RS-fractal-algorithm-matlab-code

关于无人动力伞的matlab数学模型建立，以及无人动力伞的控制研究。(unmanned paramotor on the matlab mathematical modeling, and control of unmanned paramotor.)

说明：  计算lzernike矩，用于图像的额模板匹配或者别的方向(zernike:Calculate lzernike moments for image frontal template matching or other directions)

gvf snake 范例程式,以matlab撰写(gvf snake sample program to write matlab)

eye picture示模型。修改高斯白噪声信道的SNR值可以看到不同情况下的eyepicture (eye picture show model. Modified Gaussian white noise channel SNR value can be seen under different circumstances eyepicture)

its usage is in computer vision

高丝混合模型，内涵matlab程序，用EM算法(Kose hybrid model, the connotation matlab program, using the EM algorithm)

捷联惯导系统解算程序包括MATLAB和C两个文件(SINS solver includes two files MATLAB and C)

本程序是快速独立成分分析算法的主程序，包含了详细的注释，不含子程序。(This procedure is fast independent component analysis algorithm of the main program, including detailed notes, excluding subroutine.)

Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted its corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF) is enhanced. Then based on kernel fuzzy c-means (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented respectively to extract the CSF region and the CSF combining MS lesions region. A raw MS lesions image is obtained by subtracting the CSF region CSF combining MS region. By applying median filter and thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively uated on BrainWeb images using Dice similarity coefficient (DSC). Finally, the potential of the method as well as its limitations are discussed.

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)