SlideShare a Scribd company logo
1 of 10
Download to read offline
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
DOI : 10.5121/ijcseit.2012.2216 187
DETECTION OF CONCEALED WEAPONS IN X-RAY
IMAGES USING FUZZY K-NN
Dr. Mohamed Mansoor Roomi #1
R.Rajashankari#2
Department of Electronics and Communication Engineering, Thiagarajar College of
Engineering,Madurai
1
smmroomi@tce.edu
2
r.rajasankari@gmail.com
ABSTRACT
Scanning baggage by x-ray and analysing such images have become important technique for detecting
illicit materials in the baggage at Airports. In order to provide adequate security, a reliable and fast
screening technique is needed for baggage examination.This paper aims at providing an automatic method
for detecting concealed weapons, typically a gun in the baggage by employing image segmentation method
to extract the objects of interest from the image followed by applying feature extraction methods namely
Shape context descriptor and Zernike moments. Finally the objects are classified using fuzzy KNN as illicit
or non-illicit object.
KEYWORDS
Aviation security, Shape Context Descriptor, Zernike Moments, Nearest Neighbour Classifier
1.INTRODUCTION
X-ray imaging is an important technology in many fields from inspection of delicate objects to
weapon detection at security checkpoints [1].To achieve higher threat detection rates during
inspection of X-ray luggage scans is a pressing and sought after goal for airport security
personnel. The Baggage inspection system used in airport ensures security of the passengers. The
process of identifying the contents of each bag and the methods adopted by terrorists for hiding
the threat objects are complicated, the existing luggage inspection system do not reveal 100% of
threat items. Further an object inside a bag may be in any position, it may be rotated so an
algorithm whist is rotational, translational invariant should be used for providing accurate results.
In addition, the threat item is superimposed by other objects in the bag, the harder it becomes to
detect it (effect of superposition). The passenger’s baggage may contain threat items such as
handgun, bomb, grenade,etc which must be detected efficiently so the human operators must be
assisted by an weapon detection system. Advanced security screening systems are becoming
increasingly used to aid airport screeners in detecting potential threat items [2]. Unfortunately,
most airport screening is still based on the manual detection of potential threat objects by human
experts. In response to this, security training is relying heavily on the object recognition test
(ORT) as a means of qualifying human airport luggage screeners [4].In order to provide
appropriate security, a much more sophisticated, reliable, and fast screening technique is needed
for passenger identification and baggage examination. Automatic threat detection is an important
application in x-ray scene analysis. Understanding x-ray images is a challenging task in computer
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
188
vision and an automatic system should be developed that consumes less time for processing and
performs accurately with reduced false positive results.
Although several X-ray technology based automatic systems exist for threats detection, only a
few of these systems make use of the well established pattern recognition and machine learning
techniques.On the other hand, several approaches based on Classifier have
been proposed to detect weapons[3].Additionally, the importance of image enhancement and
pseudo-coloring[5] to help aid decision making by human is now a recognized area of critical
need. Also, the system should provide automatic detection of potential threat objects.
2. RELATED WORK
For the detection of threat items, many types of imaging system exist. X-ray imaging systems and
MMW (Millimetre wave imaging) are used and x-ray imaging system is widely used for carry-on
bags. The techniques used for analysing these x-ray images are pseudo-coloring and segmentation
based techniques. Pseudo-coloring [1] process is the one in which the objects inside the bag are
given different colors based on their material type. In segmentation based methods, the x-ray
images are segmented to extract the objects of interest. Using these methods, satisfactory results
are produced and assisted human for detecting the threat items. X-ray photons, however,
penetrate most materials. As a result, all objects along an x-ray path attenuate the x-ray and
contribute to the final measured intensity. In the x-ray community, a common way of
disambiguating objects is through CT reconstruction [7]. This is typically obtained through the
filtered back-projection algorithm. Although several X-ray technology based automatic systems
exist for threats detection [8], only a few of these systems make use of the well established
pattern recognition and machine learning techniques [9, 10, 11, and 12]. New X-ray imaging
systems at airports use dual-energy analysis to estimate the atomic numbers of materials in the
passenger baggage. This method obtains a measure of the density and thickness of the material.
3. PROPOSED WORK.
The image is converted to binary image by choosing the threshold as the mean of the two peaks
of bimodal histogram and the objects are labelled. The area of each object is computed and the
values are sorted. The mean value is calculated and is set as threshold to collect the objects of
interest. Object boundary is extracted and shape feature extraction algorithm is implemented. The
classifier is trained with the extracted features and the object is classified as object or non-object.
Figure 1 SampleX-ray Images
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
189
Figure 1 shows some of the x-ray images considered in this work.Figure 2 shows the overall
frame work for the approach.
Image
Figure 1 Flow chart of proposed work
3.1 FEATURE EXTRACTION
3.1.1 SHAPE CONTEXT DESCRIPTOR
Shape is not the only, but a very powerful descriptor of image content .Shape is almost certainly
the most important property that is perceived about objects. Shape provides more information
about the object than other features and can be used in object recognition. Using shape as a
attribute provide more accurate and reliable results .Addressing objects based on their shape is
unique.Shape is an important cue as it captures a prominent element of an object.. Ideally, a good
shape descriptor has the following desirable properties1)Discrimination should be
high;2)Efficient matching;3)Compact representation;4)Efficient Feature Extraction;5)Invariance
to shape representation;6)Invariance to similarity transformation;7)Invariance to shape
degeneracies and noises.
Shape context is a shape descriptor proposed by Serge Belongie and Jitendra Malik[13]. The
shape context is anticipated to be a way of describing shapes that allows for measuring
shape similarity and the recovering of point correspondences.It characterize a particular
Removal of Spurious
Component
Preliminary Feature
Extraction
Binarization
Extract Object Boundary
Co-ordinates
Shape Context
Descriptor
Zernike Moments Max, Min, Mean Gray
value
Fuzzy k-NN Classifier
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
190
point location on the shape.The fundamental idea is to pick n points on the contours of a
shape. For each point pi on the shape, consider the n − 1 vectors obtained by connecting
pi to all other points.The set of all these vectors is a rich description of the shape localized
at that point but is far too detailed. The key initiative is that the distribution over relative
positions is a robust, compressed, and very discriminative descriptor. So, for the point pi,
the coarse histogram of the relative coordinates of the remaining n − 1 points, Concretely,
for a point pi on the shape, compute a coarse histogram hi of the relative coordinates of the
remaining n-1 points
)}()(:{# kbinpqpqh ii
k
i ∈−≠= (1)
is define to be the shape context of pi . The bins are normally taken to be uniform in log-
polar space. In the absence of background clutter, the shape context of a point on a shape can be
made invariant under uniform scaling of the shape as a whole. This is accomplished by
normalizing all radial distances by the mean distance α between the n2
point pairs in the shape.
Consider the shape of the alphabet fig.3 (a).sampled edge points of the shape. A log polar
histogram bin as shown in fig.3 (b) is overlaid on any sampled boundary point. Belongie et al.
have used 12 bins for log r and 5 bins for angle θ. As illustrated in Fig. 1, shape contexts is
computed for each point in the shape and will be unique for each point and similar shapes will
have similar shape context.Translational invariance come naturally to shape context. Scale
invariance is obtained by normalizing all radial distances by the mean distance between
all the point pairs in the shape.
Figure 2 Shape Context Computation
3.1.2 ZERNIKE MOMENTS
Moments have been widely used in image processing applications through the years. For both
contour and region of a shape, one can use moment's theory to analyse the object. Geometrical,
central and normalized moments were for many decades the only family of applied moments. The
main disadvantage of these descriptors was their disability to fully describe an object in a way
that, using the moments set, the reconstruction of the object could be possible. In other words
they are not orthogonal. Zernike comes to fill this gap, by introducing a set of complex
polynomials, which form a complete orthogonal set over the interior of the unit circle,
x2
+ y2
= 1.
These polynomials have the form
)exp()(),(),( θρθρ jmyx RVV nmnmnm
== (2)
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
191
Where n is a non-negative integer m is a non zero integer subject to the constraints n-|m| even and
|m|≤ n, ρ is the length of vector from the origin ( )yx, to the pixel(x, y),θ the angle between vector
ρ and x axis in counter-clockwise direction. Rnm(ρ ) is the Zernike radial polynomials in (ρ,θ)
polar coordinates
)()(, ρρ nmmn RR =− (3)
The Zernike moment of order n with repetition m for a continuous image function f(x,y), that
vanishes outside the unit disk is
∫∫
≤+
+
=
1
*
22
),(),(
1
),(
yx
nmnm
dxdyVyxf
n
yxZ θρ
π
(4)
For a digital image, the integrals are replaced by summations to get
∑∑ ≤+
+
=
x
nm
y
nm
yxyxf
n
VZ 1),,(),(
1 22*
θρ
π
(5)
Suppose that one knows all moments Znm of f(x,y) up to a given order nmax. It is desired to
reconstruct a discrete function ),(ˆ yxf whose moments exactly match those of f(x,y) up to the
given order nmax. Zernike moments are the coefficients of the image expansion into orthogonal
Zernike polynomials. By orthogonality of the Zernike basis
),(),(ˆ
max
0
θρnm
n
n m
nm
Vyxf Z∑∑=
= (6)
Since Zernike moments are only rotationally invariant, additional properties of translation and
scale invariance should be given to these moments in some way. We can introduce translation
invariance in the Zernike moments by converting the absolute pixel coordinates as follows






