Automatic Registration Method fo

Leather Section Image using

SIFT and Wavelet Transform

Xin Zhang, Jinyong Cheng and Huayong Zhang

School of Information, Qilu University of Technology, Jinan, 250353, China

Keywords: Image registration; Key point; Wavelet transform.

Abstract: Image registration is one of the hot topics of image processing, which has been widely concerned by

researchers. However, most existing image registration methods are inefficient and inaccuracy. Thus, this

paper proposes an efficient method that uses multi-resolution wavelet transform to process the original

images. This method utilizes decomposition and reconstruction of two-layered wavelet to eliminate the error

matching points effectively. Experimental results show that this method has a better robustness, which can

not only get more key points but also improve the correct matching rate greatly.

1 INTRODUCTION

Leather is regarded as high-grade consumer goods,

because of its excellent material and attractive

appearance, but its three-dimensional structure of the

fiber, has not been fully discovered. As Nature

starting to crack the mystery of toughness of spider

silk from spider silk structure in literature (Glareh

Askarieh, My Hedhammar and Kerstin Nordling,

2010), it provides guiding significance for the

manufacture of man-made fibers manufacture.

With the development of image processing

technology, much attention has been paid to high

resolution, large image processing technology.

However, due to the order of magnitude of a piece of

leather fiber is very small, So it need to enlarge the

larger multiples. If you want to get a complete high-

definition images, image stitching (Koiolovoci,

Vagvolgyib, 2000) in which the technology is

particularly important where the image registration

is the key part of it. Image registration is found the

relative position of point to point and mapping

relation between different images of the same scene

shot, or establish connection to some feature points.

The process usually includes: feature detection,

feature matching, transformation model parameter

estimation, image resampling and transformation. In

recent years, more widely used based on feature

points matching (Thornburg Jonathan, Rodes

Charles and Lamvik Michael, 2006) are SIFT

feature matching algorithm is put forward by Lowe

DG in 2004 (HeMY, DaiYC and ZhangJ, 2008) and

SURF feature matching (Bay H, Tuytelaars T and

VanGool L, 2006) by Bay. SIFT algorithm under the

condition of the scale and rotation changes better

than the SURF (Huang L, Chen C, Shen H, 2015), in

the field of image registration and stitching been

more widely used (Jiachang Gong, Jichang Guo,

2016 and Chen Y, Shang L, 2016).

2 IMAGE ACQUISITION

2.1 Leather interface of image retrieval

The texture of leather fibre is very soft. They usually

occurred deformation easily when we cut the leather

into slices according to the spacing of a few

microns. And it also will cause some changes in

different degrees with the position and shape for

every fibre in the image.

But the leather is porous material, epoxy resin is

infiltrated into leather fiber. The resin will be

infiltration of leather fiber as soon as the leather

touched the resin which is low viscosity and strong

permeability. The leather fibre is fixed with the resin

fixative. Then we can get leather - resin composite

material with a certainty hardness. And the material

can also be mosaic, grinding, polishing and soon

technology.

Zhang X., Zhang H. and Cheng J.

Automatic Registration Method for Leather Section Image using SIFT and Wavelet Transform.

DOI: 10.5220/0006443600530057

In ISME 2016 - Information Science and Management Engineering IV (ISME 2016), pages 53-57

ISBN: 978-989-758-208-0

2.2 Maintaining the Integrity of the

Specifications

After we get the picture of metallographic sample

preparation, we can confirm the interlamllar spacing

of sequence diagram by measuring the thickness of

the substrate. The leather sample would be put into

the temperature of 40℃ for one week. After that,

polish the metallographic sample preparation which

is cylindrical to be smooth. Choose four points

which is near the leather sample, then use micron

micrometer to measure the thickness of the four

points and record the dates. We can get the leather

cross section images which are the first of the

sample sequence images via the microscope

observation.

Cut out the leather sample piece to meet the

requirements of the thickness which complete the

operation of take a photo, then take it for polishing,

respectively measuring the thickness of the

cylindrical sample piece of four points use micron

micrometer, and obtain the second image on

microscope, repeat the steps to get images in turn, so

that we can get sequence images. Through

calculation the 4 points thickness before and after in

twice, take the average as the distance between two

image, so we can obtain the layer spacing of

sequence image.

3 SIFT ALGORITHM

3.1 SIFT algorithm description

SIFT algorithm is an operator which is based on

scale space, image scaling, rotation invariance, or

even affine transformation, the noise also maintained

a certain degree of stability of local image features

described. It can detect all of SIFT key points and

128-dimensional feature vectors from each section

of the leather image. By the determination of the

position, size and orientation, it establishment a

descriptor for each key point, described through a set

of vectors to described. In order to achieve image

registration. It can be divided intoeveral steps:

1) Construction scale space, detect extreme

points, to obtain scale invariance.

