Fast background removal of JPEG images based on HSV polygonal cuts for a foot scanner device

  • T. TriganoEmail author
  • Y. Bechor
Original Research Paper


Foot scanning devices aim to provide information on a patient’s foot and to help in diagnosing issues to be corrected with orthoses. The Galaxy foot scanner developed by the Aetrex company aims to provide a computer-aided framework to help physicians in their diagnoses. As numerous embedded devices, used for image processing and 3D-reconstruction, it includes cameras which provide JPEG pictures of the object to reconstruct. In this framework, an important step is the segmentation of the image, to isolate the object of interest, but the JPEG compression introduces artifacts which can lower the performance of any segmentation procedure. In this paper, we suggest a model which takes the artifacts stemming from the JPEG compression into account. The pixels are first sorted into layers of pixels with similar value V in the HSV color space, and the background is modeled by a polygon from an additional picture. Segmentation based on the knowledge of the background and the layer to be processed is then performed. Results obtained with the Galaxy foot scanner illustrate that this method provides good results for segmentation, while being sufficiently fast to be implemented for near real-time applications.


Image segmentation JPEG compression Embedded devices HSV color space Foot scanners 



  1. 1.
    Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings of the 8th IEEE international conference on computer vision (ICCV). IEEE, Vancouver, 7-14 July 2001Google Scholar
  2. 2.
    Chen, J.J., Su, C.R., Grimson, W.E.G., Liu, J.L., Shiue, D.H.: Object segmentation of database images by dual multiscale morphological reconstructions and retrieval applications. IEEE Trans. Image Process. 21, 828–843 (2012). MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Faro, A., Giordano, D., Spampinato, C., Ullo, S., Di Stefano, A.: Basal ganglia activity measurement by automatic 3-D striatum segmentation in SPECT images. IEEE Trans. Instr. Measure. 60(10), 3269–3280 (2011)CrossRefGoogle Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 4th edn. Pearson, New York (2017)Google Scholar
  5. 5.
    Haines, T.S.F., Xiang, T.: Background Subtraction with Dirichlet Processes, pp. 99–113. Springer, Berlin Heidelberg (2012)Google Scholar
  6. 6.
    Li, C.T.: Multiresolution image segmentation integrating gibbs sampler and region merging algorithm. Signal Process. 83(1), 67–78 (2003)CrossRefzbMATHGoogle Scholar
  7. 7.
    Li, T.H.S., Wang, Y.H., Chen, C.C., Lin, C.J.: A fast color information setup using EP-like PSO for manipulator grasping color objects. IEEE Trans. Indus. Inform. 10(1), 645–654 (2014)CrossRefGoogle Scholar
  8. 8.
    Liu, Y., Payeur, P.: Robust Image-based detection of activity for traffic control. Can. J. Electr. Comput. Eng. 28(2), 63–67 (2003)CrossRefGoogle Scholar
  9. 9.
    Pears, N., Liu, Y., Bunting, P.: 3D Imaging, Analysis and Applications. Springer, New York (2012)CrossRefGoogle Scholar
  10. 10.
    Rother, C., Kolmogorov, V., Blake, A.: “GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph 23(3), 309–314 (2004a)CrossRefGoogle Scholar
  11. 11.
    Rother, C., Kolmogorov, V., Blake, A.: “GrabCut”: Interactive Foreground Extraction Using Iterated Graph Cuts. In: ACM SIGGRAPH 2004 Papers, ACM, New York, NY, USA, SIGGRAPH ’04, pp 309–314 (2004b)Google Scholar
  12. 12.
    Sigal, L., Sclaroff, S., Athitsos, V.: Skin color-based video segmentation under time-varying illumination. IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 862–877 (2004)CrossRefGoogle Scholar
  13. 13.
    Silva, A.S., Quintao Severgnini, F.M., Oliveira, M.L., Santiago Mendes, V.M., Assis Peixoto, Z.M.: Object tracking by color and active contour models segmentation. IEEE Latin Am. Trans. 14, 1488–1493 (2016). CrossRefGoogle Scholar
  14. 14.
    Telfer, S., Gibson, K.S., Hennessy, K., Steultjens, M.P., Woodburn, J.: Computer-aided design of customized foot orthoses: reproducibility and effect of method used to obtain foot shape. Arch. Phys. Med. Rehab. 93(5), 863–870 (2012)CrossRefGoogle Scholar
  15. 15.
    Wasserman, L.: All of Nonparametric Statistics (Springer Texts in Statistics). Springer, New York (2006)zbMATHGoogle Scholar
  16. 16.
    Wei, K., Jing, Z.L., Li, Y.X., Tuo, H.Y.: Extended scheme of chan-vese models for color image segmentation. IET Image Process. 5, 583–597 (2011). MathSciNetCrossRefGoogle Scholar
  17. 17.
    Yoon, I., Kim, S., Kim, D., Hayes, M.H., Paik, J.: Adaptive defogging with color correction in the HSV color space for consumer surveillance system. IEEE Trans. Consum. Electr. 58, 111–116 (2012). CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringShamoon College of EngineeringAshdodIsrael
  2. 2.Aetrex IsraelNes ZionaIsrael

Personalised recommendations