−
−
→





0
0
Yy
Xx
y
x
(7)
Where
00
10
0
m
m
X =
00
01
0
m
m
Y =
are the centroid coordinates of the object (with m denoting the geometrical moment).
Scaling invariance can be achieved by normalizing the Zernike moments with respect to the
geometrical moment m00 of the object. The resulting moments are derived from the following
equation
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
192
00
'
m
Z
Z nm
nm = (8)
where Znm are the Zernike moments computed by using equation (4.4).Since Zernike basis
functions take the unit disk as their domain, this disk must be specified before moments can be
calculated.
In the implementation, all the shapes are normalized into a unit circle of fixed radius. The unit
disk is then centred on the shape centroid. This makes the obtained moments scale and translation
invariant. Rotation invariance is achieved by only using magnitudes of the moments. Rotation
invariance is achieved by only using magnitudes of the moments. The magnitudes are then
normalized by dividing them by the mass of the shape.
The similarity between two shapes indexed with Zernike moments descriptors is measured by the
Euclidean distance between the two Zernike moments vectors. The computation of ZMD does not
need to know boundary information, making it suitable for more complex shape representation.
Like Fourier descriptors, Zernike moments descriptors can be constructed to arbitrary order, this
overcomes the drawback of geometric moments in which higher order moments are difficult to
construct. However, Zernike moments descriptors lose the perceptual meanings as those reflected
in Fourier descriptors and geometric moments. Besides, ZMD does not emphasize shape
boundary features which are important features of a shape. Zernike moments have many
advantages such as rotation invariance(the magnitudes of Zernike moments are invariant to
rotation), robustness(they are robust to noise and minor variations in shape) and
expressiveness(since the basis is orthogonal, they have minimum information redundancy).
3.2 CLASSIFICATION
Once significant features are extracted from X-ray images, a good classification technique is
needed to identify the target object with a quantified confidence level so that this information can
assist the security operator in making an appropriate response. Classifiers ranging from KNN to
the Artificial Neural Networks (ANNs)are used for image classification, shape recognition, and
image retrieval. However the classification rate in x-ray luggage scanning is well below
satisfactory levels.
3.2.1 FUZZY K-NN CLASSIFIER
Fuzzy k-nearest neighbor is a classification technique, which provides the simplicity and the
practicability of classical K nearest neighbor and also the advantages of using fuzzy logic. Fuzzy
sets are sets whose elements have degrees of membership. In classical set theory, the membership
of elements in a set is assessed in binary terms according to a bivalent condition — an element
either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual
assessment of the membership of elements in a set; this is described with the aid of a membership
function. This algorithm assigns membership as a function of the object’s distance from its K-
nearest neighbours and the memberships in the possible classes.The main algorithm is very
similar to K-NN. In the training procedure, the sample objects are located to the feature vector
space and these samples are initialized in a fuzzy state. In the classification phase, for each new
object x, it’s K nearest neighbors are detected and then class membership values are calculated
according to the following formula:
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
193
∑
∑
=
=
= K
j
j
K
j
jij
i
W
Wu
xu
1
1
)( (9)
Where, uij
is the i
th
class membership value of neighborj
and Wj is the weight of the neighborj
given by
(10)
A distance measure d(i1,i2) between any pair a1=(a1,1 ,.........,a1,k); a2=(a2,1 ,.........,a2,k) of instances
Euclidean distance is given by
2
1
,2,1212 )(),( ∑=
−=
k
j
jj aaaad (11)
The value of m, used to scale the effect of the distance between x and neighborj
, is entirely
arbitrary. As m approaches to ± infinity, the results of classifier approach to K-nn.
Figure 3 A Classification Example
Figure 3 shows A classification example of an unlabeled object with using fuzzy k nearest
neighbor algorithm. In the figure (a), the unlabeled green circular object’s 3 neighbors are at the
same distance.
Fuzzy K-NN approach reduces the disadvantages of traditional K-NN approach and also it is a
simple and effective solution for classification.
( ) 1
2
tan
1
−
=
m
j
cedis
W
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
194
4. RESULTS AND DISCUSSION
In this work the x-ray images of bags which are taken in the airport were considered. Size
of the images used for processing is 310 X 1035. To obtain the optimum result in
classification an efficient algorithm is used.In this project, feature extraction technique is
applied to the input pre-processed image to extract features .A total of 15 images are taken among
which seven images are with weapon and the remaining images are without weapon. The
classifier which is used in this project classify efficiently. The classifier is initially trained for all
the images. The training images with a new set of two images which is not included in the
training is considered for testing. Figure 4 shows the objects of interest extracted after preliminary
feature extraction process.A set of training and testing images considered for classification is
shown in Figure 5 and Figure 6 respectively.
Figure 5 Training Images
Figure 4 Objects Of Interest Extracted
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
195
Figure 6 Testing Images
Figure 7 shows the detected weapon in the x-ray images considered.
Figure 7 Detected Weapon
5. CONCLUSION
In this work, a fuzzy KNN based classifier has been presented to detect concealed
weapon by capturing and analysing x-ray images. This proposed work relies on reliable
features like Shape context descriptor and Zernike moments to detect concealed weapons.
The proposed method performs satisfactorily and the future work involves classification
of detected weapons.
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
196
References
[1] Abidi, B., Y. Zheng, A. Gribok, and M. Abidi,. Improving Weapon Detection in Single Energy X-
Ray Images Through Pseudocoloring.
[2] N.E.L. Shanks and A.L.W. Bradley, Handbook of Checked Baggage Screening: Advanced Airport
Security Operation, WileyBlackwell, 2004.
[3] Steve Green, Michael Blumenstein, Vallipuram Muthukkumarasamy and Jun Jo School of
Information Technology, Investigation of a classification-based technique to detect Illicit objects for
aviation security.
[4] D. Hardmeier, F. Hofer, A. Schwaninger,"The X-ray object recognition test (X-ray ORT) – a reliable
and valid instrument for measuring visual abilities needed in X-ray screening," IEEE CCST 2005, pp.
189-192.
[5] Abidi, B., Y. Zheng, A. Gribok and M. Abidi, Screener Evaluation of Pseudo-Colored Single Energy
X-ray Luggage Images. Proceedings of IEEE conference on Computer Vision and Pattern Recognition
Workshop, San Diego CA June 2005.
[6] B. Abidi, J. Liang, M. Mitckes, and M. Abidi. Improving the detection of lowdensity weapons in x-
ray luggage scans using image enhancement and novel scene-decluttering techniques. Jrnl of Elec.
Imaging, 13(3):523–538, 2004.
[7] A. C. Kak and M. Slaney. Principles of computerized tomographic imaging. Society for Industrial
and Applied Mathematics, PA, USA, 2001.
[8] S. Singh and M. Singh, “Explosives detection systems(EDS) for aviation security,” Signal Processing,
vol. 83, no. 1, pp. 31–55, 2003.
[9] R. Gesick, C. Saritac, and C. Hung, “Automatic Image Analysis process for the detection of concealed
weapon” in Proceedings of the 5th
Annual Workshop on Cyber Security and Information Intelligent
Research,2009.
[10] V. Muthukkumarasamy, M. Blumenstein, J. Jo, and S.Green, “Intelligent illicit object detection
system for enhanced aviation security”,in International Conference on Simulated Evolution and
Learning,2004.
[11] X.Shi, “Improving object classification in X-ray luggage inspection,Ph.D Thesis,Department of
Computer and Electrical Engineering,Virginia Tech.and State University,2000.
[12] T. Feather, I. Guan, A. Lee-Kwen, and R.B. Paranjape, “Caxss: an intelligent threat detection system,”
SPIE, Applications of
Signal and image Processing in Explosives Detection Systems, vol. 1824,pp.152-161, 1993.
[13] A.S.Belongie,J.Malik and J.Puzicha, “Shape matching and object recognition using shape
contexts”,IEEE Trans.PAMI,vol.24,pp509-522,April 2002.
[14] A.Khotanzad and Y.H.Hong, “Invariant Image Recognition by Zernike Moments”,IEEE Transactions
on Pattern Anal& Machine Intelligence,vol.PAMI-12,No.5.,pp.489-497,1990.
[15] Keller, J.M., Gray, R., Givens, J.A.J.R. A Fuzzy k-nearest Neighbor Algorithm. IEEE Trans. Systems
Man Cybernet. 15(4), 580-585, 1985.
Authors
S.Mohamed Mansoor Roomi received his B.E degree in Electronics and communication.
Engineering from Madurai Kamarajar University, in 1990 and the M. E (Power Systems) &
ME (Communication Systems) from Thiagarajar College of Engineering, Madurai in
1992&1997 and phd in 2009 from Madurai Kamarajar University.His primary Research
Interests include Image Enhancement and Analysis.
R.Rajashankari received her B.E degree in is in Electronics and Communication. Engineering
from Sethu Institute of Technology,Virudhunagar.She is currently doing her M.E degree in
Communication systems at Thiagarajar College of Engineering,Madurai,India Her research
interests.

