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A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation

A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation. Source: IEEE Transactions on Image Processing, vol. 17, No. 2, February 2008 Author: Jundi Ding, Runing Ma, and Songcan Chen Impact Factor: 2.715 Speaker: Chun-Chieh Chen ( 陳俊杰 ) Date: 2008/3/18. Outline.

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A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation

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  1. A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation Source: IEEE Transactions on Image Processing, vol. 17, No. 2, February 2008 Author: Jundi Ding, Runing Ma, and Songcan Chen Impact Factor: 2.715 Speaker: Chun-Chieh Chen (陳俊杰) Date: 2008/3/18

  2. Outline • Introduction • Connected Coherence Tree Algorithm (CCTA) • Experimental Results • Conclusions

  3. Introduction

  4. Connected Coherence Tree Algorithm (CCTA) (1/8) Block size : (2k+1) × (2k+1) , k=1 Threshold :

  5. Connected Coherence Tree Algorithm (CCTA) (2/8) Block size : (2k+1) × (2k+1) , k=7 Threshold :

  6. Connected Coherence Tree Algorithm (CCTA) (3/8) 97 Block size : (2k+1) × (2k+1) , k=1 Threshold :

  7. Connected Coherence Tree Algorithm (CCTA) (4/8) Block size : (2k+1) × (2k+1) , k=1 Threshold :

  8. Connected Coherence Tree Algorithm (CCTA) (5/8)

  9. Connected Coherence Tree Algorithm (CCTA) (6/8)

  10. Connected Coherence Tree Algorithm (CCTA) (7/8) k=1

  11. Connected Coherence Tree Algorithm (CCTA) (8/8) Ave(6) = 29.022 Ave(7) = 31.65 Ave(18) =50.343 Ave(12) = 41.51

  12. Experimental Results(1/8) • Experiments on Synthetic Images

  13. Experimental Results(2/8) • Experiments on Synthetic Images Gr CCT1 CCT2 Ncut KMST

  14. Experimental Results(3/8) • Experiments on Synthetic Images CCTA Ncut KMST

  15. Experimental Results(4/8) • Experiments on Natural Image

  16. Experimental Results(5/8) • Evaluation of experimental comparison • Entropy-based evaluation function E

  17. Experimental Results(6/8) 21 gray images from the Berkeley segmentation datasets

  18. Experimental Results(7/8) • Evaluation of experimental comparison • Global Consistency Error (GCE) and Local Consistency Error (LCE)

  19. Experimental Results(8/8) LCE=0.0927 GCE=0.1384 LCE=0.1110 GCE=0.1641 LCE=0.0867 GCE=0.1327

  20. Conclusions • Our contribution lies in proposing a scale-based CCTA for image segmentation, which satisfies a so-called 3-E property: • Easyto implement, • Effectivefor semantic segmentation • Efficientin computational cost.

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