edge segmentation techniques for thai paphiopedilum images

Proceeding Book of Sakon Nakhon Rajabhat University International Conference

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Edge Segmentation Techniques for Thai Paphiopedilum Images

Wanchai Kosorn, Put Panuwanitchakorn, Janjira Payakpate*

Department of Computer Science and Information Technology, Faculty of Science, Naresuan

University, 99 Moo 9 Tambon ThaPho, Muang District, Phitsanulok , 65000,Thailand

*Corresponding Author: [email protected]

Received 7 August 2015; Revised 31 August 2015; Accepted 1 September 2015; Available online 1 October 2015

ABSTRACT

The purpose of this research was to identify the species of Thai Paphiopedilum, an orchid endemic

to Thailand, by using an edge segmentation technique to analyze photographic images of the

flowers. Suitable edge segmentation techniques were investigated and the most suitable identified.

There are five edge segmentation techniques: the Sobel Method, the Roberts Method, the Prewitt

Method, the Canny Method and the Laplacian of Gaussian Method. An image of each of the

fourteen species of orchid was selected and encoded as the four types of image extension: Joint

Photographic Experts Group (.JPEG), Portable Network Graphics (.PNG), Graphics Interchange

Format (.GIF) and Bitmap Image File (.BMP). Each of the fifty-six images was processed with each

edge segmentation techniques using MATHLAB R2008B. To compare the edge segmentation

techniques on each image, the root mean square error (RMSE) of each image result showed that the

RMSE of the Canny method on the .JPEG and .GIF images were less than that of the other methods

(5.24E+05, 4.37E+05). For the .PNG images, the Prewitt method was the most suitable method

with a RMS less than the other four methods. The Robert method was the most suitable for .BMP

images. Further study will be undertaken to implement a system of identification of these Thai

Paphiopedilum orchids from images.

Keywords: Edge Detection Technique, Thai Paphiopedilum

INTRODUCTION

The purpose of this study was to investigate

the use of edge segmentation techniques to

enable the identification of specific flower

species from photographs of the flower. An

orchid native to Thailand, Paphiopedilum,

was chosen for the investigation. There are 14

species of this native orchid which has a

flower with complex striping making it

difficult to identify by visual inspection alone.

Only an expert can identify the specific

species in this way.

Edge segmentation is the process of detecting

a line around objects in an image. The line

around an object can be used for calculating

the area of that object, its shape and other

identifying characteristics of the object.

However, accurately finding the edge of the

object is not easy, particularly in low

resolution images where there is little

difference between the foreground and the

background. The light intensity of the image

is also a factor in being able to find the edge

of the object. In Edge-based segmentation of

an image, the edge refers to the area where

the change of gray scale is significant. There

are various characteristics of change which

we can refer to as step edge or ramp edge

(Lee, 2015). Step edge is the edge which


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occurs from the gray scale value in the pixel

having a substantially higher value to that of

the next pixel. Ramp edge is the edge where

the change is gradual. Here, the Slope value is

inversely proportional to the blurring of the

border of the image itself.

Since the complexity and detail of orchids,

especially in this case the Paphiopedilum

orchid, makes identification quite difficult,

the idea pursued in this research is to offer a

convenient and easy way to achieve this. This

means providing a guide to the selection of

edge segmentation techniques which are best

suited to the purpose. In this paper, we

discuss detecting an edge by an edge

segmentation technique such as the Sobel

Method, the Prewitt Method, the Canny

Method, Laplacian of Gaussian Method or

Roberts Method to find the best and most

suitable method for each file type (.JPEG,

.PNG, .GIF, .BMP). The images of fourteen

species of Paphiopedilum were collected.

Then the five methods of edge segmentation

are applied. Finally, the analysis part was run

on MATLAB. Root Mean Square Error

(RMSE) is used for the comparison (Chai,

2014).

MATERIALS AND METHOD

Research on the performance analysis of the

Canny and the Sobel segmentation techniques

is shown in Fig. 1. Face recognition (Sattasit,

2006) is defined as the process of

automatically identifying and verifying a

person from a digital image of their face. Face recognition is one important application in

which edge segmentation plays a key role.

Computer based face recognition systems for

security applications are a widely researched

topic as facial features provide unique

biometric identity for users. Face recognition

systems are based on object recognition and

tracking technologies. One of the important

steps in object recognition is successful edge

identification and extraction.