More Related Content

What's hot

Ijarcet vol-2-issue-4-1383-1388
Ijarcet vol-2-issue-4-1383-1388Ijarcet vol-2-issue-4-1383-1388
Ijarcet vol-2-issue-4-1383-1388
Editor IJARCET
 
Paper id 252014130
Paper id 252014130Paper id 252014130
Paper id 252014130
IJRAT
 

What's hot (17)

Ear Biometrics shritosh kumar
Ear Biometrics shritosh kumarEar Biometrics shritosh kumar
Ear Biometrics shritosh kumar
 
TEMPLATE MATCHING TECHNIQUE FOR SEARCHING WORDS IN DOCUMENT IMAGES
TEMPLATE MATCHING TECHNIQUE FOR SEARCHING WORDS IN DOCUMENT IMAGESTEMPLATE MATCHING TECHNIQUE FOR SEARCHING WORDS IN DOCUMENT IMAGES
TEMPLATE MATCHING TECHNIQUE FOR SEARCHING WORDS IN DOCUMENT IMAGES
 
A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...
A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...
A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...
 
Ijarcet vol-2-issue-4-1383-1388
Ijarcet vol-2-issue-4-1383-1388Ijarcet vol-2-issue-4-1383-1388
Ijarcet vol-2-issue-4-1383-1388
 
A novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationA novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentation
 
Issues in Image Registration and Image similarity based on mutual information
Issues in Image Registration and Image similarity based on mutual informationIssues in Image Registration and Image similarity based on mutual information
Issues in Image Registration and Image similarity based on mutual information
 
[IJET-V2I2P6] Authors:Atul Ganbawle , Prof J.A. Shaikh
[IJET-V2I2P6] Authors:Atul Ganbawle , Prof J.A. Shaikh[IJET-V2I2P6] Authors:Atul Ganbawle , Prof J.A. Shaikh
[IJET-V2I2P6] Authors:Atul Ganbawle , Prof J.A. Shaikh
 
A NEW CODING METHOD IN PATTERN RECOGNITION FINGERPRINT IMAGE USING VECTOR QUA...
A NEW CODING METHOD IN PATTERN RECOGNITION FINGERPRINT IMAGE USING VECTOR QUA...A NEW CODING METHOD IN PATTERN RECOGNITION FINGERPRINT IMAGE USING VECTOR QUA...
A NEW CODING METHOD IN PATTERN RECOGNITION FINGERPRINT IMAGE USING VECTOR QUA...
 
