training convolutional neural network is used to measure the input image, and an adaptive bilateral filter is used to process the transmitted image. Then a strategy based on white balance is proposed to eliminate the color deviation of images. The fusion results of fog-free and color correction images are obtained by Laplacian pyramid fusion. Finally, the output image is converted into hybrid wavelet and directional filter bank (HWD) domain for denoising and edge enhancement. The experimental results show that the method can eliminate color distortion and improve the clarity of underwater images.)

说明：  基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析，目前使用高维特征空间。目前，由于其良好的检测精度和较小的计算成本，它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)