Fig. 1 Face recognition (Rangarajan, 2015)

Another example, as shown in Fig. 2, is a

machine vision system for counting

corrugated cardboard (Siriamornrat, 2010).

This research presents a method for counting

the number of corrugated cardboard sheets

using a machine vision system and image

processing techniques.

Fig. 2 Machine vision system for counting

corrugated cardboard (Siriamornrat, 2010).

Edge Segmentation Techniques

Fig.3 illustrates an area of an image being

displayed as the binary value of the image pixels

(adapted from Saini, 2010).

Fig. 3 An area of the image being displayed as the

binary value of image pixel (adapted from Saini,

2010).


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Sobel Method

The Sobel edge segmentation operation

extracts all of the edges in an image,

regardless of direction. The Sobel has the

advantage of providing both a differencing

and smoothing effect. It is implemented as the

sum of two directional edge enhancement

operations. Both of them are shown in Fig. 4.

Gx = [−1 0 +1−2 0 +2−1 0 +1

]; Gy = [1 2 10 0 0

−1 −2 −1]

Fig. 4 Masks used for the Sobel segmentation

technique (Rangarajan, 2015)

The operator consists of a pair of 3×3

convolution kernels. One kernel is simply the

other rotated by 90°. The kernels can be

applied separately to the input image, to

produce separate measurements of the

gradient component in each orientation (Gx

and Gy) the gradient magnitude is given by:

Equations (1) and (2)

|G| = √Gx2 + Gy

2 (1)

Typically, an approximate magnitude is

computed using:

|𝐺| = |𝐺𝑥| + |𝐺𝑦| (2)

Which is much faster to compute.

Gradient calculation is shown in Fig. 5

(Rangarajan, 2015). After that, the threshold

is calculated (Fig. 6). Threshold is the

simplest method of image segmentation.

From a grayscale image, thresholding can be

used to create binary images. When the value

of Equation (2) in the image is less than 12,

the threshold of that pixel is equal to 0. On the

other hand, if the value of Equation (2) in the

image is more than 12, the threshold of that

pixel is equal to 1. Where the value of

Equation (2) from the image is equal to 12,

the threshold of that pixel depends on the

value of the surrounding pixels.

Fig. 5 Gradient Calculation (Karnjanadecha,

2007).

Fig. 6 Threshold Calculation (Saini, 2010).

Prewitt Method

The Prewitt method (Rangarajan, 2015)

detects two types of edges - vertical and

horizontal edges. Edges are calculated by

using the difference between the

corresponding pixel intensities of an image.

The masks are called derivative masks. Fig. 7

shows a two mask matrices.

Gx = [−1 0 +1−2 0 +2−1 0 +1

]; Gy = [1 2 10 0 0

−1 −2 −1]

Fig. 7 Masks used for the Prewitt segmentation

technique (Rangarajan, 2015).

Gx = [+1 0

0 −1] ; Gy = [

0 +1

−1 0]

Fig. 8 Masks used for the Robert segmentation

technique (Saini, 2010).

Roberts Method

The Roberts method (Saini, 2010) performs a

simple, quick to compute, 2-D spatial gradient

measurement of an image. Pixel values at

each point in the output represent the

Image |𝐺| = |𝐺𝑥| + |𝐺𝑦| Threshold =12


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estimated absolute magnitude of the spatial

gradient of the input image at that point. The

operator consists of a pair of 2×2 convolution

kernels as shown in Fig.8. Both kernels are

designed to respond maximally to edges

running at 45° to the pixel grid, one kernel for

each of the two perpendicular orientations.

The kernels can be applied separately to the

input image, to produce separate

measurements of the gradient component in

each orientation (Gx and Gy). These can then

be combined together to find the absolute

magnitude of the gradient at each point and

the orientation of that gradient. The gradient

magnitude is given by: Equations (3) and (4)

|G| = √Gx2 + Gy

2 (3)

Typically, an approximate magnitude is computed

using:

|𝐺| = |𝐺𝑥| + |𝐺𝑦| (4)

Which is much faster to compute.

Laplacian of Gaussian Method

The Laplacian is a 2-D isotropic measurement

of the 2nd spatial derivative of an image. The

Laplacian of an image highlights regions of

rapid change in intensity and is therefore

often used for edge detection. The Laplacian

is often applied to an image that has first been

smoothed. A Gaussian smoothing filter is

used in order to reduce the sensitive noise of

an image. The operator normally takes a

single gray level image as input and produces

another gray level image as output. Equation

(5) reveals the Laplacian L (x, y) (Shubham

Saini, 2010).

