© The Institution of Engineering and Technology
A two-step process for removing noise from polygonal shapes is presented in this study. The authors present a polygonal shape as its turning function and then apply a non-linear diffusion filter and a triangle method on it. In the first step the authors apply several different non-linear diffusion filters on the turning function and compare the performance of these filters later. Non-linear diffusion filters identify dominant vertices in a polygon and remove those vertices that are identified as noise or irrelevant features. The vertices in the turning function which diffuse until the sides that immediately surround them approach the same turning function are identified as noise and removed. The vertices that are enhanced are preserved without changing their coordinates and they are identified as dominant ones. After the authors carry this process as far as it will go without introducing noticeable shape distortion, and switch to the triangle method for further removal of vertices that are to be treated as noise. In the second step the authors remove the vertices that form the smallest area triangles. The authors submit experimental results of the tests that demonstrate that this two-step process successfully removes vertices that should be dismissed as noise while preserving dominant vertices that can be accepted as relevant features and give a faithful description of the shape of the polygon. In experimental tests of this procedure the authors demonstrate successful removal of noise and excellent preservation of shape, thanks to appropriate emphasis of dominant vertices.
References
-
-
1)
-
Hong, J., Wolfson, H.J.: `An improved model-based matching method using footprints', Proc. Ninth Int. Conf. Pattern Recognition, 14–18 November 1988.
-
2)
-
Schwartz, J.T., Sharir, M.: `Some remarks on robot vision', April 1984, Technical Report 119, Robotics Report. 25, New York Univ., Courant Inst. Math. Sci..
-
3)
-
A. Witkin
.
(1983)
Scale-space filtering, Int. Joint Conf. Artificial Intelligence.
-
4)
-
J. Koenderink
.
The structure of images.
Biol. Cyben.
,
5 ,
363 -
370
-
5)
-
D.H. Ballard ,
C.M. Brown
.
(1982)
Computer vision.
-
6)
-
Tameze, C., Vincelette, R., Melikechi, N., Zeljković, V., Izquierdo, E.: `Empirical analysis of LIBS images for ovarian cancer detection', WIAMIS 2007.
-
7)
-
J. O'Rourke ,
R. Washington ,
G. Toussaint
.
(1985)
Curve similarity via signatures, Computational geometry.
-
8)
-
H. Krim ,
A. Yezzi
.
(2006)
Statistics and analysis of shapes (modeling and simulation in science, engineering and technology).
-
9)
-
Hummel, A.: `Representations based on zero-crossings in scalespace', Proc. IEEE Computer Vision and Pattern Recognition Conf., June 1986, p. 204–209.
-
10)
-
R. Kimmel
.
(2004)
Numerical geometry of images: theory, algorithms, and applications.
-
11)
-
F. Cao ,
J.-L. Lisani ,
J.-M. Morel ,
P. Musé ,
F. Sur
.
(2006)
A theory of shape identification.
-
12)
-
A. Hummel ,
L. Uhr
.
(1987)
The scale-space formulation of pyramid data structures, Parallel computer vision.
-
13)
-
J. Babaud ,
A. Witkin ,
M. Baudin ,
R. Duda
.
Uniqueness of the Gaussian kernel for scale-space filtering.
IEEE Trans. Pattern Anal. Mach. Intell.
,
1 ,
26 -
33
-
14)
-
L.J. Latecki ,
R. Lakamper
.
Convexity rule for shape decomposition based on discrete contour evolution.
Comput. Vis. Image Underst.
,
3 ,
441 -
454
-
15)
-
E.M. Arkin ,
L.P. Chew ,
D.P. Huttenlocher ,
K. Kedem ,
J.S.B. Mitchell
.
An efficiently computable metric for comparing polygonal shapes.
IEEE Trans. Pattern Anal. Mach. Intell.
,
3 ,
209 -
216
-
16)
-
A. Yuille ,
T. Poggio
.
Scaling theorems for zero crossings.
IEEE Trans. Pattern Anal. Mach. Intell.
,
1 ,
15 -
26
-
17)
-
J. Munkres
.
(2000)
Topology.
-
18)
-
Wolfson, H.: `On curve matching', Proc. IEEE Workshop Computer Vision, 30 November–2 December 1987.
-
19)
-
L. de Floriani ,
M. Spagnuolo
.
(2007)
Shape analysis and structuring.
-
20)
-
P. Perona ,
J. Malik
.
Scale-space and edge detection using anistropic diffusion.
IEEE Trans. Pattern Anal. Mach. Intell.
,
7 ,
629 -
639
-
21)
-
R.B. Vincelette ,
C. Tameze ,
M. Savic ,
V. Zeljkovic
.
(2007)
Efficient shape recognition method using novel metric for complex polygonal shapes.
-
22)
-
A. Rosenfeld ,
M. Thurston
.
Edge and curve detection for visual scene analysis.
IEEE Trans. Comput.
,
562 -
569
-
23)
-
Hong, J., Tan, X.: `The similarity between shapes under affine transformation', Proc. Second Int. Conf. Computer Vision, 1988, p. 489–493.
-
24)
-
M. Barnsley
.
(1988)
Fractals everywhere.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2008.0233
Related content
content/journals/10.1049/iet-ipr.2008.0233
pub_keyword,iet_inspecKeyword,pub_concept
6
6