Image and Vision Computing
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Comparing and Evaluating Interest Points
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Curvature Scale Space for Robust Image Corner Detection
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A Mathematical Comparison of Point Detectors
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 11 - Volume 11
Comparing Cornerness Measures for Interest Point Detection
IMVIP '08 Proceedings of the 2008 International Machine Vision and Image Processing Conference
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Interest points are widely used in computer vision applications such as camera calibration, robot localization and object tracking that require fast and efficient feature matching. A large number of techniques have been proposed in the literature. This paper evaluates the state of art techniques for interest point detection including excution time and suitability for real time applications. Such comparative study is crucial for specific applications, since it is always necessary to understand the advantages and disadvantages of the existing techniques so that best possible ones can be selected. The comparative stully shows that: (1) the CSS method performs best in corner extraction. It is the fast and the most reliable and has the lowest noise sensitivity with the highest true corner detection rate, even though it still detects some false corners; (2) SUSAN detector would be the second choice and is acceptable and useful in applications requiring a computationally efficient detector and working on a restricted set of images.