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SIFT is perhaps the most popular invariant local feature detector at present, and has been applied succefully in many occasions, such as object recognition, image match and registration, SFM & 3D reconstruction, vSLAM, image panorama, etc. Previously, I've developped some versions of SIFT algorithm, as well as Lowe's object recognition system mentioned in the paper(IJCV04).

One of the version, named Harris-SIFT, works quite well and fast in my previous vision system-- RTL(Real-time Tracking and Localization). In fact, it's a simplified SIFT and retains virtues of the original algorithm. Recently, I've foud someone mentioned that had developped a good version of SIFT, named siftpp. I looked deep into it and found its performance quite like that of Lowe's demo. More important, that code strictly follows steps in Lowe's paper, thus is easy to read or study Lowe's algorithm. However, it runs slow and has some bugs. I'd like to opimize the code so that it can be applied in other occasions.

Other codes I refered, including and. The former is implemented in c and uses OpenCV. In fact, his implementation, in some sense, is more correct than siftpp. However, the code is not very efficient due to some redundant processes, and the not-well-organized feature structure. Further more, personally I favor C++ than C, for the reason that C++ provides many advanced features, powerful, useful and convenient for us to write robust codes, esepcially true for algorithm or libraries. The later is an excellent vision library designed for modeling human's neuron vision model. It's still under developping and CVS verion is available.

I prefer the structure of this library and it is implemented with moden C++ language, such as meta-template, and some kind of design pattens. A SIFT-based object recognition framework is nested in the library, which can be a good reference. So now, above are the main sources of my references. That's quite enough. Recently, I spent 3 days to rewrite the SIFT algorithm, and it is finished now. Main properties as follows: 1.

Correct and Accurate Algorithm routines of the code strictly conforms to Lowe's paper, and the result is quite similar to. In fact, both the feature location, scale, orientation, and the descritptor, are approximately same as that from Lowe's demo. For example, below are two sets of sample features extracted with my code and Lowe's demo respectively. Anonymous said.

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