Q0460398103
Q0460398103Q0460398103
Q0460398103
 
Face recognition using gaussian mixture model & artificial neural network
Face recognition using gaussian mixture model & artificial neural networkFace recognition using gaussian mixture model & artificial neural network
Face recognition using gaussian mixture model & artificial neural network
 
Enhanced Thinning Based Finger Print Recognition
Enhanced Thinning Based Finger Print RecognitionEnhanced Thinning Based Finger Print Recognition
Enhanced Thinning Based Finger Print Recognition
 
F045033337
F045033337F045033337
F045033337
 
Ijartes v1-i2-008
Ijartes v1-i2-008Ijartes v1-i2-008
Ijartes v1-i2-008
 
Separation of overlapping latent fingerprints
Separation of overlapping latent fingerprintsSeparation of overlapping latent fingerprints
Separation of overlapping latent fingerprints
 
A comparative study on classification of image segmentation methods with a fo...
A comparative study on classification of image segmentation methods with a fo...A comparative study on classification of image segmentation methods with a fo...
A comparative study on classification of image segmentation methods with a fo...
 
Paper id 252014130
Paper id 252014130Paper id 252014130
Paper id 252014130
 
National Flags Recognition Based on Principal Component Analysis
National Flags Recognition Based on Principal Component AnalysisNational Flags Recognition Based on Principal Component Analysis
National Flags Recognition Based on Principal Component Analysis
 

Similar to DETECTION OF CONCEALED WEAPONS IN X-RAY IMAGES USING FUZZY K-NN

Real time implementation of object tracking through
Real time implementation of object tracking throughReal time implementation of object tracking through
Real time implementation of object tracking through
eSAT Publishing House
 
Kernel based similarity estimation and real time tracking of moving
Kernel based similarity estimation and real time tracking of movingKernel based similarity estimation and real time tracking of moving
Kernel based similarity estimation and real time tracking of moving
IAEME Publication
 

Similar to DETECTION OF CONCEALED WEAPONS IN X-RAY IMAGES USING FUZZY K-NN (20)

Enhancing the Design pattern Framework of Robots Object Selection Mechanism -...
Enhancing the Design pattern Framework of Robots Object Selection Mechanism -...Enhancing the Design pattern Framework of Robots Object Selection Mechanism -...
Enhancing the Design pattern Framework of Robots Object Selection Mechanism -...
 
Real time implementation of object tracking through
Real time implementation of object tracking throughReal time implementation of object tracking through
Real time implementation of object tracking through
 
Segmentation and Classification of MRI Brain Tumor
Segmentation and Classification of MRI Brain TumorSegmentation and Classification of MRI Brain Tumor
Segmentation and Classification of MRI Brain Tumor
 
V.KARTHIKEYAN PUBLISHED ARTICLE A.A
V.KARTHIKEYAN PUBLISHED ARTICLE A.AV.KARTHIKEYAN PUBLISHED ARTICLE A.A
V.KARTHIKEYAN PUBLISHED ARTICLE A.A
 
Object Recogniton Based on Undecimated Wavelet Transform
Object Recogniton Based on Undecimated Wavelet TransformObject Recogniton Based on Undecimated Wavelet Transform
Object Recogniton Based on Undecimated Wavelet Transform
 
J017426467
J017426467J017426467
J017426467
 
Building extraction from remote sensing imageries by data fusion techniques
Building extraction from remote sensing imageries by data fusion techniquesBuilding extraction from remote sensing imageries by data fusion techniques
Building extraction from remote sensing imageries by data fusion techniques
 
Building extraction from remote sensing imageries by data fusion techniques
Building extraction from remote sensing imageries by data fusion techniquesBuilding extraction from remote sensing imageries by data fusion techniques
Building extraction from remote sensing imageries by data fusion techniques
 
Human Computer Interaction Algorithm Based on Scene Situation Awareness
Human Computer Interaction Algorithm Based on Scene Situation Awareness Human Computer Interaction Algorithm Based on Scene Situation Awareness
Human Computer Interaction Algorithm Based on Scene Situation Awareness
 
Pixel Based Fusion Methods for Concealed Weapon Detection
Pixel Based Fusion Methods for Concealed Weapon DetectionPixel Based Fusion Methods for Concealed Weapon Detection
Pixel Based Fusion Methods for Concealed Weapon Detection
 
Target Detection Using Multi Resolution Analysis for Camouflaged Images
Target Detection Using Multi Resolution Analysis for Camouflaged Images Target Detection Using Multi Resolution Analysis for Camouflaged Images
Target Detection Using Multi Resolution Analysis for Camouflaged Images
 
Kernel based similarity estimation and real time tracking of moving
Kernel based similarity estimation and real time tracking of movingKernel based similarity estimation and real time tracking of moving
Kernel based similarity estimation and real time tracking of moving
 
Development of Human Tracking System For Video Surveillance
Development of Human Tracking System For Video SurveillanceDevelopment of Human Tracking System For Video Surveillance
Development of Human Tracking System For Video Surveillance
 
E0333021025
E0333021025E0333021025
E0333021025
 
EXPLOITING REFERENCE IMAGES IN EXPOSING GEOMETRICAL DISTORTIONS
EXPLOITING REFERENCE IMAGES IN EXPOSING GEOMETRICAL DISTORTIONSEXPLOITING REFERENCE IMAGES IN EXPOSING GEOMETRICAL DISTORTIONS
EXPLOITING REFERENCE IMAGES IN EXPOSING GEOMETRICAL DISTORTIONS
 
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...Feature Extraction for Image Classification and Analysis with Ant Colony Opti...
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...
 
F1063337
F1063337F1063337
F1063337
 
International Journal of Engineering Research and Development
International Journal of Engineering Research and DevelopmentInternational Journal of Engineering Research and Development
International Journal of Engineering Research and Development
 
Integration of poses to enhance the shape of the object tracking from a singl...
Integration of poses to enhance the shape of the object tracking from a singl...Integration of poses to enhance the shape of the object tracking from a singl...
Integration of poses to enhance the shape of the object tracking from a singl...
 
A STOCHASTIC STATISTICAL APPROACH FOR TRACKING HUMAN ACTIVITY
A STOCHASTIC STATISTICAL APPROACH FOR TRACKING HUMAN ACTIVITYA STOCHASTIC STATISTICAL APPROACH FOR TRACKING HUMAN ACTIVITY
A STOCHASTIC STATISTICAL APPROACH FOR TRACKING HUMAN ACTIVITY
 

More from IJCSEIT Journal

BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...
BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...
BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...
IJCSEIT Journal
 
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
IJCSEIT Journal
 

More from IJCSEIT Journal (20)

ANALYSIS OF EXISTING TRAILERS’ CONTAINER LOCK SYSTEMS
ANALYSIS OF EXISTING TRAILERS’ CONTAINER LOCK SYSTEMS ANALYSIS OF EXISTING TRAILERS’ CONTAINER LOCK SYSTEMS
ANALYSIS OF EXISTING TRAILERS’ CONTAINER LOCK SYSTEMS
 
A MODEL FOR REMOTE ACCESS AND PROTECTION OF SMARTPHONES USING SHORT MESSAGE S...
A MODEL FOR REMOTE ACCESS AND PROTECTION OF SMARTPHONES USING SHORT MESSAGE S...A MODEL FOR REMOTE ACCESS AND PROTECTION OF SMARTPHONES USING SHORT MESSAGE S...
A MODEL FOR REMOTE ACCESS AND PROTECTION OF SMARTPHONES USING SHORT MESSAGE S...
 
BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...
BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...
BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...
 
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...
 
BIOMETRICS AUTHENTICATION TECHNIQUE FOR INTRUSION DETECTION SYSTEMS USING FIN...
BIOMETRICS AUTHENTICATION TECHNIQUE FOR INTRUSION DETECTION SYSTEMS USING FIN...BIOMETRICS AUTHENTICATION TECHNIQUE FOR INTRUSION DETECTION SYSTEMS USING FIN...
BIOMETRICS AUTHENTICATION TECHNIQUE FOR INTRUSION DETECTION SYSTEMS USING FIN...
 
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
 
Effect of Interleaved FEC Code on Wavelet Based MC-CDMA System with Alamouti ...
Effect of Interleaved FEC Code on Wavelet Based MC-CDMA System with Alamouti ...Effect of Interleaved FEC Code on Wavelet Based MC-CDMA System with Alamouti ...
Effect of Interleaved FEC Code on Wavelet Based MC-CDMA System with Alamouti ...
 
FUZZY WEIGHTED ASSOCIATIVE CLASSIFIER: A PREDICTIVE TECHNIQUE FOR HEALTH CARE...
FUZZY WEIGHTED ASSOCIATIVE CLASSIFIER: A PREDICTIVE TECHNIQUE FOR HEALTH CARE...FUZZY WEIGHTED ASSOCIATIVE CLASSIFIER: A PREDICTIVE TECHNIQUE FOR HEALTH CARE...
FUZZY WEIGHTED ASSOCIATIVE CLASSIFIER: A PREDICTIVE TECHNIQUE FOR HEALTH CARE...
 
GENDER RECOGNITION SYSTEM USING SPEECH SIGNAL
GENDER RECOGNITION SYSTEM USING SPEECH SIGNALGENDER RECOGNITION SYSTEM USING SPEECH SIGNAL
GENDER RECOGNITION SYSTEM USING SPEECH SIGNAL
 
META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL
META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVALMETA-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL
META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL
 
ERROR PERFORMANCE ANALYSIS USING COOPERATIVE CONTENTION-BASED ROUTING IN WIRE...
ERROR PERFORMANCE ANALYSIS USING COOPERATIVE CONTENTION-BASED ROUTING IN WIRE...ERROR PERFORMANCE ANALYSIS USING COOPERATIVE CONTENTION-BASED ROUTING IN WIRE...
ERROR PERFORMANCE ANALYSIS USING COOPERATIVE CONTENTION-BASED ROUTING IN WIRE...
 
M-FISH KARYOTYPING - A NEW APPROACH BASED ON WATERSHED TRANSFORM
M-FISH KARYOTYPING - A NEW APPROACH BASED ON WATERSHED TRANSFORMM-FISH KARYOTYPING - A NEW APPROACH BASED ON WATERSHED TRANSFORM
M-FISH KARYOTYPING - A NEW APPROACH BASED ON WATERSHED TRANSFORM
 
RANDOMIZED STEGANOGRAPHY IN SKIN TONE IMAGES
RANDOMIZED STEGANOGRAPHY IN SKIN TONE IMAGESRANDOMIZED STEGANOGRAPHY IN SKIN TONE IMAGES
RANDOMIZED STEGANOGRAPHY IN SKIN TONE IMAGES
 
A NOVEL WINDOW FUNCTION YIELDING SUPPRESSED MAINLOBE WIDTH AND MINIMUM SIDELO...
A NOVEL WINDOW FUNCTION YIELDING SUPPRESSED MAINLOBE WIDTH AND MINIMUM SIDELO...A NOVEL WINDOW FUNCTION YIELDING SUPPRESSED MAINLOBE WIDTH AND MINIMUM SIDELO...
A NOVEL WINDOW FUNCTION YIELDING SUPPRESSED MAINLOBE WIDTH AND MINIMUM SIDELO...
 
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...
 
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...
 
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASE
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEUSING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASE
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASE
 
FACTORS AFFECTING ACCEPTANCE OF WEB-BASED TRAINING SYSTEM: USING EXTENDED UTA...
FACTORS AFFECTING ACCEPTANCE OF WEB-BASED TRAINING SYSTEM: USING EXTENDED UTA...FACTORS AFFECTING ACCEPTANCE OF WEB-BASED TRAINING SYSTEM: USING EXTENDED UTA...
FACTORS AFFECTING ACCEPTANCE OF WEB-BASED TRAINING SYSTEM: USING EXTENDED UTA...
 
PROBABILISTIC INTERPRETATION OF COMPLEX FUZZY SET
PROBABILISTIC INTERPRETATION OF COMPLEX FUZZY SETPROBABILISTIC INTERPRETATION OF COMPLEX FUZZY SET
PROBABILISTIC INTERPRETATION OF COMPLEX FUZZY SET
 
ALGORITHMIC AND ARCHITECTURAL OPTIMIZATION OF A 3D RECONSTRUCTION MEDICAL IMA...
ALGORITHMIC AND ARCHITECTURAL OPTIMIZATION OF A 3D RECONSTRUCTION MEDICAL IMA...ALGORITHMIC AND ARCHITECTURAL OPTIMIZATION OF A 3D RECONSTRUCTION MEDICAL IMA...
ALGORITHMIC AND ARCHITECTURAL OPTIMIZATION OF A 3D RECONSTRUCTION MEDICAL IMA...
 

Recently uploaded

Maher Othman Interior Design Portfolio..
Maher Othman Interior Design Portfolio..Maher Othman Interior Design Portfolio..
Maher Othman Interior Design Portfolio..
MaherOthman7
 
Artificial intelligence presentation2-171219131633.pdf
Artificial intelligence presentation2-171219131633.pdfArtificial intelligence presentation2-171219131633.pdf
Artificial intelligence presentation2-171219131633.pdf
Kira Dess
 
electrical installation and maintenance.
electrical installation and maintenance.electrical installation and maintenance.
electrical installation and maintenance.
benjamincojr
 

Recently uploaded (20)

The Entity-Relationship Model(ER Diagram).pptx
The Entity-Relationship Model(ER Diagram).pptxThe Entity-Relationship Model(ER Diagram).pptx
The Entity-Relationship Model(ER Diagram).pptx
 
Involute of a circle,Square, pentagon,HexagonInvolute_Engineering Drawing.pdf
Involute of a circle,Square, pentagon,HexagonInvolute_Engineering Drawing.pdfInvolute of a circle,Square, pentagon,HexagonInvolute_Engineering Drawing.pdf
Involute of a circle,Square, pentagon,HexagonInvolute_Engineering Drawing.pdf
 
Raashid final report on Embedded Systems
Raashid final report on Embedded SystemsRaashid final report on Embedded Systems
Raashid final report on Embedded Systems
 
CLOUD COMPUTING SERVICES - Cloud Reference Modal
CLOUD COMPUTING SERVICES - Cloud Reference ModalCLOUD COMPUTING SERVICES - Cloud Reference Modal
CLOUD COMPUTING SERVICES - Cloud Reference Modal
 
handbook on reinforce concrete and detailing
handbook on reinforce concrete and detailinghandbook on reinforce concrete and detailing
handbook on reinforce concrete and detailing
 
Maher Othman Interior Design Portfolio..
Maher Othman Interior Design Portfolio..Maher Othman Interior Design Portfolio..
Maher Othman Interior Design Portfolio..
 
