The 8th International Conference on Computer Science & Education (ICCSE 2013) April 26-28, 2013. Colombo, Sri Lanka SuC1.6

Detecting Changed Areas in Images from Different

View Points

D.M.R Kulasekara

Department of Physics

University of Colombo

Colombo, Sri Lanka

dmrangak@gmail.com

Ahstract-This paper is about a method to extract changed

areas in images from different viewpoints using image processing techniques.It approximates or even outperforms previously

proposed schemes with regard to inexpensive, descriptive and

efficient, and a much faster image comparison method that can

be computed and compared. A recent report on discolouration of

world heritage Sigiri frescoes revealed that there is no proper

method in Sri Lanka to identify the discolouration and

distortions that may occur on archeologically valuable pictures.

Although many feature detection and feature matching methods

are available, these methods do only one to one feature matching

whereas archaeologists need the whole image for comparison.

Simplifying these methods to the essential images can give equal geometrical elevation using homographic transformations. Then

image subtraction methods can be applied from this subtraction

outcome to identify the areas that are different from the image

referred to. The approach in this project is to identify the

differences between the different view point images by

comparing the image of the object with the image referred to in

spite of huge or small colors gaps remaining between the original

and the new image and without considering the camera, the light

conditions and age difference between images.

Index Tenns- view point, Homographic transformations,

Image subtraction, data range, featuresdetection.

I. INTRODUCTION

Nowadays many archaeologists and scientists use the naked

eye to investigate and compare images from different viewpoints. This method is time consuming, expensive and

inefficient as well. The proposed method analyses the old

images with new images while overcoming the drawbacks in

the old method.

The main objective of the project is to compare a recently

taken image of a certain object with a reference image of the

same object and identify the changed areas of the recently

taken image.

The reference image can differ from the referred image by

age, position, where the image was taken and the angle of the

image taken, as shown in Figure 1.

There are many feature detection and feature machine

methods like Gradient Location and Orientation Histogram,

Scale-invariant feature transform, Principal Component

Analysis- Scale-invariant feature transform and Speeded Up

978-1-4673-4463-0/13/$31.00 ©2013 IEEE 525

S.M.B. Harshanath

Department of Information Technology

SUIT

Malabe, Sri Lanka

harshanathsmb@gmail.com

Robust Features. But these methods do only one to one feature

machine in images that have different angles and there is no

proper method for the whole image to compare the image

descriptively.

With the position and the angle of the camera an image

taken by it changes. So it is virtually impossible to have two

identical images of the same object. To answer these issues

common areas of different images must be used to compare

them with each other.

It is important to adjust sizes of different images to a

common size before the comparison process.

Image 1 (� .. --.--------------'. , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' .... _-----_._--_. __ .. ;,'

I

Image 2 , .. -_._--------------.. , I ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' >._----------------_.'

Fig. 1. 20 appearance of objects can change radically with viewpoint.

II. THEORY

• Highly distinctive feature detection and one to one

feature matching is done by Speeded-Up Robust

Features algorithm (SURF) [7],[9]. • Homography transforms or maps between points on

two image planes that correspond to the same location

•

•

•

•

•

on a planar object in image. It can be shown that such a

mapping represents a single 3-by-3 orthogonal matrix

[9, 13].

Transformation from one image plane to another image

plane is done by Perspective transformation [9].

Simple blur operation is used for smoothing [9].

Image transformation using Erosion and Dilation is

done using a 3-by-3 kernel with 1 iteration [9].

Image subtraction uses pixel by pixel 3 channel

subtraction [9], [15].

Identified the howspread out data, calculate range for

the Pixels colors values using the standard deviation

and mean.[17],[18].

(a) Negatively skewed

Mode

NegatNe dlrecDOil

(b) Normal (no skew)

Mean MBdian Mode

The normal cul'le

repre&!nts a pertectly symmell1ca1 d!slrilXlllon

(e) Positively skewed

Mode

Positive direction

Fig. 2. Three types of data distribution. [17].

III. METHODOLOGY AND IMPLEMENTATION

Implementation of the project was done using Open Source

Computer Vision Library 2.1 commonly known as OpenCV,

Microsoft Visual Studio 2008 and Visual C++.

First, interest points were selected at distinctive locations

on the image, such as corners, blobs, and T-junctions. Then,

the descriptive vectors were matched between the reference

image and the object image. The matching is often based on

the distance between the vectors, such as Mahalanobis distance

or Euclidean distance. Once they were matched, the

corresponding points between two images were identified.

The orthogonal matrix transforms one plane image to

another plane image by the Perspective transformation. Then

the object image plane was converted to reference image plane.

Object image comers were applied to the orthogonal matrix to

identify the common area.

