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ASM_version1b
ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。
基本思想：
在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。
(Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach.
Basic idea:
The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions.
After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour.
Literature:
- Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I)
- 2010-03-04 17:01:22下载
- 积分：1
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log
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DSP_MATLAB
包含了清华大学版数字信号处理的所有实例程序，实例详细，对学习数字信号处理作用很大。(Tsinghua version contains all instances of digital signal processing procedures, detailed examples, the study of digital signal processing play a great role.)
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sys
本实验隶属现代数字信号处理，涉及到谱估计和谱分解，对学习数字信号的现代处理方法帮组很大。系统辨识实验实现：输入为功率为1（方差为1）的白噪声信号，通过一个线性移不变（LSI）系统，采用功率谱分析方法实现系统辨识对一个LSI系统进行辨识,并比较最后辨识出的系统与原系统的差别，分别显示出来。
(Experimental realization of system identification: input power of 1 (variance 1) of the white noise signal through a linear shift invariant (LSI) system, using power spectral analysis method of system identification of an LSI system identification, and compare the final system and identify the difference between the original system, respectively, is displayed.)
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ver_0.8
CNN - Convolutional neural network class
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xssy
源程序是关于心音信号分析的，可以对心音进行EMD分解(It is about the dealing of heart voice .)
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gray_scale_half
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Text-files
how to read and write files in matlab
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Converterqwqw
Converter is a unit conversionarithmetic calculatorarea and volume calculator
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Desktop
特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。
我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下：
對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )
- 2014-12-18 12:19:30下载
- 积分：1
