Transactions on Computer Science and Technology December 2015, Volume 4, Issue 4, PP.72-75
The Research of Thangka Buddha Gesture Segmentation Algorithm Yan Xu Mathematics and Computer Science College, Northwest University for Nationalities, Lanzhou 730030, China Email: xdcr_126@126.com
Abstract This paper describes the meaning of split Thangka Buddha gesture and steps, then the choice of the canny operator method of edge detection on the thangka Buddha's gesture. Through a simple way of human-computer interaction, the Buddha's gesture will be cut off from Thangka Buddha, and then make simulation experiments by using the software of Matlab. Finally, through analysis and comparison, this paper expounds the advantages and disadvantages of the algorithm. Keywords: Thangka; Gesture; Image Segmentation; Edge Detection Introduction
1 INTRODUCTION Thangka is the Tibetan art treasures which is a kind of unique painting art form in Tibetan culture. The Research of Thangka Buddha Gesture Detection Algorithm which havehas a profound effect on the whole understanding of thangka image segmentation region. Thangka image is different from the natural images. The color distribution, shape and texture characteristics of the natural images have certain regularity. However, because of thousands of years opreservation, the texture of Thangka images are more or less shaken, the edges characteristics and gray distribution are too difficult to find its own rules. So this paper through the human-computer interaction cuts out the Buddha gestures in a rectangular area first, then splits them from the shape of the gesture area.
2 ALGORITHM DESCRIPTION 2.1 Image Segmentation Image segmentation which divides the image into each has its own features and non-overlapping area. Assuming that collection represents the whole image area, the image segmentation issue is to determine the subset Rk which needs to meet the following conditions: K
(1)
I = Rk
(2)
Rk = Rj ϕ, k ≠ j
k =1
(3) In a certain standard, each subset of inner pixels must be similar, but the pixel to the differences between different subsets. (4) Each Rk is connected. Image segmentation often uses the target similarity and the difference between background and target can be achieved. Image segmentation needs to 1 find some similar characteristics, in this characteristic, the target interior is similar.
2.2 Edge Detection At the junction, two large differences between two grayscale are usually the edges of the image and the edges
National Natural Science Foundation of China. (No.60875006, No.61162021). The central university program. ( No. 31920150083) - 72 http://www.ivypub.org/cst
contain a wealth of information. Edge is detected, the image of the interested regions can be divided, so the algorithm is very important to detect edges. Edge detection methods can be divided based on the first derivative of the edge detection and second derivative-based edge detection. Based on the first derivative, the edge detection operators have gradient operator, the direction operator, Canny operator, etc. The second derivative-based edge detection operators have pull-operator, LOG operator, etc. This paper uses the Canny operator of Thangka image edge detection.
3 THE SIMULATION 3.1 Image Acquisition and Artificial Shear Thangka Buddha images are full of variety, morphological complexity, so searching for Buddha gestures in any images is very difficult. So this paper chooses the number of gestures which is obvious in the study of the images, and then uses the appropriate software to cut off Buddha gesture. The results are showed in Figure 1 and Figure2.
3.2 Canny Operator Method for Image Segmentation Mablab is a language which is based on matrix and array, and it is also a simpler grammar rules programing language. It is closed to the human way of thinking used to program. Matlab has now become the world's most widely used software development, so in this paper, the software is used as a simulation tool. Canny operator is the optimum edge detection operator [4]. Suppose that the function:
G ( x, y ) =
1 2πσ 2
exp(−
x2 + y2 ) 2σ 2
(1)
Assuming gradient vector is:
∇G = [
∂G / ∂X ] ∂G / ∂Y
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(2)
We can use the decomposition method to increase speed and let the ∇G two-dimensional filtering convolution template decomposed into two one-dimension of filter. x2
y2
− 2 − 2 ∂G ( x, y ) = kxe 2σ e 2σ = h1 ( x)h2 ( y ) ∂x
y2
(3)
x2
− 2 − 2 ∂G ( x, y ) = kye 2σ e 2σ = h1 ( y )h2 ( x) ∂y
(4)
In this formula,
h1 ( x) = k xe
−
h1 ( y ) = k ye
x2 2σ 2
−
y2 2σ
2
,
,
h2 ( y ) = k e
h2 ( x) = k e
−
−
y2 2σ 2
x2 2σ 2
(5)
(6)
Therefore,
h1 ( x) = xh2 ( x) , h1 ( y ) = yh2 ( y )
(7)
Finally, we use these two templates respective convolution with f ( x, y ) to obtain:
Ex =
∂G ( x, y ) ∂G ( x, y ) *f * f ; Ey = ∂y ∂x
(8)
And,
A(i, j ) = E x + E y , a (i, j ) = arctan 2
2
E y (i, j ) E x (i, j )
(9)
In this situation, A(i, j ) represents edge strength, a (i, j ) represents the vertical direction of the edge[4]. Using Canny operator cut off Thangka Buddha image, the result image is shown in Figure 3.
4 CONCLUSION Through Matlab simulation experiment, it shows that Canny operator for edge information of the image has a good ability to identify. Using the Canny operator cut off image, its notable feature is accurate in edge positioning, good continuity, less false edges and has single pixel width. That is two-dimensional case which has a good direction. However, in this experiment, not only can the Canny operator for Buddha gestures on the edge be detected very well, but also put other non-gestures regions of border to be detected too. Therefore, the algorithm requires further improvement. - 74 http://www.ivypub.org/cst
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Zhang Yu-jin. Image Segmentation [M]. Beijing: Science Press. 2001, 149-153
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Yu Song-Yu, Zhou Yuan-hua, Zhang Rui. Digital Image Processing. Shanghai: Shanghai Jiaotong University Press.2007, 281-299, 312-316
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Lan Zhang-li, Li Yi-cai, Li Ai-xing. Beijing: Tsinghua University Press. 2009: 157-165
[5]
Xu Lu-ping. Digital Image Processing. Beijing: Science Press.2007:203-205
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Zhao Wen-qi. Image segmentation technology in the beef automatic grading of research. Southwest University of Science and Technology, 2009
AUTHORS Yan Xu, Birthday: Jun 11, 1982. Educational background: Guizhou university master degree of computer application technology, Lanzhou, China
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