Artificial intelligence presentation2-171219131633.pdf
Artificial intelligence presentation2-171219131633.pdfArtificial intelligence presentation2-171219131633.pdf
Artificial intelligence presentation2-171219131633.pdf
 
Independent Solar-Powered Electric Vehicle Charging Station
Independent Solar-Powered Electric Vehicle Charging StationIndependent Solar-Powered Electric Vehicle Charging Station
Independent Solar-Powered Electric Vehicle Charging Station
 
Artificial Intelligence in due diligence
Artificial Intelligence in due diligenceArtificial Intelligence in due diligence
Artificial Intelligence in due diligence
 
Maximizing Incident Investigation Efficacy in Oil & Gas: Techniques and Tools
Maximizing Incident Investigation Efficacy in Oil & Gas: Techniques and ToolsMaximizing Incident Investigation Efficacy in Oil & Gas: Techniques and Tools
Maximizing Incident Investigation Efficacy in Oil & Gas: Techniques and Tools
 
Software Engineering Practical File Front Pages.pdf
Software Engineering Practical File Front Pages.pdfSoftware Engineering Practical File Front Pages.pdf
Software Engineering Practical File Front Pages.pdf
 
electrical installation and maintenance.
electrical installation and maintenance.electrical installation and maintenance.
electrical installation and maintenance.
 
8th International Conference on Soft Computing, Mathematics and Control (SMC ...
8th International Conference on Soft Computing, Mathematics and Control (SMC ...8th International Conference on Soft Computing, Mathematics and Control (SMC ...
8th International Conference on Soft Computing, Mathematics and Control (SMC ...
 
5G and 6G refer to generations of mobile network technology, each representin...
5G and 6G refer to generations of mobile network technology, each representin...5G and 6G refer to generations of mobile network technology, each representin...
5G and 6G refer to generations of mobile network technology, each representin...
 
Dynamo Scripts for Task IDs and Space Naming.pptx
Dynamo Scripts for Task IDs and Space Naming.pptxDynamo Scripts for Task IDs and Space Naming.pptx
Dynamo Scripts for Task IDs and Space Naming.pptx
 
litvinenko_Henry_Intrusion_Hong-Kong_2024.pdf
litvinenko_Henry_Intrusion_Hong-Kong_2024.pdflitvinenko_Henry_Intrusion_Hong-Kong_2024.pdf
litvinenko_Henry_Intrusion_Hong-Kong_2024.pdf
 
SLIDESHARE PPT-DECISION MAKING METHODS.pptx
SLIDESHARE PPT-DECISION MAKING METHODS.pptxSLIDESHARE PPT-DECISION MAKING METHODS.pptx
SLIDESHARE PPT-DECISION MAKING METHODS.pptx
 
Interfacing Analog to Digital Data Converters ee3404.pdf
Interfacing Analog to Digital Data Converters ee3404.pdfInterfacing Analog to Digital Data Converters ee3404.pdf
Interfacing Analog to Digital Data Converters ee3404.pdf
 
Research Methodolgy & Intellectual Property Rights Series 1
Research Methodolgy & Intellectual Property Rights Series 1Research Methodolgy & Intellectual Property Rights Series 1
Research Methodolgy & Intellectual Property Rights Series 1
 
Insurance management system project report.pdf
Insurance management system project report.pdfInsurance management system project report.pdf
Insurance management system project report.pdf
 