The problem of SIFT algorithm can be classified as

find the key point in different scale space (Tony

Lindeberg, 1994), and the Gauss kernel function is

the only one that can generate the multi-scale space.

Convolution of Two-dimensional gaussian

function

),,(

yxG

, can achieve the purpose of

blurred image Template Rogos dimensions m*n

(generally speaking, the size is

）（）（ 16*16

−

then the image position (x, y) on the template of the

corresponding gauss formula is：

)2/()2/(

),(G

πσ

nymx

eyx

−−−

−



(1)

The scale space of two-dimensional image

),,(

yxL

can be defined as the convolution

operation of a change in scale two-dimensional

Gauss's function

),,(

yxG

with the original image

),( yxI

),(*),,(),,( yxIyxGyxL



(2)

Where * denotes the convolution operation,

is the

scale space factor, the smaller value the less

smoothed, the corresponding scale smaller.

Scale space is realized by Gauss Pyramid, each

pixel in the dog scale space need to compare with

the pixel point of neighboring points around the 8

adjacent points and the upper and lower scales

corresponding to the location of the surrounding

neighborhood 9*2 points a total of 26 pixels, so that

can detect the local minima both in the scale space

and two-dimensional image space.

2) Filter and precise positioning feature points.

DoG operator can generate strong edge response,

and it is needed to eliminate the unstable edge

response points. A poor DoG function at the point of

the peak points have larger principal curvature in the

direction of across the edge, but in the direction of

the vertical edge of is smaller. The relationship of

main curvature and characteristic value is

proportional, so it can be used to determine a

threshold value to eliminate the edge response

points, which can enhance the stability of the

matching and improve the ability of anti-noise.

3) Distribution direction value for the feature

points.

We can use the distribution features of the gradient

direction. We need to specify the direction of

parameter for every key point, then descriptor will

have rotation invariance. For the key points detected

in the DoG pyramid, gather the gradient and

directional distribution feature for pixels which are

in the Gaussian pyramid image neighboring window.

ISME 2016 - Information Science and Management Engineering IV

ISME 2016 - International Conference on Information System and Management Engineering

Below is the module value of gradient and

directional for the pixels

))1,()1,(()),1(),1((),( −−++−−+= yxLyxLyxLyxLyxm



 (3)

))

)

,1(),1(/))1,()1,(((tan,

yxLyxLyxLyxLyx −−+−−+=

−

）（



 (4)

where L represents value of a key point in spatial

scale.

Direction of sampling points is carried out by the

adjacent area window with the key points as the

center. It shown through 36 columns (Divides the 0-

360 to 10 degrees for a column).The direction of the

key points is the peak of the histogram.

4) Generate feature descriptor

After the calculated of characteristics of the key

points through the above steps, The value of the key

points feature can be determined, we can describe

the key points through a set of vectors, so that it is

not affected by changes in perspective, the impact of

changes in perspective.

First, determine the calculation the image area of

descriptor. SIFT descriptor is a representation of

statistics results about the key point adjacent area of

Gaussian image gradient, In (David G.Lowe, 2004)

Lowe papers suggest that descriptor use eight

directions information calculated by 4*4 window in

the key points scale space, in total4*4*8=128

dimensional vector. Descriptor gradient direction

histogram are computed by the blurred image of the

scale where the key point exist, the radius of image

region is expressed as:

1)1(*2*3 ++

radius

oct



(5)

where

oct

indicates the scale of group containing

key point.

Then rotate the coordinate axis as the key point

of the main direction, so the new coordinates after

the rotation is:

⎟

⎠

⎞

⎜

⎝

⎛

⎟

⎠

⎞

⎜

⎝

⎛

−

⎟

⎠

⎞

⎜

⎝

⎛

θθ

cossin

sincos

]),[,( radiusradiusyx −∈



(6)

The last, Calculate gradient value of each pixel

which in the area of image radius and distribute it to

eight directions, calculate the weights. Setting

threshold so that we can truncation larger gradient

value, then normalized processing again, remove

some direction of gradient value which is the value

of gradient too large cause by the illumination

change to improve the identification of features. If

ratio of the nearest two key point distance to the

nearer two key point distance is less than the

proportion of threshold then accept the matching

points.

3.2 Principle of Wavelet Transform

Wavelet transform is a major breakthrough of

Fourier transform and short time Fourier transform,

and it is successfully applied to image denoising,

edge detection and so on. Compared with the Fourier

transform, The signal can be local subdivided by

wavelet transform in the two dimensions of time and

frequency.

When we use wavelet analysis the image, we

should choose the appropriate basic wavelet or

wavelet, wavelet function is formed by a series of

basic wavelet translation, flexible operation, and

finally the signal will be projected onto the signal

space composed of translation and stretch wavelet

for analysis.

