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Embedded planar surface segmentation system for stereo images

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Abstract

An embedded system is developed to segment stereo images using disparity. The recent developments in the embedded system architecture have allowed real time implementation of low-level vision tasks such as stereo disparity computation. At the same time, an intermediate level task such as segmentation is rarely attempted in an embedded system. To solve the planar surface segmentation problem, which is iterative in nature, our system implements a Segmentation–Estimation framework. In the segmentation phase, segmentation labels are assigned based on the underlying plane parameters. Connected component analysis is carried out on the segmentation result to select the largest spatially connected area for each plane. From the largest areas, the parameters for each plane are reestimated. This iterative process was implemented on TMS320DM642 based embedded system that operates at 3–5 frames per second on images of size 320 × 240.

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References

  1. Altunbasak Y., Tekalp A., Bozdagi G.: Simultaneous motion-disparity estimation and segmentation from stereo. IEEE Int. Conf. Image Process. 3, 73–77 (1994)

    Google Scholar 

  2. Besag J.: On the statistical analysis of dirty pictures. J. R. Stat. Soc. B (Methodological) 48(3), 259–302 (1986)

    MATH  MathSciNet  Google Scholar 

  3. Burschka, D., Hager, G.: Scene classification from dense disparity maps in indoor environments. In: 16th International Conference on Pattern Recognition, vol. 3, pp. 708–712 (2002)

  4. Corso, J., Burschka, D., Hager, G.: Direct plane tracking in stereo image for mobile navigation. In: International Conference on Robotics and Automation (2003)

  5. Ding Y., Ping X., Hu M., Wang D.: Range image segmentation based on randomized hough transform. Pattern Recognit. Lett. 26(13), 2033–2041 (2005)

    Article  Google Scholar 

  6. Granston, E.: Hand-tuning loops and control code on the TMS320C6000. Tech. rep., Texas Instruments (2006). http://focus.ti.com/lit/an/spra666/spra666.pdf

  7. Haralick R.M., Shapiro L.G.: Computer and Robot Vision. Addison-Wesley Longman Publishing Co. Inc., Boston (1992)

    Google Scholar 

  8. Harris, C., Stephens, M.: A combined corner and edge detector. In: The Fourth Alvey Vision Conference, Manchester, pp. 147–151 (1988)

  9. Hartley R.I., Zisserman A.: Multiple View Geometry in Computer Vision,2nd edn. Cambridge University Press, Cambridge (2004)

    MATH  Google Scholar 

  10. Hoover A., Jean-Baptiste G., Jiang X., Flynn P.J., Bunke H., Goldgof D.B., Bowyer K., Eggert D.W., Fitzgibbon A., Fisher R.B.: An experimental comparison of range image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 673–689 (1996)

    Article  Google Scholar 

  11. Izquierdo E.: Disparity/segmentation analysis: matching with an adaptive windowand depth-driven segmentation. Circuits Syst Video Technol IEEE Trans 9(4), 589–607 (1999)

    Article  Google Scholar 

  12. Kanazawa, Y., Kawakami, H.: Detection of planar regions with uncalibrated stereo using distribution of feature points. In: British Machine Vision Conference, London, vol. 1, pp. 247–256 (2004)

  13. Kehtarnavaz, N.: Real-Time Digital Signal Processing: Based on the TMS320C6000. Newnes, Oxford (2004)

  14. Konolige, K.: Small vision system: Hardware and implementation. In: Eighth International Symposium on Robotics Research (1997)

  15. Li S.Z.: Markov Random Field Modeling in Computer Vision. Springer, London (1995)

    Google Scholar 

  16. Mühlmann K., Maier D., Hesser J., Männer R.: Calculating dense disparity maps from color stereo images, an efficient implementation. Int. J. Comput. Vision 47(1), 79–88 (2002)

