Contents
Is SIFT algorithm patented?
SIFT. The SIFT detector is a scale-invariant feature descriptor algorithm which is based on the Difference of Gaussian detector. SIDENOTE: The SIFT detector is actually patented by the University of British Columbia. The use the SIFT detector in commercial application requires a license.
Is SURF patented?
In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction.
Is ORB better than sift?
We showed that ORB is the fastest algorithm while SIFT performs the best in the most scenarios. For special case when the angle of rotation is proportional to 90 degrees, ORB and SURF outperforms SIFT and in the noisy images, ORB and SIFT show almost similar performances.
What are ORB features?
Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented by Ethan Rublee et al. in 2011, that can be used in computer vision tasks like object recognition or 3D reconstruction.
Can I use the SIFT algorithm without any fear?
I want to use the SIFT algorithm for my scientific research (in EU, concretely SP), but it may end in a commercial software. I’ve searched the internet and what I found is that the patent is only in US and that in EU there is no “software development” patents. Can I use SIFT algorithm without any fear? For sure, without selling to US.
What can be used as an alternative to sift?
LoG filter – since the patented SIFT uses DoG (Difference of Gaussian) approximation of LoG (Laplacian of Gaussian) to localize interest points in scale, LoG alone can be used in modified, patent-free algorithm, tough the implementation could run a little slower
How is the SIFT descriptor used in object recognition?
This section summarizes the original SIFT algorithm and mentions a few competing techniques available for object recognition under clutter and partial occlusion. The SIFT descriptor is based on image measurements in terms of receptive fields over which local scale invariant reference frames are established by local scale selection.
How many SIFT features are needed for object description?
Object description by set of SIFT features is also robust to partial occlusion; as few as 3 SIFT features from an object are enough to compute its location and pose. Recognition can be performed in close-to-real time, at least for small databases and on modern computer hardware.