1

图像处理PPT，适合初学者，介绍各种图像处理方法(Image processing PPT, suitable for beginners, introducing the various image processing methods)

说明：  MATLAB 边缘检测相关代码，里面包括多种方法，请朋友们根据自己实际情况选择方法。(Edge detection related MATLAB code, which includes a number of ways, please friends, the actual situation according to their own options. )

matlab版本 图论中的kruskal算法 一般C版本的比较多，这里提供matlab版本的，更简单方便使用 (Matlab code of kruskal algorithm in graph theory)

color image segmentation

Rotacion de puntos en dos dimensiones

The book is divided into three parts: theory, method and implementation. Starting with a summary of the state-of-the-art in mobile technologies, the first part analyses their impacts on cartography and pinpoints the missing theories concerned with the development of map-based mobile services. A conceptual framework of mobile cartography is then introduced with the emphasis on mobile usage context. The second part is devoted to the design methodology under the constraints defined in the theoretical framework. A core issue deals with personalised mobile map services. The final part demonstrates the feasibility of the methods by using application scenarios.

阵列信号处理里面的经典算法，music算法，详细解答(Array signal processing algorithms inside classics, music algorithm, detailed answers)

描述几种常见的变步长算法，步长因子与误差之间关系的曲线。(This paper describes several common variable step size algorithms, the curves of the relationship between the step factor and the error.)

统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines( This section should give the reader a quick overview of the methods implemented in STPRtool. • Analysis of linear discriminant function: Perceptron algorithm and multiclass modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection of known algorithms solving the Generalized Anderson’s Task. • Feature extraction: Linear Discriminant Analysis. Principal Component Analysis (PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis. • Probability distribution estimation and clustering: Gaussian Mixture Models. Expectation-Maximization algorithm. Minimax probability estimation. K-means clustering. • Support Vector and other Kernel Machines: Sequential Minimal Optimizer (SMO). Matlab Optimization toolbox based algorithms. Interface to the SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers. Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell- Demyanov-Molozenov algorithm )

说明：  小波变换的matlab程序，包括离散小波变换，多尺度变换，小波包分析，小波用于图像压缩、水印、去噪(Matlab wavelet transform procedures, including the discrete wavelet transform, multi-scale transform, wavelet packet analysis, wavelet image compression, watermarking, denoising)