Automatically Detecting Protruding Objects When Shooting Environmental Portraits

  • Pei-Yu Lo
  • Sheng-Wen Shih
  • Jen-Chang Liu
  • Jen-Shin Hong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)


This study proposes techniques for detecting unintentional protruding objects from a subject’s head in environmental portraits. The protruding objects are determined based on the color and edge information of the background regions adjacent to the head regions in an image sequence. The proposed algorithm consists of watershed segmentation and KLT feature tracking model for extracting foreground regions, a ROI (Region of Interest) extracting model based on face detection results, and a protruding object detection model based on the color clusters and edges of the background regions inside the ROI. Experimental evaluations using four test videos with different backgrounds, lighting conditions, and head ornaments show that the average detection rate and false detection rate of the proposed algorithm are 87.40% and 12.11% respectively.


Photo Composition Protruding Object Computational Photography 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, L., Chen, R., Wolf, L., Cohen, D.: Optimizing Photo Composition. Computer Graphics Forum 29-2, 469–478 (2010)CrossRefGoogle Scholar
  2. 2.
    Miyake, T., Soga, T.: Digital Still Camera with Composition Advising Function, and Method of Controlling Operation of Same. Fujifilm Corporation, United States Patent, Patent Number: 7317458 (2008)Google Scholar
  3. 3.
    Suarez, L.A.F.: Picture Composition Guidance System. Sony Corporation, Sony Electronics Inc., United States Patent, Patent Number: 5873007 (1999)Google Scholar
  4. 4.
    Shen, C.T., Liu, J.C., Shih, S.W., Hong, J.S.: Towards Intelligent Photo Composition-Automatic Detection of Unintentional Dissection Lines in Environmental Portrait Photos. Expert Systems with Applications 36, 9024–9030 (2009)CrossRefGoogle Scholar
  5. 5.
    Cavalcanti, C., Gomes, H., Meireles, R., Guerra, W.: Towards Automating Photographic Composition of People. In: IASTED International Conference on Visualization, Imaging, and Image Processing, pp. 25–30 (2006)Google Scholar
  6. 6.
    Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 583–598 (1991)CrossRefGoogle Scholar
  7. 7.
    Meyer, F.: Color Image Segmentation. In: International Conference on Image Processing and its Applications, pp. 303–306 (2002)Google Scholar
  8. 8.
    Tomasi, C., Kanade, T.: Detection and Tracking of Point Features. Carnegie Mellon University, Technical Report CMU-CS-91-132 (1991)Google Scholar
  9. 9.
    Shi, J., Tomasi, C.: Good Features to Track. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar
  10. 10.
    McKenna, S.J., Jabri, S., Duric, Z., Wechsler, H., Rosenfeld, A.: Tracking Groups of People. Computer Vision and Image Understanding 80, 42–56 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Viola, P., Jones, M.: Rapid Objects Detection using a Boosted Cascade of Simple Features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  12. 12.
    Canny, F.J.: A Computational Approach to Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)CrossRefGoogle Scholar
  13. 13.
    Lai, C.Y., Chen, P.H., Shih, S.W., Liu, Y., Hong, J.S.: Computational Models and Experimental Investigations of Effects of Balance and Symmetry on the Aesthetics of Text-Overlaid Images. International Journal of Human Computer Studies 68(1-2), 41–56 (2010)CrossRefGoogle Scholar
  14. 14.
    Byers, Z., Dixon, M., Smart, W.D., Grimm, C.: Say Cheese! Experiences with a Robot Photographer. AI Magazine 25(3), 37–46 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pei-Yu Lo
    • 1
  • Sheng-Wen Shih
    • 1
  • Jen-Chang Liu
    • 1
  • Jen-Shin Hong
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Chi Nan UniversityPuliTaiwan

Personalised recommendations