Boundary and Region based Moments Analysis for Image Pattern Recognition
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1 ISSN , England, UK Journal of Inforation and Coputing Science Vol. 10, No. 1, 015, pp Boundary and Region based Moents Analysis for Iage Pattern Recognition Dr. U Ravi Babu 1, Dr Md Mastan and Dr Y Venkateswarlu 3 1 Professor, Departent of CSE. MREC, Secendrabad, TS, India Professor and HOD, Departent of CSE. MREC, Secendrabad, TS, India 3 Professor and HOD, Departent of CSE. GIET Engg College, RJY, AP, India (Received October 09, 014, accepted Deceber 8, 014 Abstract. In a nuber of pattern recognition application, oents have been used to recognize iage patterns. The recognition process involves effective shape representation ethod. So, the present paper analyzes the recognition rate in two different shape representation ethods called external and internal representation. With the proper shape representation of the given iage pattern, the present paper has coputed 7 boundary (external based and 10 region (internal based Hu oents. The experiental results on five different pattern iage groups (Brick, Circle, Curve, Line and Zigzag are precisely recognized by both boundary based and region based oents of order 1 and 10 respectively. Keywords: Skeleton, Hu Moent, recognition 1. Introduction Iage retrieval is becoing a ore iportant proble with the rapid increase of edia inforation. Users want to provide query iages and obtain a set of siilar iages. In content-based iage retrieval systes, several low-level iage features, such as color, texture, shape or the cobination of these features, describe iages. Shape is an iportant low-level iage feature. There are generally two types of shape descriptors: external representation based shape descriptors and internal representation based shape descriptors. External representation based shape descriptors use only the boundary of the objects shape, while the internal representation based shape descriptors use the internal region details in addition to the boundary [1. Moents due to its ability to represent global features have found extensive applications in the field of iage processing [ [10. In 1961, Hu [ introduced oent invariants. Based on the theory of algebraic invariants he derived a set of oent invariants, which are position, size and orientation independent. Dudani et al. [4 used Hu s oent invariants up to the third order in the recognition of iages of aircraft. The sae invariants were also used for recognition of ships [5. Markandey et al. [10 developed techniques for robot sensing based on high diensional oent invariants and tensors. Gang Xu et al. [11 has proposed a new iage recognition algorith by using region based shape representation. They proposed new region based oents based on skeletons. Zhihu Huang et al. [1 has perfored an analysis of boundary based Hu s Moent invariants on iage scaling and rotation. Hongbo Mu [13 used boundary and region based Hu oents for recognizing different types of defects in wood pattern iages. Cecila Di Ruberto et al. [14 has cobined orphological iage features with the oent invariants for classification. The organization of the paper is as follows. Section deals with the ethodology of boundary and region based oent coputation, the results and discussions are presented in section 3 and last section deals with conclusions.. Methodology Shape representation is an iportant issue in iage processing and coputer vision, because it provides the foundation for developing algoriths for shape-related processing such as iage coding, shape atching and object/pattern recognition, content-based video processing and iage data retrieval. Published by World Acadeic Press, World Acadeic Union
2 Journal of Inforation and Coputing Science, Vol. 10(015 No. 1, pp The present paper uses two types of shape representation naely external representation and internal representation. In external representation, the shape of the given object is represented by the boundary while in the internal representation, the entire region is represented by skeleton. Fro the second- and third-order noralized central oents, a set of seven invariant oents, which are invariant to translation, scale change and rotation, has been derived by Hu as given in Equations (1-(7. The present paper has coputed the 7 Hu oents on the boundary of the given iage pattern (1 5 3( ( ( 11 3 ( 3 1 (3 1 ( 4 ( 1 ( 1 3 ( 1 1 [( 1 [3( 1 1 ( 3( 1 1 ( 0 0 [( 11 ( ( 11 ( (3 (3 1 1 ( ( 1 [( 1 [3( 1 1 ( 3( 1 Where the noralized central oent of order (p+q is given in the Equation (8 pq pq pq 00 The central oent of order (p+q is given by the Equations (9-(11. μ pq x,yc x y ( x x p x,yc x,yc x,yc x,yc ( y y xf q ( x,ydxdy f ( x,ydxdy ( x,ydxdy f ( x,ydxdy f ( x,y dxdy The 10 extended Hu oents given in the Equations (1-(1 are coputed on the skeleton of the given iage. yf 1 ( (3 (4 (5 (6 (7 (8 (9 (10 (11 RM1 1 (1 JIC eail for subscription: publishing@wau.org.uk
