数字图像处理_第三版_英_答案

说明：  脆弱性数字水印能为多媒体内容完整性认证和篡改检测提供有效的解决方案。该文对用于图像认证的脆弱性数字水印技术的研究 现状、一般原理、基本特征、典型算法进行了综述(The vulnerability of digital watermark for the integrity of multimedia content authentication and tamper detection to provide an effective solution. The image authentication for the vulnerability of digital watermarking technology, research, general principles, basic features, typical algorithms are reviewed)

图像的采样 • 写程序：以2 为尺度，下采样图像 ，观察不同分辨率下图像的变化情况(a) • 写程序：把(a)中获得的图像，进行上采样处理，可以选择不同的平滑方 法，比如，邻近像素复制，或者线性插值等。讨论上采样后图像的质量变 化。找出相应的改进方法。

关于自适应粒子滤波，我觉得很好用，贝叶斯滤波适合非线性和非高斯环境，在粒子滤波基础上 又加改进(About adaptive particle filter, I feel very good, a bayesian filter for nonlinear and non-gaussian environment, particle filter in again on the foundation and improvement)

说明：  可以将txt文件中的某一列数据进行提取出来，然后进行图像制图(You can extract a column of data from the txt file and then do image mapping)

应用程序，寻找出与训练集中头像图片相同的图片，并显示结果(Applications, find out the same picture picture picture with the training set, and displays the results)

pca+fisher 人脸识别MATLAB程序，附加图像数据库，适合简单的人脸识别。 (pca+ fisher face recognition program using MATLAB , adding image database for easy recognition.)

说明：  基于matlab的车牌识别，预处理。定位。识别.qiege(License plate recognition system based on MATLAB)

ENVI二次开发去云处理补丁，可用于去除薄云影响(ENVI secondary development to cloud processing patch, can be used to remove the cloud-affected)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

说明：  图像融合算法，exercise1:为基于小波变化与局部能量的图像融合方法 exercise2:为基于自适应算法的图像融合算法 myfunction1~myfunction5:为性能指标评价函数(Image fusion algorithm, exercise1: wavelet-based change and the local energy method of image fusion exercise2: adaptive algorithm based on image fusion algorithm myfunction1 ~ myfunction5: performance indicators for the evaluation function)