brodatz

CT图像重建的几种ART迭代算法，包括一般的简单ART和联合ART。(Some kinds of ART iterations algorithm in CT reconstruction including simple ART and SART.)

matlab,图像分割手势，不是用摄像头的，是将图片输入进去识别使用MATLAB进行图像分割(Matlab, image segmentation gesture, not with the camera, is to input pictures to identify the use of MATLAB image segmentation)

采用阻抗跟踪及模糊逻辑算法，实现锂电池包SOC及SOH精确估算。(Estimation of the SOC and SOH of Li-ion battery pack ,by empedence measure with fuzzy logic inference)

用matlab实现的c均值聚类算法，用于图像分割，分割效果很好(Matlab achieved with c-means clustering algorithm for image segmentation, segmentation well)

Image Database 2.5.0 (w/ thumbnails)

使用傅立叶变换对图像的条纹噪声进行滤除，分别使用了理想低通滤波、巴特沃兹低通滤波和高斯低通滤波进行处理(The use of Fourier transform on the image to filter out the noise of the stripes, respectively, using an ideal low-pass filtering,巴特沃兹Gaussian low-pass filtering and low pass filter to deal with)

本代码比较全面地给出了图像边缘检测常用的各种算法的代码，包括：基于LoG算子边缘检测、基于Canny算子的边缘检测、基于SUSAN算子的边缘检测、基于小波变换模极大值的边缘检测、利用有限冲击响应来提取不同方向边缘、采用灰度形态学膨胀和腐蚀，对图像进行单尺度和多尺度的形态学边缘检测(This code gives a more comprehensive variety of commonly used edge detection algorithm code, including: LoG operator based on edge detection, based on Canny operator edge detection based on SUSAN operator edge detection based on wavelet transform modulus maxima edge detection values ​ ​ using finite impulse response to extract edges in different directions, using grayscale morphological dilation and erosion, the image of the single-scale and multi-scale morphological edge detection)

说明：  在静止背景下使用背景差分法和帧间差分法进行移动目标的检测(Moving target detection using background subtraction and inter-frame subtraction in static background)

用于图像拷贝检测的图像匹配，提取图像的SIFT特征点，构成图像的特征。(For image copy detection of image matching, image SIFT feature point extraction, and then sift feature point the image characteristics)

最小二乘图像影像匹配方法，一个很详细的源码例程，有过程有结果，希望对初学者有帮助。高斯解方程，以及最小二乘匹配，里面都很详细。(Squares image image matching method, a very detailed source code routines, the outcome of the process, I hope to help beginners. Gaussian solution of equations, and least squares matching, which is very detailed.)