Difference between revisions of "Performance:SURF"
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= Descriptor Stability = | = Descriptor Stability = | ||
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<gallery caption="Images from evaluation data set" heights=150 widths=200 > | |||
Image:Performance-descriptor-Graffiti.jpg|Graffiti | |||
Image:Performance-descriptor-Boat.jpg|Boat | |||
Image:Performance-descriptor-Trees.jpg|Trees | |||
Image:Performance-descriptor-Bricks.jpg|Bricks | |||
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Tests were performed using standardized test images from [http://www.robots.ox.ac.uk/~vgg/research/affine/], which have known transformations. Because the transformation between images is known this allows the true associations to be known. Stability was measured based upon the number of correct associations between two images in the dataset. The testing procedure is summarized below: | Tests were performed using standardized test images from [http://www.robots.ox.ac.uk/~vgg/research/affine/], which have known transformations. Because the transformation between images is known this allows the true associations to be known. Stability was measured based upon the number of correct associations between two images in the dataset. The testing procedure is summarized below: | ||
Revision as of 05:22, 25 October 2011
SURF Performance in BoofCV
The SURF descriptor is a state of the art image region descriptor that is scale, orientation, and illumination invariant. By using an integral image it can be computed efficiently across different scales. Inside of BoofCV SURF can be configured many different ways to create several variants. To ensure correctness and optimal performance a study has been performed comparing its performance against other open source libraries as well as itself.
Tested Implementations:
- BoofCV: SURF
- See FactoryDescribeRegionPoint.surf()
- BoofCV: MSURF
- See FactoryDescribeRegionPoint.msurf()
- OpenSURF
- Build 27/05/2010
- http://www.chrisevansdev.com/computer-vision-opensurf.html
- Reference Implementation
Descriptor Stability
- Images from evaluation data set
Graffiti
Boat
Trees
Bricks
Tests were performed using standardized test images from [1], which have known transformations. Because the transformation between images is known this allows the true associations to be known. Stability was measured based upon the number of correct associations between two images in the dataset. The testing procedure is summarized below:
- For each image, detect features (scale and location) using the fast Hessian detector in BoofCV.
- For each image, compute a feature description for all found features.
- In each image sequence, associate features in the first image to the Nth image, where N > 1.
- Association is done by minimizing Euclidean error
- Validation is done using reverse association. E.g. This association must be the optimal association going from frame 1 to N and N to 1.
- Compute the number of correct associations.
- An association is correct if it is within 3 pixels of the true location.
Since the transformation is known between images the true location could have been used. However, in reality features will not lie at the exact point and a descriptor needs to be tolerant to this type of errors. Thus this is a more accurate measure of the description's strength.
