Gesture image segmentation with Otsu’s method based on noise adaptive angle threshold

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By analyzing the essence and deficiency of the improved Otsu’s method, this paper proposes a noise adaptive angle threshold based Otsu’s method for gesture image segmentation. It first designs a two-dimensional histogram of gray value-neighborhood truncated gray mean to avoid the interference of extreme noise by discarding the extremes of the neighborhood. Then, the probability that the pixel is noise is calculated according to the actual situation, adaptive filtering is implemented to enhance the algorithm’s universal applicability. It finally converts the threshold space to an angle space from 0° to 90°, and the threshold search range is compressed to improve its efficiency. As the gesture is close to the background and the boundary is blurred, this paper combines the global and local Otsu’s method to segment the gesture images based on the angle space. On the one hand, it uses the global Otsu’s method to obtain the global threshold t1. On the other hand, it uses the local Otsu’s method to obtain the local threshold t2, and segments gesture images based on t2. Experimental results show that the proposed method is effective and can accurately segment gesture images with different noises.

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  1. 1.

    Anis BI (2017) Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework. Physica A 466:521–536

  2. 2.

    Ding S, Qu S, Xi Y, Wan S (2019) Stimulus-driven and concept-driven analysis for image caption generation. Neurocomputing

  3. 3.

    Eman T, Fatimah K, Puteri SS, Razali Y (2018) Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background. J Ambient Intell Humaniz Comput 9:755–769

  4. 4.

    Fan JL, Zhao F (2007) Two-dimensional Otsu’s curve thresholding segmentation method for gray-level images. Acta Electron Sin 35(4):751–755

  5. 5.

    Fan CD, Zhang YJ, Ouyang HL et al (2014) Improved Otsu’s method based on histogram oblique segmentation for segmentation of rotary kiln flame image. Acta Automat Sin 40(11):2480–2489

  6. 6.

    Ju ZJ, Ji XF, Liu HH (2017) An integrative framework of human hand gesture segmentation for human–robot interaction. IEEE Syst J 11(3):1326–1336

  7. 7.

    Li Q, Tang H, Chi JN et al (2017) Gesture segmentation with improved maximum between-cluster variance algorithm. Acta Automat Sin 43(4):528–537

  8. 8.

    Liu K, Gong DW (2017) Multi-objective optimization model and its evolution-based solution for gesture segmentation problems. Control Decis 32(1):100–104

  9. 9.

    Liu JZ, Li WQ (1993) The automatic thresholding of gray-level pictures via two-dimension Otsu method. Acta Automat Sin 19(1):101–105

  10. 10.

    Lv CS, Zhang T, Liu CY (2017) An improved Otsu’s thresholding algorithm on gesture segmentation. J Adv Comput Intell Intell Inf 21(2):247–250

  11. 11.

    Maryam V, Alireza B (2015) A vision based system for communicating in virtual reality environments by recognizing human hand gestures. Multimed Tools Appl 74(18):7515–7535

  12. 12.

    Nie FY, Wang YL, Pan MS et al (2013) Two-dimensional extension of variance-based thresholding for image segmentation. Multidim Syst Sign Process 24(3):485–501

  13. 13.

    Otsu’s N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

  14. 14.

    Sathya PD, Kayalvizhi R (2011) Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images. Measurement 44(10):1828–1848

  15. 15.

    Sebastien Marcel Dynamic Hand Posture Database (2018)

  16. 16.

    Sha CS, Hou J, Cui HX (2016) A robust 2D Otsu’s thresholding method in image segmentation. J Vis Commun Image Represent 41(11):339–351

  17. 17.

    Wang RY, Popovic J (2009) Real-time hand-tracking with a color glove. ACM Trans Graph 28(63):1–8

  18. 18.

    Wang M, Lin JS, Fu ZX, Meng GQ (2014) Gesture segmentation using an adaptive threshold algorithm. Int J Latest Res Sci Technol 3(4):65–71

  19. 19.

    Wu YQ, Pan Z, Wu WY (2008) Image thresholding based on two-dimensional histogram oblique segmentation and its fast recurring algorithm. J Commun 29(4):77–83

  20. 20.

    Wu D, Lionel P, Pieter-Jan K et al (2016) Deep dynamic neural networks for multimodal gesture segmentation and recognition. IEEE Trans Pattern Anal Mach Intell 38(8):1583–1597

  21. 21.

    Xi Y, Zhang Y, Ding S, Wan S (2019) Visual question answering model based on visual relationship detection. Signal Process Image Commun 80:115648

  22. 22.

    Zhang XM, Sun YJ, Zheng YB (2011) Precise two-dimension Otsu’s image segmentation and its fast recursive realization. Acta Electron Sin 39(8):1778–1784

  23. 23.

    Zhao Y, Li H, Wan S, Sekuboyina A, Hu X, Tetteh G, Piraud M, Menze B (2019) Knowledge-aided convolutional neural network for small organ segmentation. IEEE J Biomed Health Inform

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This work was supported by the National Natural Science Foundation of China (Grant No. 61573299, 51677063), the Natural Science Foundation of Hunan Province of China (Grant No. 2016JJ3125, 2017JJ3315), the Project of Xiangtan University (Grant No. 15XZX31, 16XZX30). The authors would like to thank the reviewers and associate editor for their comments that greatly helped to improve the manuscript.

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Correspondence to Shaohua Wan.

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Xiao, L., Ouyang, H., Fan, C. et al. Gesture image segmentation with Otsu’s method based on noise adaptive angle threshold. Multimed Tools Appl (2020).

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  • Gesture segmentation
  • Otsu’s method
  • Set mapping
  • Adaptive filtering
  • Angle threshold