L(x, y) = 𝜕2𝐼

𝜕𝑥2 + 𝜕2𝐼

𝜕𝑦2 (5)

Since the input image is represented as a set

of discrete pixels, we have to find a discrete

convolution kernel that can approximate the

second derivatives in the definition of the

Laplacian. Three mask matrices are shown in

Fig. 9.

Gx = [0 1 01 −4 10 1 0

] ; Gy = [1 1 11 −8 11 1 1

]

Fig. 9 Masks used for the Laplacian of Gaussian

segmentation technique (Saini, 2010).

Because the measurement is calculated as a

second derivative, the measurement is an

approximation. So the image must first be

smoothed via the Gaussian Filter. This pre-

processing step reduces the high frequency

noise components prior to the differentiation

step. The 2-D LoG function with Gaussian

standard deviation is shown in Equation (6).

LoG(x, y) = −1

𝜋𝜎4[1 −

𝑥2+ 𝑦2

2𝜎2] 𝑒

− 𝑥2+ 𝑦2

2𝜎2 (6)

Canny Method

The Canny edge segmentation is totally

different from the previously described edge

segmentation techniques. It is an

approximation which allows optimization of

the edge-searching problem. The steps of

Canny (Saini, 2010) are shown in Fig. 10.

Fig. 10 Four Step calculation in the Canny

segmentation technique (Saini, 2010).


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Step 1: Smoothing with Gaussian.

The first step is to remove noise. Using the

Gaussian filter framework, noise will be

reduced, resulting in a smoother image.

Step 2: Gradient Calculation

Find the gradient of the image. This shows the

changes in intensity, which indicates the

presence of edges. This actually gives two

results, the gradient in the x direction and the

gradient in the y direction.

Step 3: Non-maxima Suppression

Fig. 11 illustrates non-maximal suppression.

Edges will occur at the point where the

gradient is at the maximum. The magnitude

and direction of the gradient are computed at

each pixel (Saini, 2010).

Fig. 11 Borders and directions of the gradient

(Saini, 2010).

Fig. 12 Graph shows the range of value High

threshold and Low thresholds. (Saini,

2010)

Step 4: Threshold

The threshold (Saini, 2010) value is classified

into the high threshold (T1) and the low

threshold (T2). The graph in Fig. 12 shows

the range of T1 and T2. If the pixel value is

greater than T1 the threshold value of that

pixel is 1 and if the pixel value is less than T2

the threshold value of that pixel is 0. Where

the pixel value is between T1 and T2 the

threshold value can be either 1 or 0 depending

on the surrounding pixels.

Fig. 13 Conceptual Framework.

Root Mean Square Error (Chai, 2014)

The Root Mean Square Error (RMSE) is

measuring the difference between predicted

values. It is used to indicate the accuracy of

the predicted value

n

XXRMSE

n

i idelmoiobs

1

2

,, )(

(7)

CONCEPTUAL FRAMEWORK

This research consisted of three steps. Fig. 14

shows the conceptual framework.

Step 1: Collect & Convert Images. Images of

the Paphiopedilum orchard were collected

from various sources. The images were

converted into gray scale with resolution of

640x480 pixels. The images of each species

were saved into files of the four different

graphical types (.JPG, .PNG, .GIF and .BMP)

Collect & Convert (Grayscale and size image 640 x 480 pixel)


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(a) is Gray scale. (b) is Sobel method. (c) is Canny

method. (d) is Prewitt method. (e) is Roberts

method. (f) is Laplacian of Gaussian method

Fig. 14 Paphiopedilum villosum image with

extension .JPEG and five segmentation algorithms

(a) is Gray scale. (b) is Sobel method. (c) is

Canny method. (d) is Prewitt method. (e) is

Roberts method. (f) is Laplacian of Gaussian

method


extension .PNG and five segmentation algorithms





extension .GIF and five segmentation algorithms.





extension .BMP and five segmentation algorithms

Step 2: Apply the edge segmentation

techniques (Sobel Method, Prewitt Method,

Roberts Method, Canny Method and

Laplacian of Gaussian Method) on the images

in all four graphics image types from Step 1

by using MATLAB.

