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Semantic Segmentation of Indoor-Scene RGB-D Images Based on Iterative Contraction and Merging

  • Jia-Hao SyuEmail author
  • Shih-Hsuan Cho
  • Sheng-Jyh Wang
  • Li-Chun Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

In this paper, we propose an iterative contraction and merging framework (ICM) for semantic segmentation in indoor scenes. Given an input image and a raw depth image, we first derive the dense prediction map from a convolutional neural network (CNN) and a normal vector map from the depth image. By combining the RGB-D image with these two maps, we can guide the ICM process to produce a more accurate hierarchical segmentation tree in a bottom-up manner. After that, based on the hierarchical segmentation tree, we design a decision process which uses the dense prediction map as a reference to make the final decision of semantic segmentation. Experimental results show that the proposed method can generate much more accurate object boundaries if compared to the state-of-the-art methods.

Keywords

Convolutional neural network Iterative contraction and merging RGB-D image Semantic segmentation 

References

  1. 1.
    Ren, X., Bo, L., Fox, D.: RGB-(D) scene labeling: features and algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2759–2766 (2012)Google Scholar
  2. 2.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012 Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33715-4_54CrossRefGoogle Scholar
  3. 3.
    Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: Learning rich features from RGB-D images for object detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014 Part VII. LNCS, vol. 8695, pp. 345–360. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10584-0_23CrossRefGoogle Scholar
  4. 4.
    Long, J., Shelhamer, E., Darrell, T: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  5. 5.
    Syu, J.H., Wang, S.J., Wang, L.C.: Hierarchical image segmentation based on iterative contraction and merging. IEEE Trans. Image Process. 26(5), 2246–2260 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jia-Hao Syu
    • 1
    Email author
  • Shih-Hsuan Cho
    • 2
  • Sheng-Jyh Wang
    • 2
  • Li-Chun Wang
    • 1
  1. 1.Department of Communications EngineeringNational Chiao Tung UniversityHsinchuTaiwan
  2. 2.Department of Electronics EngineeringNational Chiao Tung UniversityHsinchuTaiwan

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