    Article  MATH  Google Scholar 

  17. Middlebury Stereo Vision Page. http://www.middlebury.edu/stereo

  18. Molton N., Se S., Brady J., Lee D., Probert P.: A stereo vision-based aid for the visually impaired. Image Vision Comput. 16, 251–263 (1998)

    Article  Google Scholar 

  19. Okada K., Kagami S., Inaba M., Inoue H.: Plane segment finder: algorithm, implementation and applications. IEEE Int. Conf. Rob. Autom. 2, 2120–2125 (2001)

    Google Scholar 

  20. Qureshi S.: Embedded Image Processing on the TMS320C6000 DSP. Springer, Berlin (2005)

    Google Scholar 

  21. Klette R., Schlüns K.A.K.: Computer Vision: Three-Dimensional Data from Images. Springer, Berlin (1998)

    MATH  Google Scholar 

  22. Schindler, K., Suter, D.: Two-view multibody structure-and-motion with outliers through model selection. Pattern Anal. Mach. Intell. IEEE Trans 28(6), 983–995 (June 2006). doi:10.1109/TPAMI.2006.130

  23. Se, S., Brady, M.: Stereo vision-based obstacle detection for partially sighted people. In: Third Asian Conference on Computer Vision, pp. 152–159. Springer, London (1997)

  24. Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, Seattle (1994)

  25. Sinclair D., Blake A.: Quantitative planar region detection. Int. J. Comput. Vision 18(1), 77–91 (1996)

    Article  Google Scholar 

  26. TMS320C6000 Programmer’s Guide (Rev. I). Texas Instruments (2006). http://focus.ti.com/lit/ug/spru198i/spru198i.pdf

  27. TMS320C64x/C64x+ DSP CPU and Instruction Set Reference Guide (Rev. C). Texas Instruments (2006). http://focus.ti.com/lit/ug/spru732c/spru732c.pdf

  28. TMS320C64x Image/Video Processing Library Programmer’s Reference. Texas Instruments (2003). http://focus.ti.com/lit/ug/spru023b/spru023b.pdf

  29. Triggs, B.: Autocalibration from planar scenes. In: 5th European Conference on Computer Vision, pp. 89–105. Springer, London (1998)

  30. Trucco, E., Isgro, F., Bracchi, F.: Plane detection in disparity space. In: International Conference on Visual Information Engineering, pp. 73–76 (2003)

  31. Tyzx Inc. http://www.tyzx.com/

  32. Tzovaras D., Grammalidis N., Strintzis M.G.: Joint three-dimensional motion/disparity segmentation for object-based stereo image sequence coding. Opt. Eng. 35, 137–144 (1996)

    Article  Google Scholar 

  33. Valde systems. http://www.valdesystems.com/

  34. Videre Design. http://www.videredesign.com/

  35. Wang X., Wang H.: Markov random field modeled range image segmentation. Pattern Recognit. Lett. 25(3), 367–375 (2004)

    Article  MATH  Google Scholar 

  36. Zucchelli, M., Santos Victor, J., Christensen, H.: Multiple plane segmentation using optical flow. In: British Machine Vision Conference, pp. 313–322 (2002)

  37. Zuliani M., Kenney C., Manjunath B.: The multiransac algorithm and its application to detect planar homographies. IEEE Int. Conf. Image Process. 3, 153–156 (2005)

    Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, University of Texas at Arlington, Arlington, TX, 76010, USA

    Ninad Thakoor & Sungyong Jung

  2. Computer Science and Engineering Department, University of Texas at Arlington, Arlington, TX, 76010, USA

    Jean Gao

Authors
  1. Ninad Thakoor
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  2. Jean Gao
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  3. Sungyong Jung
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Correspondence to Ninad Thakoor.

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Thakoor, N., Gao, J. & Jung, S. Embedded planar surface segmentation system for stereo images. Machine Vision and Applications 21, 189–199 (2010). https://doi.org/10.1007/s00138-008-0147-x

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