[ptx] Please help me on SIFT detection by David Lowe's IJCV paper

[Xianyong Fang]

Hi,

  I have implemented the SIFT detection algorithm of David Lowe's IJCF paper 'Distinctive Image Features from Scale-Invariant Keypoints', but why I can not find many initial keypoints(that is the peaks in DOG images). Even I find the initial keypoints do not always occur in the corners and edges.

  According to my understanding, the resampled guassian image is used as the first scale image of the next octave, am I right? I build three DOG images in each octave (two DOG images have the same problem), but find that only few (actually less that 10) initial key points can be detected. But if I compare each sample points in each DOG image independently(that is, to every position, only when the value in each DOG image of the same octave is the maximum or minimum among it's eight neighbors of the same DOG image it is selected as the initial key points) , I can find many more initial key points(more that 100). So I don't know why I get so few key points with the original method metioned in Lowe's paper? Are there some mistakes made by myself? 
  
  Even I can not understand that why we can select initial key points from DOG images by the original method. In my mind, the middle DOG image's intensity should lie between the upper and lower DOG images. So if we use the value in the middle DOG image to compare with all its neighboring values( including the upper and the lower DOG images) it is hard to get the maximum or minimum position.

  Can anyone give me some advice? Thanks very much.

Xianyong