cfa_laroche

整理了五种多曝光图像融合的代码，用于图像处理，图像去雾，有英文介绍(Five kinds of multi-exposure image fusion codes are sorted out.For image processing, image defogging, with English introduction)

可以将连续图片转换成视频文件，值得参考学习(Convert a continuous image into a video file)

hough和randon函数求图像中圆，对于水中气泡检测效果很不错(Circle detection based on random Hough transform)

采用改进的SURF方法进行特征的提取，相比于SIFT，能获取更多的特征点(With the improved SURF methods for feature extraction, compared to SIFT, can get more feature points)

何凯明的基于暗原色先验的去雾代码，去雾效果很好(Kaiming dark colors based on a priori defogging code defogging good effect)

为数字图像水印提供多种攻击方式，其中包括JPEG压缩、高斯低通滤波等多达十种攻击方式。(Offers a variety of attacks of digital watermarking ,including the jpeg compression, Gaussian low-pass filtering, up to ten different attacks)

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：  一个用于进行图像去噪的很好的算法，很新很好的一个算法(A well used for image denoising algorithm, an algorithm is a new good)

对图像进行纹理处理，为图像识别、目标跟踪创造条件。主要分析：惯性矩、熵以及局部平衡性。(The image texture processing for image recognition, target tracking and create conditions. Main analysis: moment of inertia, entropy, and local balance.)

实现对灰度图像的哈夫曼编码，对于其他图像，也可以类似的完成。(Realize grayscale images of hoffmann coding, for other image, also can be similar to the finish.)