1) Wavelet definition

Set

)()(

RLx ∈

, and meet the conditions

00 =

∧

）（

then we called

)(x

as basic wavelet or wavelet, the

wavelet function is obtained by scaling and

translation:

)(

−

ϕϕ



0,, ≠∈ aRba



(7)

Where a is stretch factor, b is the translation factor.

The continuous wavelet transform of any

function

)()(

RLxf ∈

is defined as:



fbafW

Rba

)()(

,),)((

−

∫>==<

ϕϕ



(8)

The local change of the continuous wavelet

transform, the high frequency resolution, low

resolution in low frequency, which is more

conducive to the extraction of features in the image.

2) Image registration based on wavelet

transform

Wavelet analysis has multi-scale characteristics, it

provides a more flexible processing method for

image processing. In this paper, we use the discrete

Automatic Registration Method for Leather Section Image using SIFT and Wavelet Transform

wavelet transform, the image is decomposed into 2

layers, and the low frequency components of the 2

layer are used to match the image. Experiments

show that the low frequency component contains

most of the information of the image, through the

removal of high frequency components of noise,

improve the robustness of the matching.

DbN wavelet is the discrete orthogonal wavelet

dbN, where N is the order of the wavelet, the size of

the N reflects the smoothness and concentration of

the wavelet. Only N = 1 (Haar wavelet) has an

analytic expression. Haar wavelet as the wavelet

analysis of the most typical wavelet, the analytical

method of the expression as follows:



other

x 11/2

1/20

<≤

⎪

⎩

⎪

⎨

⎧

−=）（



(9)

The corresponding scaling function is:



other

1 <≤

⎩

⎨

⎧

x）（



(10)

DbN wavelet function and the effective support area

of the scale function is 2N-1, and the vanishing

moment of wavelet function is N, so its scalability is

better, which can effectively solve the boundary

problem.

The image processing algorithm based on

wavelet design as follows:

 Firstly, use multi-scale wavelet analyze and

deal with the image which have much noise,

we can get low frequency images by the 1

layer wavelet decomposition;

 Using the obtained image to carry out wavelet

threshold denoising operation, after that,carry

out the operation of 2 layer decomposition,

then we can obtain the two stage low

frequency image;

 Reconstruction of two stage low frequency

images and use it for registration;

4 EXPERIMENTAL RESULTS

The experiment done in matlab programming under

Windows 7 in this article, the experimental data is

image slices of the pigskin leather, on the condition

of leather image translation, rotation, and the change

of illumination to complete the operation of image

registration, the result as follows.

Table 1: Comparison of image registration.

Image

key point

count (1



image)

key point

count (2



image)

Matching

point

Correct

rate

Original

 image

1278 1663 5 80%

Wavele

tImage

1404 1763 7 100%

Image registration is affected by illumination,

position, etc. In this experiment, we use different

layers of images, there are differences in the position

and brightness, and the edge of the feature area is

not obvious. The matching results of the original

image are shown in Figure 1, Figure 2.

Figure 1: Original image a,b

Figure 2: The results of original image registration

The image matching results of the after 2 layers

of wavelet transform are shown in Figure 3, Figure 4.

ISME 2016 - Information Science and Management Engineering IV

ISME 2016 - International Conference on Information System and Management Engineering

Figure 3: The image after wavelet transform a, b

Figure 4: The results of the image after wavelet transform

Experimental results show that the original

image matching have error point, but using wavelet

to low-frequency image processing, it not only

makes the image edge response is more sensitive,

but also through the noise processing, reduce the

matching error, correct matching rate increased

significantly. Compared to the original image, the

image after wavelet transform can get more

matching point, use the method, iamge registration

can not only improve efficiency, and has better

robustness and higher accuracy.

5 CONCLUSION

This experiment use leather image in the article,

Experiment results show that the image after wavelet

image processing, the feature points matching rate of

SIFT algorithm increased significantly, the correct

rate is also improved, there is a better efficiency and

robustness.

Recently, as the rapidly developing of image

information, SIFT algorithm has made a great

contribution to image automatic mosaic and image

fusion, based on feature matching, we can move

forward apply SIFT algorithm to practical

application, as target recognition and image data, ect.

Provide essential conditions for image analysis and

other technology.

ACKNOWLEDGMENT

The research work is supported by A Project of

Natural Science Foundation of Shandong Province

(ZR2011FQ038), China, and the National Natural

Science Foundation of China (Grant No. 61502259),

and the Project of Shandong Province Higher

Educational Science and Technology Program

(J10LG20, J12LN09), China. Jinyong Cheng is the

corresponding author.

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Automatic Registration Method for Leather Section Image using SIFT and Wavelet Transform