These corresponding points were used to convert image from one plane to other plane using Homography transform of

points on two image planes corresponding to the same location

on a planar object in image. The output of this mapping was

represented by a single 3-by-3 orthogonal matrix[19].

Then Simple blur operation was applied on both images for

smoothening. From this action camera noise was wiped out.

The reference image was subtracted from the Object image.

This colour image subtraction was done pixel by pixel.

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Q (I,j) =P'(I,j) -P2(I,j)

Output of the subtraction was converted to gray scale, and

then the used threshold value less than the gray values of pixels

was converted to zero. { 0 , if src[X,Yl < tbresbolded value ,

dis[X,y)= 255, else.

Tbresbolded value = L'Src[x,y] / / Number of pixels

This gray level image had some small white dots. So that

Erosion and Dilation were applied on the image.

The region of white pixels in the output was used for

identification. Then the reference image was compared with

the corresponding object image region. Then the respective means and variances of the regions in the two images were

compared. Range and standard deviation measure shows the

spread out pixels vales in detected regions. If that region has a

difference in spread type then respective changes in the

particular region were identified and represented in the output.

Figure 3 is a graphical explanation of the implemented system.

Reference Image Object Image

Smoothing

D Image SubtractioD

D _.:\.pply Erosion and Dilation

D :Compare Dat3 Spreading in Corresponding Regions

D Detecting Ch3Dged Are3s in Object 1m3ge

Fig. 3. System Diagram.

IV. RESULTS

Fig. 4. Reference image taken from Kandewiharaya temple[19].

The image in figure 4 is considered as reference or

previous/initial image. The dimension of this image is 3648 x

2736.The image taken is from Kandewiharaya temple.

Fig. 5. Object image with a change position and discoloured places are added to the image[19].

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After changing the position of the camera the second image

was taken. That image is shown in the figure 5. The dimension

of this image is 3648x2736. Noises were added and brightness, contrast and fill light were changed in this image. This image

was considered as the object image.

The output of the image is shown in figure 6.Discolurated and/or deformed areas were marked by A, B, C, D, E , F, G, H,

I, J, K, L, M&N. Since these same areas (A,B) are invisible to

the naked eye, the proposed system is the best way to analyze

them. The selected areas have different intensity compared

with reference image corresponding area.

Figure 6 is the Output image and contains the common area

of referenced image, figure 4 and figure 5 is the object image.

If there were places in that common area, with different color

values then these areas were marked by the black cycle. It is not always necessary to mark with a black cycle. The

deformed area can be shown marked as it is in the original.

Fig. 6. The discolouration and defomJations places are shown with a black mark[19].

V. CONCLUSION

Archaeologists use the naked eye comparison method to

compare images. This method is inefficient and less accurate. It

does not cover the whole image and some small areas in the

image can be missed. Further, it cannot be applied on pixel by

pixel. The color changed area size depends on the size of the

image. This is why the naked eye comparison method and

feature detection fail.

This research visually identifies the discolouration and

deformations of two-dimensional images with a system

implemented. The system compares an image, pixel by pixel

against the reference image and detects seven small areas

where small changes have occurred in the referred image

including even small points like dots. If an area has different color value greater than threshold value and that area is greater

than 9 pixels, it will be identified as a respective changed area.

When colors change suddenly within a small area, then the system selects this area as respective changedplaces but it is

wrong. This wrong decision was generated by the homographic

transformations. To rectify this several object images were

used .These images must be taken from different angles.

This system transforms an image into a large collection of

local feature vectors, each of which is invariant to image

translation, scaling, rotation and partially invariant to

illumination changes and affine. Although the image is rotated,

it would not be a problem for that method. The brightness, Contrast and focusing ability of the camera is different in each

image. Brightness and contrast are adjusted before the analysis.

Since these images were captured by cameras, they contain

noise. The system uses smoothing techniques to remove this

noise.

VI. COPYRIGHT FORMS

You must submit the IEEE Electronic Copyright Form

(ECF) as described in your author-kit message. THIS FORM

MUST BE SUBMITTED IN ORDER TO PUBLISH YOUR

PAPER.

ACKNOWLEDGMENT

Since childhood, it was a habit to visit places of historical

interest. There were various old paintings called frescoes to be

seen. This influenced the decision to work on a project to

compare the discolouration in those frescoes using a computer

based method. An interest in image processing drove me

towards this.

D.M.R Kulasekara offers his heartfelt gratitude to Dr.

Chandana Jayaratne for motivating and giving advice to

complete this project.

Finally we appreciate everyone who helped with our

project. It is a great pleasure for us to acknowledge the

contributions of all the individuals who lent us a helping hand.

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[19] D.M.R Kulasekara and S.M.B. Harshanath. "Image Processing Technique to Detect Discoloration and Deformations in Ancient Pictures", presented at laffna University International Research

Conference, 1 affna, Sri Lanka 2012.