© The Institution of Engineering and Technology
Past research work has shown that the process of shape matching can be rendered into an optimisation problem that determines, based on evolutionary algorithms, the best matching score between pairs of object boundaries. This important finding has enabled near planar objects to be identified efficiently when they are captured under different camera viewpoints. Among other evolutionary techniques, the genetic algorithm (GA) has demonstrated its feasibility in matching silhouette images of objects that are captured under a well-controlled environment. As the latter is not guaranteed in practice, the method has also been extended to match fragmented and incomplete contours. Despite the moderate success achieved, the overall performance is rather inconsistent and also varies significantly among different geometries. To overcome this problem, two variants of a novel approach based on the integration of a simple GA, the distance transform and the migrant principle are developed and presented. Experimental results reveal that the proposed methods are capable of matching incomplete and broken contours with a high success rate and exhibit good stability in performance.
References
-
-
1)
-
H.S. Lim ,
S.H. Cheraghi
.
An optimization approach to shape matching and recognition.
Comput. Electr. Eng.
,
183 -
200
-
2)
-
K.G. Khoo ,
P.N. Suganthan
.
Evaluation of genetic operators and solution representations for shape recognition by genetic algorithms.
Pattern Recognit. Lett.
,
13 ,
1589 -
1597
-
3)
-
G. Roth ,
M.D. Levine
.
Geometric primitive extraction using a genetic algorithm.
IEEE Trans. Pattern Anal. Mach. Intell.
,
9 ,
901 -
905
-
4)
-
Kunttu, I., Lepisto, L., Rauhamaa, J., Visa, A.: `Multiscale Fourier descriptor for shape-based image retrieval', Proc. 17th Int. Conf. Patt. Recog., 2004, 2, p. 765–768.
-
5)
-
A. Toet ,
W.P. Hajema
.
Genetic contour matching.
Pattern Recognit. Lett.
,
849 -
856
-
6)
-
E. Ozcan ,
C.K. Mohan
.
Partial shape matching using genetic algorithm.
Pattern Recognit. Lett.
,
987 -
992
-
7)
-
Simunic, K.S., Loncaric, S.: `A genetic search-based partial image matching', Proc. ICIPS'98, IEEE Internat. Conf. Int. Process. Syst., 1998, p. 119–122.
-
8)
-
P.W.M. Tsang
.
A genetic algorithm for aligning object shapes.
Image Vis. Comput.
,
819 -
831
-
9)
-
Tsang, P.W.M., Tsang, W.H.: `A floating point genetic algorithm for affine invariant matching of object shapes', Mech. and Mach. Vis. 2002: Current Practice, 2002, p. 41–53.
-
10)
-
P.N. Suganthan
.
Structural pattern recognition using genetic algorithms.
Pattern Recognit.
,
9 ,
1883 -
1893
-
11)
-
P.W.M. Tsang
.
A genetic algorithm for invariant recognition of object shapes from broken boundaries.
Pattern Recognit. Lett.
,
7 ,
631 -
639
-
12)
-
S.Y. Yuen
.
Guaranteeing the probability of Success using repeated runs of genetic algorithm.
Image Vis. Comput.
,
551 -
560
-
13)
-
Lee, D.J.: `Contour matching for a fish recognition and migration-monitoring system', Proc. SPIE, 2004, 5606, p. 37–48.
-
14)
-
J.H. Holland
.
(1975)
Adaptation in natural and artifical systems.
-
15)
-
Adamek, T., O'Conner, N.E.: `Efficient contour-based shape representation and matching', Proc. 5th ACM SIGMM Int. Work. Mult. Info. Ret'l, 2003, p. 138–143.
-
16)
-
T.M. Centeno ,
H.S. Lopes ,
M.K. Felisberto ,
L.V.R. Arruda
.
(2005)
Object detection for computer vision using a robust genetic algorithm.
-
17)
-
Dunn, M., Billingsley, J., Finch, N.: `Machine vision classification of animals', Proc. 10th IEEE Int. Conf. M2VIP, 2003, p. 9–11.
-
18)
-
Tsang, P.W.M.: `A novel approach to reduce the effects of initial population on simple genetic algorithm', PDPTA 2001, 2001, p. 457–462.
-
19)
-
G. Borgefors
.
Hierarchical chamfer matching: a parametric edge matching algorithm.
IEEE Trans. Pattern Anal. Mach. Intell.
,
6 ,
849 -
865
-
20)
-
Rube, I.A.E., Ahmed, M., Kamel, M.: `Coarse to fine affine invariant shape matching and classification', Proc. 17th ICPR'04, 2004.
-
21)
-
S. Lee ,
M.C. Lee ,
D. Adjeroh
.
Effective invariant features for shape-based image retrieval.
J. Am. Soc. Inf. Sci. Technol.
,
7 ,
729 -
740
-
22)
-
Bosco, G.L.: `A genetic algorithm for image segmentation', Proc. 11th Int. Conf. Img. Ana. Process., 2001, p. 262–266.
-
23)
-
D.E. Goldberg
.
(1989)
Genetic algorithms in search, optimization, and machine learning.
-
24)
-
Wang, Y.K., Fan, K.C.: `Applying genetic algorithms on pattern recognition: an analysis and survey', Proc. of the 13th Int. Conf. on Patt. Rec., 1996, 2, p. 740–744.
-
25)
-
C.L. Lee ,
S.Y. Chen
.
Classification for leaf images.
16th IPPR Conf. CVGIP
,
355 -
362
-
26)
-
Yan, X.-Q., Li, W.-F., Chen, D.-F.: `Image recognition based on evolutionary algorithm', Proc. Int. Conf. Mach. Learn. Cyber., 4–5 November 2002, 4, p. 1771–1773.
-
27)
-
P.W.M. Tsang
.
Genetic algorithm for affine invariant object shape recognition.
Proc. Inst. Mech. Eng.
,
385 -
292
-
28)
-
Keogh, E., Wei, L., Xi, X.P., Lee, S.H., Vlachos, M.: `LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures', Proc. 32nd Int. Conf. VLDB, 2006, 32, p. 882–893.
-
29)
-
L. Zhang ,
W. Xu ,
C. Chang
.
Genetic algorithm for affine point pattern matching.
Pattern Recognit. Lett.
,
9 -
19
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi_20070036
Related content
content/journals/10.1049/iet-cvi_20070036
pub_keyword,iet_inspecKeyword,pub_concept
6
6