DETECTION OF CONCEALED WEAPONS IN X-RAY IMAGES USING FUZZY K-NN

  • 1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 DOI : 10.5121/ijcseit.2012.2216 187 DETECTION OF CONCEALED WEAPONS IN X-RAY IMAGES USING FUZZY K-NN Dr. Mohamed Mansoor Roomi #1 R.Rajashankari#2 Department of Electronics and Communication Engineering, Thiagarajar College of Engineering,Madurai 1 smmroomi@tce.edu 2 r.rajasankari@gmail.com ABSTRACT Scanning baggage by x-ray and analysing such images have become important technique for detecting illicit materials in the baggage at Airports. In order to provide adequate security, a reliable and fast screening technique is needed for baggage examination.This paper aims at providing an automatic method for detecting concealed weapons, typically a gun in the baggage by employing image segmentation method to extract the objects of interest from the image followed by applying feature extraction methods namely Shape context descriptor and Zernike moments. Finally the objects are classified using fuzzy KNN as illicit or non-illicit object. KEYWORDS Aviation security, Shape Context Descriptor, Zernike Moments, Nearest Neighbour Classifier 1.INTRODUCTION X-ray imaging is an important technology in many fields from inspection of delicate objects to weapon detection at security checkpoints [1].To achieve higher threat detection rates during inspection of X-ray luggage scans is a pressing and sought after goal for airport security personnel. The Baggage inspection system used in airport ensures security of the passengers. The process of identifying the contents of each bag and the methods adopted by terrorists for hiding the threat objects are complicated, the existing luggage inspection system do not reveal 100% of threat items. Further an object inside a bag may be in any position, it may be rotated so an algorithm whist is rotational, translational invariant should be used for providing accurate results. In addition, the threat item is superimposed by other objects in the bag, the harder it becomes to detect it (effect of superposition). The passenger’s baggage may contain threat items such as handgun, bomb, grenade,etc which must be detected efficiently so the human operators must be assisted by an weapon detection system. Advanced security screening systems are becoming increasingly used to aid airport screeners in detecting potential threat items [2]. Unfortunately, most airport screening is still based on the manual detection of potential threat objects by human experts. In response to this, security training is relying heavily on the object recognition test (ORT) as a means of qualifying human airport luggage screeners [4].In order to provide appropriate security, a much more sophisticated, reliable, and fast screening technique is needed for passenger identification and baggage examination. Automatic threat detection is an important application in x-ray scene analysis. Understanding x-ray images is a challenging task in computer
  • 2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 188 vision and an automatic system should be developed that consumes less time for processing and performs accurately with reduced false positive results. Although several X-ray technology based automatic systems exist for threats detection, only a few of these systems make use of the well established pattern recognition and machine learning techniques.On the other hand, several approaches based on Classifier have been proposed to detect weapons[3].Additionally, the importance of image enhancement and pseudo-coloring[5] to help aid decision making by human is now a recognized area of critical need. Also, the system should provide automatic detection of potential threat objects. 2. RELATED WORK For the detection of threat items, many types of imaging system exist. X-ray imaging systems and MMW (Millimetre wave imaging) are used and x-ray imaging system is widely used for carry-on bags. The techniques used for analysing these x-ray images are pseudo-coloring and segmentation based techniques. Pseudo-coloring [1] process is the one in which the objects inside the bag are given different colors based on their material type. In segmentation based methods, the x-ray images are segmented to extract the objects of interest. Using these methods, satisfactory results are produced and assisted human for detecting the threat items. X-ray photons, however, penetrate most materials. As a result, all objects along an x-ray path attenuate the x-ray and contribute to the final measured intensity. In the x-ray community, a common way of disambiguating objects is through CT reconstruction [7]. This is typically obtained through the filtered back-projection algorithm. Although several X-ray technology based automatic systems exist for threats detection [8], only a few of these systems make use of the well established pattern recognition and machine learning techniques [9, 10, 11, and 12]. New X-ray imaging systems at airports use dual-energy analysis to estimate the atomic numbers of materials in the passenger baggage. This method obtains a measure of the density and thickness of the material. 3. PROPOSED WORK. The image is converted to binary image by choosing the threshold as the mean of the two peaks of bimodal histogram and the objects are labelled. The area of each object is computed and the values are sorted. The mean value is calculated and is set as threshold to collect the objects of interest. Object boundary is extracted and shape feature extraction algorithm is implemented. The classifier is trained with the extracted features and the object is classified as object or non-object. Figure 1 SampleX-ray Images
  • 3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 189 Figure 1 shows some of the x-ray images considered in this work.Figure 2 shows the overall frame work for the approach. Image Figure 1 Flow chart of proposed work 3.1 FEATURE EXTRACTION 3.1.1 SHAPE CONTEXT DESCRIPTOR Shape is not the only, but a very powerful descriptor of image content .Shape is almost certainly the most important property that is perceived about objects. Shape provides more information about the object than other features and can be used in object recognition. Using shape as a attribute provide more accurate and reliable results .Addressing objects based on their shape is unique.Shape is an important cue as it captures a prominent element of an object.. Ideally, a good shape descriptor has the following desirable properties1)Discrimination should be high;2)Efficient matching;3)Compact representation;4)Efficient Feature Extraction;5)Invariance to shape representation;6)Invariance to similarity transformation;7)Invariance to shape degeneracies and noises. Shape context is a shape descriptor proposed by Serge Belongie and Jitendra Malik[13]. The shape context is anticipated to be a way of describing shapes that allows for measuring shape similarity and the recovering of point correspondences.It characterize a particular Removal of Spurious Component Preliminary Feature Extraction Binarization Extract Object Boundary Co-ordinates Shape Context Descriptor Zernike Moments Max, Min, Mean Gray value Fuzzy k-NN Classifier
  • 4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 190 point location on the shape.The fundamental idea is to pick n points on the contours of a shape. For each point pi on the shape, consider the n − 1 vectors obtained by connecting pi to all other points.The set of all these vectors is a rich description of the shape localized at that point but is far too detailed. The key initiative is that the distribution over relative positions is a robust, compressed, and very discriminative descriptor. So, for the point pi, the coarse histogram of the relative coordinates of the remaining n − 1 points, Concretely, for a point pi on the shape, compute a coarse histogram hi of the relative coordinates of the remaining n-1 points )}()(:{# kbinpqpqh ii k i ∈−≠= (1) is define to be the shape context of pi . The bins are normally taken to be uniform in log- polar space. In the absence of background clutter, the shape context of a point on a shape can be made invariant under uniform scaling of the shape as a whole. This is accomplished by normalizing all radial distances by the mean distance α between the n2 point pairs in the shape. Consider the shape of the alphabet fig.3 (a).sampled edge points of the shape. A log polar histogram bin as shown in fig.3 (b) is overlaid on any sampled boundary point. Belongie et al. have used 12 bins for log r and 5 bins for angle θ. As illustrated in Fig. 1, shape contexts is computed for each point in the shape and will be unique for each point and similar shapes will have similar shape context.Translational invariance come naturally to shape context. Scale invariance is obtained by normalizing all radial distances by the mean distance between all the point pairs in the shape. Figure 2 Shape Context Computation 3.1.2 ZERNIKE MOMENTS Moments have been widely used in image processing applications through the years. For both contour and region of a shape, one can use moment's theory to analyse the object. Geometrical, central and normalized moments were for many decades the only family of applied moments. The main disadvantage of these descriptors was their disability to fully describe an object in a way that, using the moments set, the reconstruction of the object could be possible. In other words they are not orthogonal. Zernike comes to fill this gap, by introducing a set of complex polynomials, which form a complete orthogonal set over the interior of the unit circle, x2 + y2 = 1. These polynomials have the form )exp()(),(),( θρθρ jmyx RVV nmnmnm == (2)
