SIFT和HOG特徵描述符

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Heyyy ,,,我要做分類。而對於描述我計劃使用HOG和LOWE SIFT描述..SIFT和HOG特徵描述符

1. For HOG, is that true that we need to compute the gradient of all image's pixels?? 
For example we have image with size 10x10 pixels. And we compute the HOG to seek the orientation   
for every pixels of image. And eventually we will get 100 orientations and generate histogram 
(represent all of those image's pixels orientation). This Histogram is going to be used for the  
classification?? 
And to get the scale invariant we need to perform this descriptor of different size (scale)  
images??

SIFT是尺度不變特徵變換。所以它是比例和旋轉不變量。
我讀from here，在SIFT我們需要做的採用高斯，使我們的圖像的
分辨率變低平滑..

2. Why we have to do that? 
    And for scale invariant features, how to obtain that in SIFT?? Do we need to rescale our image 
    in every octave and then apply Gaussian filter in this new scaled images?? Or it's enough to 
    get scale invariant only in 1 octave by applying 3 times gaussian filter?? 
    How about the histogram, Is it same with HOG that we have to compute all the pixels???

Thankss

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2014-04-03 user3378327

嗨，歡迎來到SO。除了格式不正確之外，你的問題與編程無關。找出理論並回來，可能遇到任何實施問題。 –

如果您打算實現SIFT至少你應該閱讀Lowe以來的論文是CV歷史上引用最多的一篇！

在SIFT中使用高斯平滑來計算DOG（高斯差）。然後執行Scale Extrema Detection，您將檢測特徵點。

一旦你有了這個特徵點，你將需要計算每個特徵的HOG。你不需要爲整個圖像計算它！由於我們採用16x16的鄰域，結果將是一個128長度的描述符。

尺度不變性是因爲梯度在每個關鍵點周圍的窗口中針對按鍵的尺度進行採樣。

但是，你應該再次通過Lowe的論文，因爲它在那裏非常清楚。你還應該有一些清晰的概念，比如特徵點，HOG，DOG等等，才能真正理解SIFT

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2014-04-05 15:07:23

SIFT和HOG特徵描述符

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