Keywords: HSI Color Model, Color Segmentation.

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EE 5359 MULTIMEDIA PROCESSING

PROJECT PROPOSAL

Submitted
by,

Prasanna Venkatesh Palani
, UTA ID
: 1000660520

Email id: prasannaven.palani@mavs.uta.edu

PROJECT TOPIC:

Vehicle License Plate Detection Algorithm Based on Statistical Ch
aracteristics in HSI Color
Model
.

ABSTRACT:


Image capturing and p
rocessing has become an important part of intelligent robotics. It is
mainly used to detect an object

in real
-
time.

With constantly increasing traffic on roads, there is a
need for intelligent traffic management syst
em. License plate detection is widely used for detecting
speeding cars, security control, traffic law enforcement and electronic toll collection.

License plate
detection can be performed via various approaches such as vector quantization, Gabor transform,
optical character recognition, neural networks etc.

Character recognition in license plate is an
inevitable component of vehicle license plate recognition (VLPR) system.
Lot of research has been
carried out to detect the characters in the vehicle license p
late. In [1], the character recognition is
done by extracting the plate region of the license plate by using edge detection algorithms and
smearing algorithms. Then image segmentation is done by using smearing algorithms, filtering
techniques and some mor
phological algorithms are also used. Lastly, statistical approach is used to
identify the characters in the license plate using template matching. In [2]
,

pulse coupled neural
network is used fo
r license plate identification.

Marques et al

[3]

proposed an

algorithm based on
Max/Min post
-
processing and maximum correlation to locate the license plate for moving and
parked vehicle images.
This project

will focus

on implementing a
n algorithm in [4], which uses hue,
saturation, intensity (HSI
) color

model to d
etermine the threshold statistically for detecting the
candidate regions. Irrespective of the color of the license plate, the candidate regions shall includ
e
license plate

regions and the geometrical properties are used for classification of the license pl
ates.
Predetermined license plate alphanumeric character
s

shall be used to decompose the candidate
regions by using the position histogram an
d detect the license plate
.

The effectiveness of the
algorithm will be studied followed by conclusion
s
.

Keywords
: HSI

Color Model, Color Segmentation.




REFERENCES:

[1]
O.Serkan and

E.Ergun,
"Automatic Vehicle Iden
tification by Plate Recognition
"
,

Proceedings
of World Academy of Science
,Engineering and Technology,


, Vol
9, pp

222
-

225, November
2005.

[2]
M.M.I.Ch
acon and
S.A.Zimmerman , "License plate location

based on a dynamic PCNN
scheme
"
,

Proceedings of the International Joint Conference on

Neural Networks,

Vol

2
, pp
1195
-

1200, 20
-
24 July 2003
.

[3]
C.P.Marques et al,

“License Vehicle Plates Localization Us
ing Maximum Correlation”,
Structural, Syntactic, and Statistical Pattern Recognition Lecture Notes in Computer Science
,
Sp
ringer Berlin / Heidelberg, Vol

3138/2004,

pp

470
-
476, 2004.


[4
]
K. Deb,

S. Kang and

K. Jo. “Statistical Characteristics in HSI Color
Model and Position
Histogram Based
Vehicle License Plate Detection

,
Intelligent Service Robotics
, DOI:
10.10
07/s11370
-
009
-
0043
-
x, Vol 2 , pp

173
-
186, June 2009
.

[5]

M.Donosser , C.Arth and

H.Bischof , “Detecting, Tracking and Recogn
izing license plates. In
:
Y.Yagi, S.B.Kang, I.S.Kweon and H.
Zha

(eds.) ACCV2
--
7, Part II. LNCS,

Vol
4844, pp 447
-
456.
Springer, Heidelberrg, 2007
.

[6]
Y. Li and M. Wang,

“Novel and fast Algorithms of License Plate Locations and Extractions,”

IEEE International Conference on Infor
mation and Automation (ICIA),DOI :
10.1109/ICINFA.2010.5512273,pp 2462
-

2465
, 2010
.


[7
]
Y. Wang et al,

“Study on HSI Color Model
-
Based Fruit Quality Evaluation,”
International
Congress on Image and Signal Processing (CISP)
, DOI:10.1109/CISP.2010.564794
3,

Vol 6 , pp
2677


2680, 2010.

[8
]
R. C. Gonzalez, and R. E. Woods,
Digital Image Processing, Third Edition
. Prentice Hall, N.J.,
2008.