3 4 U Ravi Babu et.al. : Boundary and Region based Moents Analysis for Iage Pattern Recognition RM 1 1 RM RM RM RM RM RM RM RM (13 (14 (15 (16 (17 (18 (19 (0 (1 3. Results and Discussions The experients are conducted with 5 different iage pattern groups naely Brick, Circle, Curve, Line and Zigzag patterns, 10 iages per group of each size 56 56, collected as shown in Figure (1-(5. In the first ethod, the 7 boundary based Hu oents are calculated and with in each group, the average of all 10 iages is coputed and are represented in Table 1. In the second ethod, the 10 region based extended Hu oents are calculates and within each group, the average of all 10 iages is coputed and are represented in Table. (a (b (c (d (e (f (g (h (i (j Figure 1. Input Iages of Brick Pattern Iages (a Brick1 (b Brick (c Brick3 (d Brick4 (e Brick5 (f Brick6 (g Brick7 (h Brick8 (i Brick9 (j Brick10. (a (b (c (d (e (f (g (h (i (j Figure. Input Iages of Circle Pattern Iages (a Circle1 (b Circle (c Circle3 (d Circle4 (e Circle5 (f Circle6 (g Circle7 (h Circle8 (i Circle9 (j Circle10. JIC eail for contribution: editor@jic.org.uk
4 Journal of Inforation and Coputing Science, Vol. 10(015 No. 1, pp (a (b (c (d (e (f (g (h (i (j Figure 3. Input Iages of Curve Pattern Iages (a Curve1 (b Curve (c Curve3 (d Curve4 (e Curve5 (f Curve6 (g Curve7 (h Curve8 (i Curve9 (j Curve10. (a (b (c (d (e (f (g (h (i (j Figure 4. Input Iages of Line Pattern Iages (a Line1 (b Line (c Line3 (d Line4 (e Line5 (f Line6 (g Line7 (h Line8 (i Line9 (j Line10. (a (b (c (d (e (f (g (h (i (j Figure 5. Input Iages of Zigzag Pattern Iages (a Zigzag1 (b Zigzag (c Zigzag3 (d Zigzag4 (e Zigzag5 (f Zigzag6 (g Zigzag7 (h Zigzag8 (i Zigzag9 (j Zigzag10. Table 1. Average Boundary based Hu oent values of Iage Pattern Groups. Iage Nae Brick Circle Curve Line Zigzag Table. Average Region based Extended Hu oent values of Iage Pattern Groups. Iage Nae RM1 RM RM3 RM4 RM5 RM6 RM7 RM8 RM9 RM10 Brick Circle Curve Line Zigzag Figure 6. Recognition graph for Iage Pattern Groups by Boundary based Moents. JIC eail for subscription: publishing@wau.org.uk
5 44 U Ravi Babu et.al. : Boundary and Region based Moents Analysis for Iage Pattern Recognition Figure 7. Recognition graph for Iage Pattern Groups by Region based Moents. The graphs of Figure 6 and 7 show the recognition rate of all five different iage pattern groups. All five groups are precisely recognized by using boundary based oents of order 1 and region based oents of order 10. The Table 3 shows the average coputation tie (in second used by the boundary based and region based oents per each group of iage pattern. Table 3. Coputation Tie (in seconds used by Boundary based and Region based Moents. Input Iage Pattern Group Boundary based Moent Region based Moent Brick Circle Curve Line Zigzag Conclusions All the five types of iages are clearly classified by using boundary and region based Moents. The iages are precisely classified by using Boundary Moent of order 1 and by using Region Moent of order 10. In boundary and region oents, Line shape iages are having axiu values. Though the iages are precisely classified by boundary and region oents, the region oents are efficient because the average coputation tie is less copared to boundary oents. 5. References [1 Irina Mocanu, Iage Retrieval by Shape Based on Contour Techniques - A Coparative Study, IEEE Conference, pp. 19-3, 007. [ M. K. Hu, Visual pattern recognition by oent invariants, IRE Trans. Infor. Theory, vol. IT-8, pp , Feb [3 M. R. Teague, Iage analysis via the general theory of oents, J. Opt. Soc. Aer., vol. 70, pp. 90 9, Aug [4 S. Dudani, K. Breeding, and R. McGhee, Aircraft identification by oent invariants, IEEE Trans. Coput., vol. 6, pp , Feb [5 D. Casasent and R. Cheatha, Iage segentation and real iage tests for an optical oent-based feature extractor, Opt. Coun., vol. 51, pp. 7, Sept [6 A. Khotanzad and J. J. H. Liou, Recognition and pose estiation of unoccluded three-diensional objects fro a two-diensional perspective view by banks of neural networks, IEEE Trans. Neural Networks, vol. 7, pp , Sept [7 S. O. Belkasi, M. Shridhar, and M. Ahadi, Pattern recognition with oent invariants: A coparative study and new results, Pattern Recognit., vol. 4, no. 1, pp , [8 Shape recognition using Zernike oent invariants, in Proc. 3rd Annu. Asiloar Conf. Signals Systes and Coputers, Oct. Nov. 1989, pp JIC eail for contribution: editor@jic.org.uk
6 Journal of Inforation and Coputing Science, Vol. 10(015 No. 1, pp [9 S. Ghosal and R. Mehrotra, Orthogonal oent operators for subpixel edge detection, in Proc. 3rd Annu. Asiloar Conf. Signals Systes and Coputers, vol. 6, 1993, pp [10 V. Markandey and R. J. P. Figueiredo, Robot sensing techniques based on high diensional oent invariants and tensors, IEEE Trans. Robot Autoat., vol. 8, pp , Feb [11 Gang Xu, Yuqing Lei, A New Iage Recognition Algorith based on Skeleton, IEEE 008, pp [1 Zhihu Huang, Jinsong Leng, Analysis of Hu s Moent Invariants on Iage Scaling and Rotation, IEEE nd International Conference on Coputer Engineering and Technology, Vol. 7, pp , 010. [13 Hongbo Mu, Dawei Qi, Pattern Recognition of Wood Defects Types based on Hu Invariant Moents, IEEE 009. [14 Cecila Di Ruberto et al., Moent based Techniques for Iage Retrieval, IEEE 19 th International Conference on Database and Expert Systes Application, pp , 008. JIC eail for subscription: publishing@wau.org.uk
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