Signal, Image and Video Processing

, Volume 9, Issue 4, pp 923–940 | Cite as

Visual detection in omnidirectional view sensors

  • Nguan Soon Chong
  • Yau Hee Kho
  • Mou Ling Dennis Wong
Original Paper


In recent years, the use of omnidirectional view (OV) sensors has gained popularity in robotics. The main reason behind this growth is due to the large field of view (FOV) that spans \(360^{\circ }\) offered by these sensors under a catadioptric configuration. The large FOV addresses several shortcomings of a conventional perspective imaging sensor by allowing simultaneous monitoring of surrounding environment under a single image compilation. Feature detection is one of the fundamental components in visual robotics applications that enable intelligent vision system with advanced features such as object, scene, and human detection, localisation, simultaneous localisation and mapping, and odometry. In this paper, the adaptation of visual detection algorithm in omnidirectional vision is reviewed by investigating the recent works and the underlying supporting mechanism. Furthermore, state-of-the-art vision detection algorithms and important factors of OV sensors, such as hardware requirements, fundamental theories, cost, and usability, are also investigated in order to explain the adaptation involved. To conclude this work, a case study related to OV mapping transform is presented, and insights on possible future research direction are provided.


Omnidirectional view sensor Feature detection Machine Vision View unwrapping  



Nguan Soon Chong thanks Swinburne University of Technology (Sarawak Campus) for his Ph.D. studentship.


