SIFT keypoint matching Trials of my Implementation

2 of the same images, using my own SIFT implementation gives me 722 matches
for my second experiment i tried translation invariance, 2 images taken from different angles. i got 243 matches
In my next experiment i tried a complete affine transformation of a graffiti wall, i got 22 matches but not so accurate. this shows the keypoints have a problem in its rotation and scaling invariants. my magnitude and teta maybe the cause of this.
This is a matching from David Lowes algorithm showing a clear affine invariance.

my implementation gives me a very bad matching. alot more work to do.

and more bad results, if the orientation of the keypoint is wrong, the matching will also suffer so i tired different tangents in matlab

atan(dy/dx);

atan2(dy,dx);

BOTH IMAGES: show clearly that rotation invariance is not achieved, this is the reason why the matching gives alot of false negative results

furthermore; the amount of BLUR is also very important

images and details from (based on my understanding):
Lowe, David G. “Distinctive Image Features from Scale­ Invariant Keypoints”. International Journal of Computer Vision, 60, 2 (2004)

My SIFT implementation and comparisons

Im trying to implement SIFT in matlab and compare it with other implementations
the comparisons are against the following:
original SIFT code by LOWE found at this URL
the other is the popular A. Vedaldi Code found at this URL


This is the result of the vadaldi code that finds 262 key points

This is the original SIFT code by David Lowe showing 249 keypoints



This is from my code showing 315 keypoints.

from all 3 images you can see that there are points that are outside of the image and a surprising 70 extra keypoints on my implementation. obviously my implementation is wrong, so i tried to go through the code and adjusted a few things

Now i get 195 keypoints and as you can see from the image, all the keypoints are located inside of the image. my next problem is the magnitude.

from the previous images, the code from LOWE and VEDALDI have a very good magnitude however my implementation has very small.




All yellow markers are from my implementation

Computer vision Datasets to use

a very nice dataset collected by Krystian Mikolajczyk for testing scaled and affine interest point detectors with various types of local image descriptors.
These are some of the other dataset that i use:

MIT Face Dataset
MIT Car Datasets
MIT Street Scenes
Pedestrian dataset from MIT

Most of the files are in *.ppm format, i personally use adobe photoshop to open these