Feature point detection under extreme lighting conditions

This paper evaluates the suitability of High Dynamic Range (HDR) imaging techniques for feature point detection under extreme lighting conditions. The conditions are extreme in respect to the dynamic range of the lighting within the test scenes used. This dynamic range cannot be captured using standard low dynamic range imagery techniques without loss of detail. Four widely used feature point detectors are used in the experiments: Harris corner detector, Shi-Tomasi, FAST and Fast Hessian. Their repeatability rate is studied under changes of camera viewpoint, camera distance and scene lighting with respect to the image formats used. The results of the experiments show that HDR imaging techniques improve the repeatability rate of feature point detectors significantly.

[1]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[2]  A. Koschan,et al.  COMPARISON AND EVALUATION OF FEATURE POINT DETECTORS , 2006 .

[3]  Kurt Debattista,et al.  Advanced High Dynamic Range Imaging: Theory and Practice , 2011 .

[4]  Didier Stricker,et al.  Robust Point Matching in HDRI through Estimation of Illumination Distribution , 2011, DAGM-Symposium.

[5]  Tobias Höllerer,et al.  Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking , 2011, International Journal of Computer Vision.

[6]  Haihong Li,et al.  Road extraction from aerial and satellite images by dynamic programming , 1995 .

[7]  Li Zhang,et al.  SURFACE RECONSTRUCTION ALGORITHMS FOR DETAILED CLOSE-RANGE OBJECT MODELING , 2006 .

[8]  H. Opower Multiple view geometry in computer vision , 2002 .

[9]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  C. Fraser,et al.  Interest operators for feature‐based matching in close range photogrammetry , 2010 .

[11]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  K. Wallis Seasonal Adjustment and Relations Between Variables , 1974 .

[13]  Pietro Perona,et al.  Evaluation of Features Detectors and Descriptors Based on 3D Objects , 2005, ICCV.

[14]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[15]  C. Fraser,et al.  INTEREST OPERATORS IN CLOSE-RANGE OBJECT RECONSTRUCTION , 2008 .

[16]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[17]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[18]  Horst Bischof,et al.  A novel performance evaluation method of local detectors on non-planar scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[19]  Óscar Martínez Mozos,et al.  A comparative evaluation of interest point detectors and local descriptors for visual SLAM , 2010, Machine Vision and Applications.

[20]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[21]  Qing Zhu,et al.  Automatic Image Mosaic-Building Algorithm for Generating Facade Textures , 2009 .

[22]  Alan Chalmers,et al.  Evaluation of tone mapping operators using a High Dynamic Range display , 2005, ACM Trans. Graph..

[23]  T. Ohdake,et al.  3D MODELING OF HIGH RELIEF SCULPTURE USING IMAGE BASED INTEGRATED MEASUREMENT SYSTEM , 2005 .