  1. 1.
    Advanced Micro Devices, Inc.: AMD Stream (2012)Google Scholar
  2. 2.
    Rituerto, A., Puig, L., Guerrero, J.J.: Comparison of omnidirectional and conventional monocular systems for visual slam. In: The Proceedings of the 10th Workshop on Omnidirectional Vision (OMNIVIS) in Conjunction with Robotics Systems and Science RSS (2010)Google Scholar
  3. 3.
    Andreasson, H., Treptow, A., Duckett, T.: Self-localization in non-stationary environments using omni-directional vision. Rob. Auton. Syst. 55, 541–551 (2007). doi: 10.1016/j.robot.2007.02.002. Google Scholar
  4. 4.
    Arican, Z., Frossard, P.: Super-resolution from unregistered omnidirectional images. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (2008). doi: 10.1109/ICPR.2008.4760988
  5. 5.
    Arican, Z., Frossard, P.: Scale-invariant features and polar descriptors in omnidirectional imaging. IEEE Trans. Image Process. 21(5), 2412–2423 (2012). doi: 10.1109/TIP.2012.2185937 CrossRefMathSciNetGoogle Scholar
  6. 6.
    Baker, S., Nayar, S.K.: A theory of catadioptric image formation. In: Proceedings of the Sixth International Conference on Computer Vision, 1998, pp. 35–42. Bombay, India (1998). doi: 10.1109/ICCV.1998.710698
  7. 7.
    Baker, S., Nayar, S.K.: A theory of single-viewpoint catadioptric image formation. Int. J. Comput. Vis. 35(2), 175–196 (1999)CrossRefGoogle Scholar
  8. 8.
    Bay, H., Ess, A., Tuytelaars, T., Vangool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). Google Scholar
  9. 9.
    Bay, H., Tuytelaars, T., Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision ECCV 2006, Lecture Notes in Computer Science, vol. 3951, pp. 404–417. Springer, Berlin (2006). doi: 10.1007/11744023_32
  10. 10.
    Bermúdez, J., Puig, L., Guerrero, J.J.: Line extraction in central hyper-catadioptric systems. In: Proccedings of the OMNIVIS —10th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras (2010)Google Scholar
  11. 11.
    Blaer, P., Allen, P.: Topological mobile robot localization using fast vision techniques. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA ’02, vol. 1, pp. 1031–1036 (2002). doi: 10.1109/ROBOT.2002.1013491
  12. 12.
    Brown, M., Lowe, D.G.: Invariant features from interest point groups. In: British Machine Vision Conference, pp. 656–665. Cardiff, Wales (2002)Google Scholar
  13. 13.
    Bulow, T.: Spherical diffusion for 3d surface smoothing. IEEE Trans Pattern Anal. Mach. Intell. 26(12), 1650–1654 (2004). doi: 10.1109/TPAMI.2004.129 CrossRefGoogle Scholar
  14. 14.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Proceedings of the 11th European conference on Computer vision: Part IV, ECCV’10, pp. 778 –792 (2010)Google Scholar
  15. 15.
    Chen, C.H., Yao, Y., Page, D., Abidi, B., Koschan, A., Abidi, M.: Heterogeneous fusion of omnidirectional and PTZ cameras for multiple object tracking. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1052–1063 (2008). doi: 10.1109/TCSVT.2008.928223 CrossRefGoogle Scholar
  16. 16.
    Chong, N.S., Kho, Y.H., Wong, M.L.D.: Closed form spherical omnidirectional image unwrapping. In: Proceedings of The IET Conference on Image Processing (IPR 2012), pp. 1–5. London, UK (2012a). doi: 10.1049/cp.2012.0443
  17. 17.
    Chong, N.S., Kho, Y.H., Wong, M.L.D.: Detection of sift keypoints in spherical omnidirectional view sensor. Procedia Eng. 41, 90–96 (2012) (International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)). doi: 10.1016/j.proeng.2012.07.147 Google Scholar
  18. 18.
    Cruz-Mota, J., Bogdanova, I., Paquier, B., Bierlaire, M., Thiran, J.P.: Scale invariant feature transform on the sphere: theory and applications. Int. J. Comput. Vis. 98, 217–241 (2012). doi: 10.1007/s11263-011-0505-4 CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Ding, Y., Xiao, J., Yu, J.: A theory of multi-perspective defocusing. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’11, pp. 217–224. IEEE Computer Society, Washington, DC, USA (2011). doi: 10.1109/CVPR.2011.5995617
  20. 20.
    Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972). doi: 10.1145/361237.361242 CrossRefzbMATHGoogle Scholar
  21. 21.
    Gaspar, J., Santos-Victor, J.: Visual path following with a catadioptric panoramic camera. In: Proceedings of the International Symposium on Intelligent Robotic Systems—SIRS’99. Coimbra, Portugal (1999). doi:
  22. 22.
    Geyer, C., Daniilidis, K.: Catadioptric camera calibration. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 398–404. Kerkyra, Greece (1999). doi: 10.1109/ICCV.1999.791248
  23. 23.
    Geyer, C., Daniilidis, K.: A unifying theory for central panoramic systems and practical applications. In: Proceedings of the 6th European Conference on Computer Vision-Part II, ECCV ’00, pp. 445–461. London, UK (2000).
  24. 24.
    Geyer, C., Daniilidis, K.: Paracatadioptric camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 687–695 (2002). doi: 10.1109/34.1000241 CrossRefGoogle Scholar
