band_math

说明：  傅里叶谱与频域低通滤波 (a)从图书网站下载图4.41(a)并计算其(居中)傅里叶谱。(b)显示频谱。(c)在等式(4.8-7)中实现高斯低通滤波器。您必须能够指定结果2D函数的大小，M x N。此外，您必须能够指定高斯函数的中心位置。(d)从图书网站下载图4.41(a)，并对其进行低通滤波，以复制图4.48中的结果。(Fourier Spectrum and Lowpass Filtering in Frequency Domain (a) Download Fig. 4.41(a) from the book web site and compute its (centered) Fourier spectrum. (b) Display the spectrum. (c) Implement the Gaussian lowpass filter in Eq. (4.8-7). You must be able to specify the size, M x N, of the resulting 2D function. In addition, you must be able to specify the location of the center of the Gaussian function. (d) Download Fig. 4.41(a) from the book web site and lowpass filter it to duplicate the results in Fig. 4.48.)

meanshift 图像分割及平滑 包括meanshift的论文及ppt(Code for the Edge Detection and Image Segmentation system and the paper)

说明：  利用小波变换技术实现，最简洁的图像融合例子(image fusion.The simplest example of image fusion using wavelet transform technology.)

基于上下文注意的显著区域检测，附件包括代码和英文文章。(Context-Aware Saliency Detection. The file includes the paper and the source code in matlab.)

SIFT特征提取演算法(包含匹配以及除错机制RANSAC)-可用于两张影像之特征点匹配 (SIFT feature extraction algorithm (including the match, as well as debug mechanisms RANSAC)- can be used for two images of the feature points matching)

不同信噪比下周期FRFT和FRFT两种方法对LFMCW信号的检测能力对比(FRFT different SNR period and the comparison of the two methods FRFT LFMCW signal detection capability)

发表于ECCV上的一篇用于人脸识别的算法，Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary (At ECCV on an algorithm for face recognition, Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary)

图像几何校正中的绝对变换使用matlab编程(Absolute transformation in image geometric correction using MATLAB programming)

Object Detection Combining Recognition and Segmentation, Eighth Asian Conference on Computer Vision (ACCV), 2007 源代码(Object Detection Combining Recognition and Segmentation, Eighth Asian Conference on Computer Vision (ACCV), 2007 source code)

本文主要介绍遗传算法的基本理论，叙述遗传算法在图像增强的的主要应用，即将原始图像变得更加清晰，特征变得更加明显。 现今关于图像增强的算法有很多，而这些算法大多是基于退化函数或者点扩展函数的知识进行图像处理的。当图像出现模糊或噪声影响大时，设计出的图像清晰化的效果肯定不够理想，因此有必要对图像进行增强处理。于是，可利用到遗传算法这种成熟稳定的仿生物进化的全局寻优算法，进行图像增强，由于遗传算法控制参数少、自适应度高，将选择该方法对图像退化分别进行不同的清晰化处理. 这样既增强了图像的对比度,又克服了传统直方图均衡化处理所造成的灰度级损失等缺点。 通过实验去表明，遗传算法从全局寻优的角度出发，能够较为精确地估计退化系统函数。和传统的线性增强、直方图均衡进行比较。实验结果表明该方法能改善原图像视觉效果，便于之后的图像分析。 (This paper mainly introduces the basic theory of genetic algorithm, genetic algorithm (ga) in the main application of image enhancement, the original image is more clear, characteristic becomes more obvious. Today about the image enhancement algorithm are many, and most of these algorithms is based on the degradation function or the knowledge of the point spread function of image processing. When the image appears fuzzy or noise influence, designed the image clarity of effect must not ideal, therefore it is necessary to enhance the image. Then, genetic algorithm is available to the mature and stable evolution of the global optimization algorithm, for image enhancement, due to less genetic algorithm control parameters, since the high fitness, will choose the method of image degradation, respectively, to manipulate the different motivation. It can enhance the image contrast, and overcome the traditional histogram equalization processing grayscale loss caused by the faults. Throug)