libsift 1.8
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libsift 1.8 Ranking & Summary
File size:
0.15 MB
Platform:
Any Platform
License:
GPL (GNU General Public License)
Price:
Downloads:
1384
Date added:
2006-01-21
Publisher:
Sebastian Nowozin
libsift 1.8 description
libsift is an algorithm to identify and locate interesting points within an image. For all such points, a descriptive signature is extracted.
The signatures can be stored and matched among multiple images, allowing for a large number of interesting applications, such as aligning overlapping images and identifying objects or motion within image sequences.
libsift is used by the autopano-sift program to create panorama images.
This library is a 100% C# implementation of the SIFT algorithm ("Scale-Invariant Feature Transform") and additional matching algorithms. The SIFT algorithm is an image feature location and extraction algorithm which provides the following key advantages over similar algorithms:
- Combined feature location and extraction algorithm.
- The keypoint locations are more precise and repeatable, because SIFT uses subpixel localization and multiple scale keypoint identification
- The descriptors are highly distinctive. For example, I tested with up to 300,000 keypoints while matching a large 60 picture panorama and it did not have a single invalid match.
- The feature vectors can be efficiently correlated using probabilistic algorithms like Best-Bin-First kd-tree search.
Enhancements:
- A serious bug in the Gaussian sigma computation and a bug in the DoG scale handling were fixed.
The signatures can be stored and matched among multiple images, allowing for a large number of interesting applications, such as aligning overlapping images and identifying objects or motion within image sequences.
libsift is used by the autopano-sift program to create panorama images.
This library is a 100% C# implementation of the SIFT algorithm ("Scale-Invariant Feature Transform") and additional matching algorithms. The SIFT algorithm is an image feature location and extraction algorithm which provides the following key advantages over similar algorithms:
- Combined feature location and extraction algorithm.
- The keypoint locations are more precise and repeatable, because SIFT uses subpixel localization and multiple scale keypoint identification
- The descriptors are highly distinctive. For example, I tested with up to 300,000 keypoints while matching a large 60 picture panorama and it did not have a single invalid match.
- The feature vectors can be efficiently correlated using probabilistic algorithms like Best-Bin-First kd-tree search.
Enhancements:
- A serious bug in the Gaussian sigma computation and a bug in the DoG scale handling were fixed.
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