  25. 25.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 26(2), 147–160 (1950)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Fourth Alvey Vision Conference, pp. 147–151. Manchester, UK (1988)Google Scholar
  27. 27.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  28. 28.
    Hecht, E.: Optics, 4th edn. Addison Wesley, Reading, MA (2001)Google Scholar
  29. 29.
    Hicks RA, Bajcsy R (2001) Reflective surfaces as computational sensors. Image Vis. Comput. 19(11), 773–777.
  30. 30.
    Hong, J., Tan, X., Pinette, B., Weiss, R., Riseman, E.M.: Image-based homing. IEEE Control Syst. 12(1), 38–45 (1992)CrossRefGoogle Scholar
  31. 31.
    Intel Corporation, Willow Garage, Itseez: OpenCV. Version 2.4.3 (2013)Google Scholar
  32. 32.
    Jeng, S.W., Tsai, W.H.: Using pano-mapping tables for unwarping of omni-images into panoramic and perspective-view images. IET Image Process. 1(2), 49–155 (2007). doi: 10.1049/iet-ipr:20060201
  33. 33.
    Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 506–513. Los Alamitos, CA, USA (2004). doi: 10.1109/CVPR.2004.183
  34. 34.
    Koenderink, J.J.: The structure of images. Biol. Cybern. 50(5), L363–L370 (1984). doi: 10.1007/BF00336961 CrossRefMathSciNetGoogle Scholar
  35. 35.
    Lei, J., Du, X., Zhu, Y.F., Liu, J.L.: Unwrapping and stereo rectification for omnidirectional images. J. Zhejiang Univ. Sci. A 10(8), 1125–1139 (2009). Google Scholar
  36. 36.
    Li, W., Li, Y.: Overall well-focused catadioptric image acquisition with multifocal images: a model-based method. IEEE Trans. Image Process. 21(8), 3697–3706 (2012). doi: 10.1109/TIP.2012.2195010 CrossRefMathSciNetGoogle Scholar
  37. 37.
    Li, W., Li, Y., Wu, Y.: A model based method for overall well focused catadioptric image acquisition with multi-focal images. In: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns, CAIP ’09, pp. 460–467. Springer, Berlin (2009). doi: 10.1007/978-3-642-03767-2_56
  38. 38.
    Li, Y., Zhang, M., Lou, J., Wang, W.: Design of catadioptric omnidirectional imaging system for defocus deblurring. Acta. Optica. Sinica. 32(9), 0911001 (2012). doi: 10.3788/AOS201232.0911001 Google Scholar
  39. 39.
    Lindeberg, T.: Scale-space theory: a basic tool for analysing structures at different scales. J. Appl. Stat. 21(2), 224–270 (1994)Google Scholar
  40. 40.
    Lourenco, M., Pedro, V., Barreto, J.: Localization in indoor environments by querying omnidirectional visual maps using perspective images. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 2189–2195 (2012). doi: 10.1109/ICRA.2012.6225134
  41. 41.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  42. 42.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004). doi: 10.1023/B:VISI.0000029664.99615.94
  43. 43.
    Mair, E., Hager, G., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision ECCV 2010, Lecture Notes in Computer Science, vol. 6312, pp. 183–196. Springer, Berlin (2010). doi: 10.1007/978-3-642-15552-9_14
  44. 44.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)CrossRefGoogle Scholar
  45. 45.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1615–1630 (2005). doi: 10.1109/TPAMI.2005.188. Google Scholar
  46. 46.
    Moravec, H.: Obstacle avoidance and navigation in the real world by a seeing robot rover. In: Technical Report CMU-RI-TR-80-03. Robotics Institute, Carnegie Mellon University and Doctoral Dissertation, Stanford University, CMU-RI-TR-80-03 (1980)Google Scholar
  47. 47.
    Murillo, A.C., Campo, P., Kosecka, J., Guerrero, J.J.: Gist vocabularies in omnidirectional images for appearance based mapping and localization. In: Proccedings of the OMNIVIS—10th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras (2010)Google Scholar
  48. 48.
    Nayar, S.K.: Catadioptric omnidirectional camera. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 482–488 (1997). doi: 10.1109/CVPR.1997.609369
  49. 49.
    Nene, S.A., Nayar, S.K.: Stereo with mirrors. In: Proceedings of Sixth International Conference on Computer Vision, pp. 1087–1094 (1998). doi: 10.1109/ICCV.1998.710852
  50. 50.
    NVIDIA Corporation: NVIDIA CUDA (2012)Google Scholar
  51. 51.
    Oliva, A., Torralba, A.: Building the gist of a scene : the role of global image features in recognition. Brain 155(1), 23–36 (2006). Google Scholar
  52. 52.
    Peng, Y., Liu, Y., Li, Y., Zhang, M.: Coded aperture techniques for catadioptric omni-directional image defocus deblurring. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3373–3377. Seoul, Korea (2012). doi: 10.1109/ICSMC.2012.6378313
  53. 53.
    Puig, L., Guerrero, J., Sturm, P.: Matching of omnidirectional and perspective images using the hybrid fundamental matrix. In: Proceedings of the 8th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras, OMNIVIS 2008. Marseille, France (2008)Google Scholar
  54. 54.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986). doi: 10.1023/A:1022643204877 Google Scholar
  55. 55.
    Ramalingam, S., Bouaziz, S., Sturm, P., Brand, M.: Geolocalization using skylines from omni-images. In: Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 23–30 (2009). doi: 10.1109/ICCVW.2009.5457723
  56. 56.
