k-mea1

基于灰色整定的PID控制在MATLAB中的源代码(Genetic Algorithm)

1 SIFT 发展历程 SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 3 SIFT算法的主要特点： a) SIFT特征是图像的局部特征，其对旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性。 b) 独特性(Distinctiveness)好，信息量丰富，适用于在海量特征数据库中进行快速、准确的匹配[23]。 c) 多量性，即使少数的几个物体也可以产生大量SIFT特征向量。 d) 高速性，经优化的SIFT匹配算法甚至可以达到实时的要求。 e) 可扩展性，可以很方便的与其他形式的特征向量进行联合。 4 SIFT算法步骤： 1） 检测尺度空间极值点 2） 精确定位极值点 3） 为每个关键点指定方向参数 4） 关键点描述子的生成 本包内容为sift算法matlab源码(1 SIFT course of development SIFT algorithm by DGLowe in 1999, the perfect summary of 2004. Later Y.Ke its description of the sub-part of the histogram with PCA instead of its improvement. 2 the SIFT main idea The SIFT algorithm is an algorithm to extract local features in scale space to find the extreme point of the extraction location, scale, rotation invariant. 3 the main features of the SIFT algorithm: a) SIFT feature is the local characteristics of the image, zoom, rotate, scale, brightness change to maintain invariance, the perspective changes, affine transformation, the noise also maintain a certain degree of stability. b) unique (Distinctiveness), informative, and mass characteristics database for fast, accurate matching [23]. c) large amounts, even if a handful of objects can also produce a large number of SIFT feature vectors. d) high-speed and optimized SIFT matching algorithm can even achieve real-time requirements. e) The scalability can be very convenient fe)

fingerprint recognization with more dip results

1d fourier shift in matlab

基于局域均值的盲源分离技术在故障诊断中的应用文章，希望对大家有用！(Based on Local Mean Blind Source Separation Techniques in Fault Diagnosis article, I hope useful for everyone!)

改进的自适应均衡算法，收敛性和稳定性较好，除噪效果明显(The improved adaptive equalization algorithm has better convergence and stability, and the effect of noise removal is obvious)

matlab教程matlab试题与解析MATLAB试题解答和方法分析(matlab)

说明：  matlab下几个三角函数实现的对比 。(matlab implementation of the next several trigonometric functions compared)

说明：  用MATLAB对一图象分别用不同小波分解，观察高低频部分进行重构，比较重构误差，再进行阈值消噪，比较消噪前后图象。(MATLAB right one image with different wavelet decomposition, high-low-frequency observations for some remodeling, more reconstruction error and then de-noising, compared before and after image denoising.)

说明：  A Short Guide on a Fast Global Minimization Algorithm for Active Contour Models文章代码基于MATLAB(A Short Guide on a Fast Global Minimization Algorithm for Active Contour Models)