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1.6 尺度不变特征提取

于 2019-02-28 发布
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代码说明:

说明:  1.6尺度不变特征提取,内附代码和文件说明,实证可用(1.6 Scale invariant feature extraction, enclosed code and document description, the empirical results are available.)

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    用Matlab产生正弦波,矩形波,以及白噪声信号,并显示各自时域波形图 2.进行FFT变换,显示各自频谱图,其中采样率,频率、数据长度自选 3.做出上述三种信号的均方根图谱,功率图谱,以及对数均方根图谱 4.用IFFT傅立叶反变换恢复信号,并显示恢复的正弦信号时域波形图 (Using Matlab generated sine wave, rectangular wave, as well as the white noise signal, and display their respective time-domain waveform of Figure 2. FFT to transform, showing their frequency spectrum, including sampling rate, frequency, data length of 3-on-demand. Make the rms signal of the three maps, power maps, as well as the number of root-mean-square map 4. Fourier Transform IFFT with the restoration of signals, and displays the sinusoidal signal the resumption of time-domain waveform)
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    图像的特征用到了Dense Sift,通过Bag of Words词袋模型进行描述,当然一般来说是用训练集的来构建词典,因为我们还没有测试集呢。虽然测试集是你拿来测试的,但是实际应用中谁知道测试的图片是啥,所以构建BoW词典我这里也只用训练集。 其实BoW的思想很简单,虽然很多人也问过我,但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了,面试中也经常问到我这个问题,不知道你们都怎么用生动形象的语言来描述这个问题? 用BoW描述完图像之后,指的是将训练集以及测试集的图像都用BoW模型描述了,就可以用SVM训练分类模型进行分类了。 在这里除了用SVM的RBF核,还自己定义了一种核: histogram intersection kernel,直方图正交核。因为很多论文说这个核好,并且实验结果很显然。能从理论上证明一下么?通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。(Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set. In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem? After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified. Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram )
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    2008-11-27 08:16:20下载
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  • juzhen
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    2012-02-27 09:35:32下载
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    2017-05-05 12:49:43下载
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    内含张正友,tsai的标定方法,可以求解摄像机内参数u轴尺度因子,v轴尺度因子,摄像机外参数:两摄像机坐标系原点之间的旋转矩阵和评议矩阵,可以得出摄像机像素参数,焦距,畸变系数,并且带有优化结果。(Containing Zhang Zhengyou, tsai calibration method that can solve the camera parameters u-axis scale factor v The axis scale factor, the camera extrinsic parameters: the rotation matrix between the two camera coordinate origin, and appraisal matrix can be drawn from the camera pixel parameters, focal length distortion coefficient, and with optimal results. )
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    利用MATLAB实现图像处理,实现图像背景分离。(Using MATLAB to realize image processing)
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