    Rees, D.W.: Patent 3505465 (1970)Google Scholar
  57. 57.
    Rosenfeld, A., Pfaltz, J.L.: Distance functions on digital pictures. Pattern Recognit. 1(1), 33–61 (1968).
  58. 58.
    Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. IEEE Int. Conf. Comput. Vis. 2, 1508–1511 (2005). doi: 10.1109/ICCV.2005.104
  59. 59.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. Eur. Conf. Comput. Vis. 1, 430–443 (2006). doi: 10.1007/11744023_34
  60. 60.
    Scaramuzza, D., Martinelli, A., Siegwart, R.: A toolbox for easily calibrating omnidirectional cameras. In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5695–5701. Beijing, China (2006). doi: 10.1109/IROS.2006.282372
  61. 61.
    Scaramuzza, D., Siegwart, R.: Appearance-guided monocular omnidirectional visual odometry for outdoor ground vehicles. IEEE Trans. Robot. 24(5), 1015–1026 (2008). doi: 10.1109/TRO.2008.2004490 CrossRefGoogle Scholar
  62. 62.
    Scaramuzza, D., Siegwart, R., Martinelli, A.: A robust descriptor for tracking vertical lines in omnidirectional images and its use in mobile robotics. Int. J. Robot. Res. 28(2), 149–171 (2009). doi: 10.1177/0278364908099858 Google Scholar
  63. 63.
    Schroth, G., Huitl, R., Chen, D., Abu-Alqumsan, M., Al-Nuaimi, A., Steinbach, E.: Mobile visual location recognition. IEEE Sig. Process. Mag. 28(4), 77–89 (2011). Google Scholar
  64. 64.
    Shabayek, A., Morel, O., Fofi, D.: Auto-calibration and 3d reconstruction with non-central catadioptric sensors using polarization imaging. In: The Proceedings of the 10th Workshop on Omnidirectional Vision (OMNIVIS) in conjunction with Robotics Systems and Science RSS. Zaragoza, Spain (2010)Google Scholar
  65. 65.
    Sturm, P.: Mixing catadioptric and perspective cameras. In: Proceedings of the Third Workshop on Omnidirectional Vision, 2002, pp. 37–44 (2002). doi: 10.1109/OMNVIS.2002.1044489
  66. 66.
    Sturzl, W., Srinivasan, M.V.: Omnidirectional imaging system with constant elevational gain and single viewpoint. In: Proccedings of the OMNIVIS—10th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras, pp. 1–7. Zaragoza, Spain (2010)Google Scholar
  67. 67.
    Svoboda, T., Pajdla, T.: Epipolar geometry for central catadioptric cameras. Int. J. Comput. Vis. 49, 23–37 (2002). doi: 10.1023/A:1019869530073. Google Scholar
  68. 68.
    Swaminathan, R.: Focus in catadioptric imaging systems. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–7 (2007). doi: 10.1109/ICCV.2007.4409205
  69. 69.
    Swaminathan, R., Grossberg, M.D., Nayar, S.K.: Non-single viewpoint catadioptric cameras: geometry and analysis. Int. J. Comput. Vis. 66(3), 211–229 (2001)CrossRefGoogle Scholar
  70. 70.
    Tamimi, H., Andreasson, H., Treptow, A., Duckett, T., Zell, A.: Localization of mobile robots with omnidirectional vision using particle filter and iterative sift. Robot. Auton. Syst. 54(9), 758–765 (2006). Google Scholar
  71. 71.
    The MathWorks Inc.: MATLAB (2011). Version 7.14 (R2012a)Google Scholar
  72. 72.
    Ulrich, I., Nourbakhsh, I.: Appearance-based place recognition for topological localization. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA ’00, vol. 2, pp. 1023–1029 (2000). doi: 10.1109/ROBOT.2000.844734
  73. 73.
    Wang, M.L., Huang, C.C., Lin, H.Y.: An intelligent surveillance system based on an omnidirectional vision sensor. In: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, pp. 1–6 (2006). doi: 10.1109/ICCIS.2006.252312
  74. 74.
    Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series. The MIT Press, Cambridge (1964)Google Scholar
  75. 75.
    Winters, N., Gaspar, J., Lacey, G., Santos-Victor, J.: Omni-directional vision for robot navigation. In: Proceedings of the IEEE Workshop on Omnidirectional Vision, pp. 21–28 (2000). doi: 10.1109/OMNVIS.2000.853799
  76. 76.
    Yagi, Y.: Omnidirectional sensing and its applications. IEICE Trans. Inf. Syst. E82–D, 568–579 (1999)Google Scholar
  77. 77.
    Yagi, Y., Kawato, S.: Panorama scene analysis with conic projection. In: Proceedings of the IEEE International Workshop on Intelligent Robots and Systems ’90. ’Towards a New Frontier of Applications’, IROS ’90, vol. 1, pp. 181–187 (1990). doi: 10.1109/IROS.1990.262385
  78. 78.
    Yamazawa, K., Yagi, Y., Yachida, M.: Omnidirectional imaging with hyperboloidal projection. In: Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems ’93, IROS ’93, vol. 2, pp. 1029–1034. Tokyo, Japan (1993). doi: 10.1109/IROS.1993.583287
  79. 79.
    Ying, X., Hu, Z.: Catadioptric camera calibration using geometric invariants. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1260–1271 (2004). doi: 10.1109/TPAMI.2004.79 CrossRefGoogle Scholar
  80. 80.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000). doi: 10.1109/34.888718 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Nguan Soon Chong
    • 1
  • Yau Hee Kho
    • 1
  • Mou Ling Dennis Wong
    • 1
  1. 1.Faculty of Engineering, Computing and ScienceSwinburne University of Technology Sarawak Campus KuchingMalaysia

Personalised recommendations