Step 3: Compare the Root Mean Square Error.

RMSE

Scope of data

1. Images of Paphiopedilum native species in

Thailand.

2. One image each of fourteen species of

Paphiopedilum.

3. Files in the formats .JPEG .PNG .GIF

.BMP (640x480 pixel)

Scope of Comparison

The analysis was undertaken on MATLAB

R2008b, comparing the five edge detection

methods: Sobel Method, Prewitt Method,

Roberts Method, Canny Method and

Laplacian of Gaussian Method.


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RESULTS AND DISCUSSION

The results are shown in Fig. 14 – Fig.17. In

Fig. 14, Paphiopedilum villosum images with

.JPG extension were processed by the five

edge segmentation techniques. (a) is a gray

scale image. (b) is the result with the Sobel

method. (c) is the result with the Canny

method. (d) is the result with the Prewitt

method. (e) is the result with the Robert

method. (f) is the result with the Laplacian of

Gaussian. Other figures (Fig. 15 – Fig. 17),

are Paphiopedilum villosum images in the

order .PNG, .GIF and BMP.

The comparison of RMSE is shown in Table

1.

TABLE 1 Value of the Root Mean Square Error

Each of the extensions.

CONCLUSION

The comparison of Edge Segmentation

techniques on images of the Paphiopedilum

orchid was undertaken. The various edge

segmentation techniques investigated were

the Sobel method, Roberts method, Prewitt

method, Canny method and Laplacian of

Gaussian Method. Images of fourteen species

of Thai Paphiopedilum orchids were

collected. All of them were encoded and

converted into graphics format images; Joint

Photographic Experts Group - JPEG, Portable

Network Graphics -PNG, Graphics

Interchange Format -GIF and Bitmap Image

File – BMP. Each image was analysed by

each edge segmentation method via

MATLAB R2008B. The Root Mean Square

Error (RMSE) was calculated in order to

compare the results from each algorithm for

each species. The results show that the Canny

method is suitable for image segmentation in

the file extensions .JPEG and .GIF. In the

case of .PNG, the value of RMSE indicates

that the Prewitt method is appropriate for this

extension. The last one, .BMP, RMSE of the

Roberts method is the lowest.

REFERENCES

Chai, T. (2014). Root mean square error (RMSE)

or mean absolute error (MAE) –

Arguments against avoiding RMSE in the

literature. University of Maryland, USA

Karnjanadecha, M. (2007). Image Processing.

Prince of Songkla University, Songkla.

Kreawsuwan, J. (2006). Detecting and correcting

the orientation of the image by using a

support vector machine. B.A.Ed. thesis.

King Mongkut's Institute of Technology,

Bangkok.

Lee, T. (2015) Edge Detection Analysis. National

Taiwan University, Taiwan

Rangarajan, S. (2015), Algorithms for Edge

Detection. Retrieved from

http://www.ee.sunysb.edu/~cvl/ese558/s2

005/Reports/Srikanth%20Rangarajan/sub

mission.doc

Saini, S. (2010). Comparative study of image edge

detection algorithms. Vellore Institute of

Technology, India

Sattasit, A. (2006). The system detects speed limit

signs from video. M.S thesis, King

Mongkut's Institute of Technology,

Bangkok.

Siriamornrat, T. (2010). Sheet Counting Machine

Using Panoramic Vision System.

Retrieved from http://annualconference.ku.ac.th/cd53/08_

04 8_O318.pdf

Vijayarani, S. (2013). Performance Analysis of

Canny and Sobel Edge Detection Algorithms

in Image Mining. Bharathiar University, India

Name

type Sobel Canny Prewitt Roberts Log

.JPEG 0.165𝑥10−3 0.524𝑥10−4 0.165𝑥10−3 0.152𝑥10−3 0.906𝑥10−4

.PNG 0.164𝑥10−3 0.120𝑥10−3 0.184𝑥10−5 0.234𝑥10−4 0.819𝑥10−4

.GIF 0.159𝑥10−3 0.436𝑥10−4 0.159𝑥10−3 0.148𝑥10−3 0.848𝑥10−4

.BMP 0.165𝑥10−3 0.538𝑥10−4 0.165𝑥10−3 0.240𝑥10−4 0.753𝑥10−4

edge segmentation techniques for thai paphiopedilum images

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