Digital_Signal_Processing_A.V.Oppenheim

To calculate symmetric bispectrum

说明：  CT重建算法，迭代算法ARTII，乘型ART算法，该算法法比初始值选择不能为0(CT reconstruction algorithm, iterative algorithm ARTII, x-ART algorithm, The algorithm method than the initial value of options can not be 0)

基本解方法求解反问题程序很好用有结果和图形(Method of Fundamental Solutions of Inverse Problems program.)

FSK调制方式所产生的信号画出时域频域波形程序(The FSK mode signal the draw time domain to the frequency domain waveform program)

说明：  禁忌算法解决TSP问题，程序简单效率高，欢迎来完善(Tabu algorithm to solve TSP problem, the program simple and efficient, welcome to improve)

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)

This function Skeleton will calculate an accurate skeleton (centerlines) of an object represented by an binary image / volume using the fastmarching distance transform.

about NFSNET topology in optical network...which consist of 14 node and 42 links ..

knn classifier that indecatw classes of test data according k nearest neighbor algoritm in pattern recognition

用matlab的小波工具箱和神经网络工具箱实现的短期负荷预测(Short-term load forecasting using wavelet toolbox matlab neural network toolbox to achieve)