On recall rate of interest point detectors
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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On recall rate of interest point detectors. / Aanæs, Henrik; Lindbjerg Dahl, Anders; Pedersen, Kim Steenstrup.
Electronic Proceedings of 3DPVT'10: The Fifth International Symposium on 3D Data Processing, Visualization and Transmission. 2010. p. 1-8.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - On recall rate of interest point detectors
AU - Aanæs, Henrik
AU - Lindbjerg Dahl, Anders
AU - Pedersen, Kim Steenstrup
N1 - Conference code: 5
PY - 2010
Y1 - 2010
N2 - In this paper we provide a method for evaluating interest point detectors independently of image descriptors. This is possible because we have compiled a unique data set enabling us to determine if common interest points are found. The data contains 60 scenes of a wide range of object types, and for each scene we have 119 precisely located camera positions obtained from a camera mounted on an industrial robot arm. The scene surfaces have been scanned using structured light, providing precise 3Dground truth. We have investigated a number of the most popular interest point detectors where we systematically have varied camera position, light and model parameters. The overall conclusion is that the Harris and Hessian corner detectors perform well followed by MSER, whereas the FAST cornerdetector, IBR and EBR performs poorly. Furthermore, only the number of interest points change with changing parameters - not the correct matches.
AB - In this paper we provide a method for evaluating interest point detectors independently of image descriptors. This is possible because we have compiled a unique data set enabling us to determine if common interest points are found. The data contains 60 scenes of a wide range of object types, and for each scene we have 119 precisely located camera positions obtained from a camera mounted on an industrial robot arm. The scene surfaces have been scanned using structured light, providing precise 3Dground truth. We have investigated a number of the most popular interest point detectors where we systematically have varied camera position, light and model parameters. The overall conclusion is that the Harris and Hessian corner detectors perform well followed by MSER, whereas the FAST cornerdetector, IBR and EBR performs poorly. Furthermore, only the number of interest points change with changing parameters - not the correct matches.
M3 - Article in proceedings
SP - 1
EP - 8
BT - Electronic Proceedings of 3DPVT'10
T2 - 5th International Symposium 3D Data Processing, Visualization and Transmission
Y2 - 17 May 2010 through 20 May 2010
ER -
ID: 18947612