1. A class of binary images thinning using two PCNNs
Neurocomputing
Volume 70, Issues 4–6, January 2007, Pages 1096–1101
Lifeng Shang, Zhang Yi
Reporter : 王柏升，陳俊穎

2. Outline Introduction Pulse coupled neuron model (PCNN)
Image thinning using two PCNNs
Simulation
Conclusions

3. Introduction
Image thinning plays an important part in image processing as it simplifies object representation, feature extraction and pattern analysis.
PCNN has been used to perform various important image processing tasks, such as edge detection , segmentation , feature extraction and pattern recognition.

4. Introduction
This paper proposes a new algorithm for a class of binary images thinning by using two PCNNs.
The proposed algorithm can be used to extract the skeletons of such images, which separate the original images into two individual regions, as circularity-like images and ribbon-like images.

5. Introduction The main procedure of the algorithm is listed below.
Getting input images: the Image-inner and the Image-outer.
Image-inner
Image-outer

6. Introduction
Getting thinning result: At each firing step, by using the pulses meeting criterion and the outputs of the two PCNNs, thinning result is obtained.
Getting final thinning result: When the stopping criterion is met, the final thinning result is obtained.

7. Pulse coupled neuron model (PCNN)

8. Image thinning using two PCNNs

9. Image thinning using two PCNNs
The threshold is a constant before the neuron firing and is raised to another constant after firing.
The intensity value TO is replaced by the constant 0.1, and the intensity value TB is replaced by the constant 1.

10. Image thinning using two PCNNs

11. Image thinning using two PCNNs
The threshold matrix is initialized by and adjusted by,

12. Image thinning using two PCNNs

13. Simulation (a)(e) original image (b)(f) Image-inner (c)(g) Image-outer
(d)(h) final thinning result

14. Simulation (a) the original image of a handwriting number ‘0’
(b) the skeleton extracted by the proposed algorithm
(c) Zhang and Suen algorithm
(d) GU algorithm

15. Conclusions
A fast parallel algorithm for a class of binary images thinning is proposed by using two PCNNs.
The pulses meeting criterion and the stopping criterion are given, and the determination of PCNN’s parameters is also given. Experimental results confirmed the proposed algorithm is efficient in thinning circularity-like and ribbon-like images.

16. Thanks for listening