Feature Extraction and Pattern Recognition from Fisheye Images in the Spatial Domain

Feature extraction for pattern recognition is a very common task in image analysis and computer vision. Most of the work has been reported for images / image sequences acquired by perspective cameras. This paper discusses the algorithms for feature extraction and pattern recognition in images acquired by omnidirectional (fisheye) cameras. Work has been reported using operators in the frequency domain, which in the case of fisheye/omnidirectional images involves spherical harmonics. In this paper we review the recent literature, including relevant results from our team and state the position that features can be extracted from spherical images, by modifying the existing operators in the spatial domain, without the need to correct the image for distortions.

[1]  Jean-Philippe Thiran,et al.  Scale Invariant Feature Transform on the Sphere: Theory and Applications , 2011, International Journal of Computer Vision.

[2]  Jake K. Aggarwal,et al.  Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation , 1996, Pattern Recognit..

[3]  T. Bulow.  Spherical diffusion for 3D surface smoothing , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Kostas Daniilidis,et al.  Catadioptric Projective Geometry , 2001, International Journal of Computer Vision.

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

[6]  Vassilis P. Plagianakos,et al.  Refinement of human silhouette segmentation in omni-directional indoor videos , 2014, Comput. Vis. Image Underst..

[7]  Wei Feng,et al.  SPHORB: A Fast and Robust Binary Feature on the Sphere , 2014, International Journal of Computer Vision.

[8]  Vassilis P. Plagianakos,et al.  Geodesically-corrected Zernike descriptors for pose recognition in omni-directional images , 2016, Integr. Comput. Aided Eng..

[9]  Peter I. Corke,et al.  Scale invariant feature matching with wide angle images , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Tom Duckett,et al.  Localization for Mobile Robots using Panoramic Vision, Local Features and Particle Filter , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[11]  Kohei Inoue,et al.  Gradient operators for feature extraction from omnidirectional panoramic images , 2015, Pattern Recognit. Lett..

[12]  Kostas Delibasis,et al.  A fall detection algorithm for indoor video sequences captured by fish-eye camera , 2015, 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE).

[13]  Hongdong Li,et al.  Plane-Based Calibration and Auto-calibration of a Fish-Eye Camera , 2006, ACCV.