Image Based Quantitative Mosaic Evaluation with Artificial Video

  • Pekka Paalanen
  • Joni-Kristian Kämäräinen
  • Heikki Kälviäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


Interest towards image mosaicing has existed since the dawn of photography. Many automatic digital mosaicing methods have been developed, but unfortunately their evaluation has been only qualitative. Lack of generally approved measures and standard test data sets impedes comparison of the works by different research groups. For scientific evaluation, mosaic quality should be quantitatively measured, and standard protocols established. In this paper the authors propose a method for creating artificial video images with virtual camera parameters and properties for testing mosaicing performance. Important evaluation issues are addressed, especially mosaic coverage. The authors present a measuring method for evaluating mosaicing performance of different algorithms, and showcase it with the root-mean-squared error. Three artificial test videos are presented, ran through real-time mosaicing method as an example, and published in the Web to facilitate future performance comparisons.


Ground Truth Video Frame Point Spread Function Geometric Error Base Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Brown, M., Lowe, D.: Recognizing panoramas. In: ICCV, vol. 2 (2003)Google Scholar
  2. 2.
    Heikkilä, M., Pietikäinen, M.: An image mosaicing module for wide-area surveillance. In: ACM international workshop on Video Surveillance & Sensor Networks (2005)Google Scholar
  3. 3.
    Jia, J., Tang, C.K.: Image registration with global and local luminance alignment. In: ICCV, vol. 1, pp. 156–163 (2003)Google Scholar
  4. 4.
    Marzotto, R., Fusiello, A., Murino, V.: High resolution video mosaicing with global alignment. In: CVPR, vol. 1, pp. I–692–I–698 (2004)Google Scholar
  5. 5.
    Tian, G., Gledhill, D., Taylor, D.: Comprehensive interest points based imaging mosaic. Pattern Recognition Letters 24(9–10), 1171–1179 (2003)CrossRefzbMATHGoogle Scholar
  6. 6.
    Boutellier, J., Silvén, O., Korhonen, L., Tico, M.: Evaluating stitching quality. In: VISAPP (March 2007)Google Scholar
  7. 7.
    Möller, B., Garcia, R., Posch, S.: Towards objective quality assessment of image registration results. In: VISAPP (March 2007)Google Scholar
  8. 8.
    Petrović, V., Xydeas, C.: Objective image fusion performance characterisation. In: ICCV, vol. 2, pp. 1866–1871 (2005)Google Scholar
  9. 9.
  10. 10.
    Ortiz, A., Oliver, G.: Radiometric calibration of CCD sensors: Dark current and fixed pattern noise estimation. In: ICRA, vol. 5, pp. 4730–4735 (2004)Google Scholar
  11. 11.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pekka Paalanen
    • 1
  • Joni-Kristian Kämäräinen
    • 1
  • Heikki Kälviäinen
    • 1
  1. 1.Machine Vision and Pattern Recognition Research Group (MVPR) MVPR/Computational Vision GroupLappeenranta University of TechnologyKouvolaFinland

Personalised recommendations