  • 5. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 191 Where n is a non-negative integer m is a non zero integer subject to the constraints n-|m| even and |m|≤ n, ρ is the length of vector from the origin ( )yx, to the pixel(x, y),θ the angle between vector ρ and x axis in counter-clockwise direction. Rnm(ρ ) is the Zernike radial polynomials in (ρ,θ) polar coordinates )()(, ρρ nmmn RR =− (3) The Zernike moment of order n with repetition m for a continuous image function f(x,y), that vanishes outside the unit disk is ∫∫ ≤+ + = 1 * 22 ),(),( 1 ),( yx nmnm dxdyVyxf n yxZ θρ π (4) For a digital image, the integrals are replaced by summations to get ∑∑ ≤+ + = x nm y nm yxyxf n VZ 1),,(),( 1 22* θρ π (5) Suppose that one knows all moments Znm of f(x,y) up to a given order nmax. It is desired to reconstruct a discrete function ),(ˆ yxf whose moments exactly match those of f(x,y) up to the given order nmax. Zernike moments are the coefficients of the image expansion into orthogonal Zernike polynomials. By orthogonality of the Zernike basis ),(),(ˆ max 0 θρnm n n m nm Vyxf Z∑∑= = (6) Since Zernike moments are only rotationally invariant, additional properties of translation and scale invariance should be given to these moments in some way. We can introduce translation invariance in the Zernike moments by converting the absolute pixel coordinates as follows       − − →      0 0 Yy Xx y x (7) Where 00 10 0 m m X = 00 01 0 m m Y = are the centroid coordinates of the object (with m denoting the geometrical moment). Scaling invariance can be achieved by normalizing the Zernike moments with respect to the geometrical moment m00 of the object. The resulting moments are derived from the following equation
  • 6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 192 00 ' m Z Z nm nm = (8) where Znm are the Zernike moments computed by using equation (4.4).Since Zernike basis functions take the unit disk as their domain, this disk must be specified before moments can be calculated. In the implementation, all the shapes are normalized into a unit circle of fixed radius. The unit disk is then centred on the shape centroid. This makes the obtained moments scale and translation invariant. Rotation invariance is achieved by only using magnitudes of the moments. Rotation invariance is achieved by only using magnitudes of the moments. The magnitudes are then normalized by dividing them by the mass of the shape. The similarity between two shapes indexed with Zernike moments descriptors is measured by the Euclidean distance between the two Zernike moments vectors. The computation of ZMD does not need to know boundary information, making it suitable for more complex shape representation. Like Fourier descriptors, Zernike moments descriptors can be constructed to arbitrary order, this overcomes the drawback of geometric moments in which higher order moments are difficult to construct. However, Zernike moments descriptors lose the perceptual meanings as those reflected in Fourier descriptors and geometric moments. Besides, ZMD does not emphasize shape boundary features which are important features of a shape. Zernike moments have many advantages such as rotation invariance(the magnitudes of Zernike moments are invariant to rotation), robustness(they are robust to noise and minor variations in shape) and expressiveness(since the basis is orthogonal, they have minimum information redundancy). 3.2 CLASSIFICATION Once significant features are extracted from X-ray images, a good classification technique is needed to identify the target object with a quantified confidence level so that this information can assist the security operator in making an appropriate response. Classifiers ranging from KNN to the Artificial Neural Networks (ANNs)are used for image classification, shape recognition, and image retrieval. However the classification rate in x-ray luggage scanning is well below satisfactory levels. 3.2.1 FUZZY K-NN CLASSIFIER Fuzzy k-nearest neighbor is a classification technique, which provides the simplicity and the practicability of classical K nearest neighbor and also the advantages of using fuzzy logic. Fuzzy sets are sets whose elements have degrees of membership. In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition — an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function. This algorithm assigns membership as a function of the object’s distance from its K- nearest neighbours and the memberships in the possible classes.The main algorithm is very similar to K-NN. In the training procedure, the sample objects are located to the feature vector space and these samples are initialized in a fuzzy state. In the classification phase, for each new object x, it’s K nearest neighbors are detected and then class membership values are calculated according to the following formula:
  • 7. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 193 ∑ ∑ = = = K j j K j jij i W Wu xu 1 1 )( (9) Where, uij is the i th class membership value of neighborj and Wj is the weight of the neighborj given by (10) A distance measure d(i1,i2) between any pair a1=(a1,1 ,.........,a1,k); a2=(a2,1 ,.........,a2,k) of instances Euclidean distance is given by 2 1 ,2,1212 )(),( ∑= −= k j jj aaaad (11) The value of m, used to scale the effect of the distance between x and neighborj , is entirely arbitrary. As m approaches to ± infinity, the results of classifier approach to K-nn. Figure 3 A Classification Example Figure 3 shows A classification example of an unlabeled object with using fuzzy k nearest neighbor algorithm. In the figure (a), the unlabeled green circular object’s 3 neighbors are at the same distance. Fuzzy K-NN approach reduces the disadvantages of traditional K-NN approach and also it is a simple and effective solution for classification. ( ) 1 2 tan 1 − = m j cedis W
  • 8. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 194 4. RESULTS AND DISCUSSION In this work the x-ray images of bags which are taken in the airport were considered. Size of the images used for processing is 310 X 1035. To obtain the optimum result in classification an efficient algorithm is used.In this project, feature extraction technique is applied to the input pre-processed image to extract features .A total of 15 images are taken among which seven images are with weapon and the remaining images are without weapon. The classifier which is used in this project classify efficiently. The classifier is initially trained for all the images. The training images with a new set of two images which is not included in the training is considered for testing. Figure 4 shows the objects of interest extracted after preliminary feature extraction process.A set of training and testing images considered for classification is shown in Figure 5 and Figure 6 respectively. Figure 5 Training Images Figure 4 Objects Of Interest Extracted
  • 9. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 195 Figure 6 Testing Images Figure 7 shows the detected weapon in the x-ray images considered. Figure 7 Detected Weapon 5. CONCLUSION In this work, a fuzzy KNN based classifier has been presented to detect concealed weapon by capturing and analysing x-ray images. This proposed work relies on reliable features like Shape context descriptor and Zernike moments to detect concealed weapons. The proposed method performs satisfactorily and the future work involves classification of detected weapons.
  • 10. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 196 References [1] Abidi, B., Y. Zheng, A. Gribok, and M. Abidi,. Improving Weapon Detection in Single Energy X- Ray Images Through Pseudocoloring. [2] N.E.L. Shanks and A.L.W. Bradley, Handbook of Checked Baggage Screening: Advanced Airport Security Operation, WileyBlackwell, 2004. [3] Steve Green, Michael Blumenstein, Vallipuram Muthukkumarasamy and Jun Jo School of Information Technology, Investigation of a classification-based technique to detect Illicit objects for aviation security. [4] D. Hardmeier, F. Hofer, A. Schwaninger,"The X-ray object recognition test (X-ray ORT) – a reliable and valid instrument for measuring visual abilities needed in X-ray screening," IEEE CCST 2005, pp. 189-192. [5] Abidi, B., Y. Zheng, A. Gribok and M. Abidi, Screener Evaluation of Pseudo-Colored Single Energy X-ray Luggage Images. Proceedings of IEEE conference on Computer Vision and Pattern Recognition Workshop, San Diego CA June 2005. [6] B. Abidi, J. Liang, M. Mitckes, and M. Abidi. Improving the detection of lowdensity weapons in x- ray luggage scans using image enhancement and novel scene-decluttering techniques. Jrnl of Elec. Imaging, 13(3):523–538, 2004. [7] A. C. Kak and M. Slaney. Principles of computerized tomographic imaging. Society for Industrial and Applied Mathematics, PA, USA, 2001. [8] S. Singh and M. Singh, “Explosives detection systems(EDS) for aviation security,” Signal Processing, vol. 83, no. 1, pp. 31–55, 2003. [9] R. Gesick, C. Saritac, and C. Hung, “Automatic Image Analysis process for the detection of concealed weapon” in Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligent Research,2009. [10] V. Muthukkumarasamy, M. Blumenstein, J. Jo, and S.Green, “Intelligent illicit object detection system for enhanced aviation security”,in International Conference on Simulated Evolution and Learning,2004. [11] X.Shi, “Improving object classification in X-ray luggage inspection,Ph.D Thesis,Department of Computer and Electrical Engineering,Virginia Tech.and State University,2000. [12] T. Feather, I. Guan, A. Lee-Kwen, and R.B. Paranjape, “Caxss: an intelligent threat detection system,” SPIE, Applications of Signal and image Processing in Explosives Detection Systems, vol. 1824,pp.152-161, 1993. [13] A.S.Belongie,J.Malik and J.Puzicha, “Shape matching and object recognition using shape contexts”,IEEE Trans.PAMI,vol.24,pp509-522,April 2002. [14] A.Khotanzad and Y.H.Hong, “Invariant Image Recognition by Zernike Moments”,IEEE Transactions on Pattern Anal& Machine Intelligence,vol.PAMI-12,No.5.,pp.489-497,1990. [15] Keller, J.M., Gray, R., Givens, J.A.J.R. A Fuzzy k-nearest Neighbor Algorithm. IEEE Trans. Systems Man Cybernet. 15(4), 580-585, 1985. Authors S.Mohamed Mansoor Roomi received his B.E degree in Electronics and communication. Engineering from Madurai Kamarajar University, in 1990 and the M. E (Power Systems) & ME (Communication Systems) from Thiagarajar College of Engineering, Madurai in 1992&1997 and phd in 2009 from Madurai Kamarajar University.His primary Research Interests include Image Enhancement and Analysis. R.Rajashankari received her B.E degree in is in Electronics and Communication. Engineering from Sethu Institute of Technology,Virudhunagar.She is currently doing her M.E degree in Communication systems at Thiagarajar College of Engineering,Madurai,India Her research interests.