HDR imaging for feature tracking in challenging visibility scenes

Purpose – In visual-based applications, lighting conditions have a considerable impact on quality of the acquired images. Extremely low or high illuminated environments are a real issue for a majority of cameras due to limitations in their dynamic range. Indeed, over or under exposure might result in loss of essential information because of pixel saturation or noise. This can be critical in computer vision applications. High dynamic range (HDR) imaging technology is known to improve image rendering in such conditions. The purpose of this paper is to investigate the level of performance that can be achieved for feature detection and tracking operations in images acquired with a HDR image sensor. Design/methodology/approach – In this study, four different feature detection techniques are selected and tracking algorithm is based on the pyramidal implementation of Kanade-Lucas-Tomasi (KLT) feature tracker. Tracking algorithm is run over image sequences acquired with a HDR image sensor and with a high resoluti...

[1]  Shree K. Nayar,et al.  High dynamic range imaging: spatially varying pixel exposures , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  Shree K. Nayar,et al.  Radiometric self calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[4]  E. Reinhard Photographic Tone Reproduction for Digital Images , 2002 .

[5]  Rabab Kreidieh Ward,et al.  HDR image construction from multi-exposed stereo LDR images , 2010, 2010 IEEE International Conference on Image Processing.

[6]  Sang Uk Lee,et al.  Illumination and camera invariant stereo matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Michael Ashikhmin,et al.  A Tone Mapping Algorithm for High Contrast Images , 2002, Rendering Techniques.

[8]  N. Aouf,et al.  Enhanced feature detection and matching under extreme illumination conditions with a HDR imaging sensor , 2013, 2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS).

[9]  A. Ardeshir Goshtasby,et al.  Fusion of multi-exposure images , 2005, Image Vis. Comput..

[10]  Masahiro Okuda,et al.  High Dynamic Range image tone mapping based on local Histogram Equalization , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[11]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[12]  Maneesh Agrawala,et al.  Multiscale shape and detail enhancement from multi-light image collections , 2007, SIGGRAPH 2007.

[13]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[14]  Aníbal Ollero,et al.  Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs , 2009, J. Intell. Robotic Syst..

[15]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[16]  Holly E. Rushmeier,et al.  Tone reproduction for realistic images , 1993, IEEE Computer Graphics and Applications.

[17]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting , 2010 .

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

[19]  T. Aach,et al.  HDR-Microscopy of Cell Specimens: Imaging and Image Analysis , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.

[20]  Richard Szeliski,et al.  High dynamic range video , 2003, ACM Trans. Graph..

[21]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH.

[22]  Y. Shimodaira,et al.  Gradient Based Synthesized Multiple Exposure Time HDR Image , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[23]  Michael J. Black,et al.  The outlier process: unifying line processes and robust statistics , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[26]  James A. Ferwerda,et al.  Elements of Early Vision for Computer Graphics (Tutorial) , 2001, IEEE Computer Graphics and Applications.

[27]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

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

[29]  Pavel Zemcík,et al.  Feature point detection under extreme lighting conditions , 2013, SCCG.

[30]  Jinhua Wang,et al.  Extreme Learning Machine based exposure fusion for displaying HDR scenes , 2012, 2012 IEEE 11th International Conference on Signal Processing.

[31]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[32]  Kurt Konolige,et al.  Large-Scale Visual Odometry for Rough Terrain , 2007, ISRR.

[33]  Binoy Pinto,et al.  Speeded Up Robust Features , 2011 .

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

[35]  Takeshi Hashimoto,et al.  Gradient-Based Synthesized Multiple Exposure Time Color HDR Image , 2008, IEEE Transactions on Instrumentation and Measurement.

[36]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..