June 2016

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International Journal of Innovative Technology & Creative Engineering Vol.6 No.6 June 2016

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From Editor's Desk Dear Researcher, Greetings!

Research article in this issue discusses about motivational factor analysis.

Let us review research around the world this month.

Childhood fears are common, normal behaviour, such as nail biting, bed-wetting and fearfulness, may actually represent a temporary phase in normal development. A most important finding [in a recent study] was that the fearful or anxious children, defined as those with seven or more worries, did not seem to be in any particular psychological trouble. Anxieties may be part of normal child development. Actually, there is reason to worry about anxious children.

Kids with anxiety disorders, depression or behavioural problems are especially likely to develop a range of difficulties as young adults, say researchers who conducted a long-term study published in 2015. The same goes for kids whose anxiety, mood or behaviour issues cause daily problems but don’t qualify as psychiatric ailments. Problems that later dogged the study’s troubled youngsters, who grew up in rural North Carolina, included drug addiction, teenage parenthood, dropping out of high school and criminal arrests.

Determining the boundaries of objects is one of the central problems in computer vision. It's something humans do with ease: We glance out the window and immediately see cars as distinct from the sidewalk and street and the people walking by, or lampposts as distinct from the facades of the buildings behind them. But duplicating that facility in silicon has proven remarkably difficult. One of the best ways for a computer to determine boundaries is to make lots of guesses and compare them; the boundaries that most of the guesses agree on are likeliest to be accurately drawn. Until now, that process has been monstrously time consuming.

It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia.

Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY.

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Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India.

Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India.

Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra.

(India).

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Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ.

Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India

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Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India.

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RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India.

MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran.

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Contents A Performance Comparison on Different Edge Detection Techniques for Iris Images Dr.R.Shanmugasundaram & Dr.S.Prasath …………….…………………………………….[360]

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A Performance Comparison on Different Edge Detection Techniques for Iris Images Dr.R.Shanmugasundaram Associate Professor, Department of Computer Science, Erode Arts and Science College (Autonomous), Erode, Tamil Nadu, India.

Email: rshanmuga.lec@gmail.com Dr.S.Prasath Assistant Professor, Department of Computer Science, Nandha Arts and Science College Erode, Tamil Nadu, India.

Email: softprasaths@gmail.com Abstract – Image Segmentation plays an important role in image processing and computer vision. Segmentation subdivides an image into its constituent regions or object.

Image segmentation can be obtained by using various methods, some which are easier to achieve than others due to the need of high programming. Edge detection techniques are mostly used to find object based on local changes in intensity field of image processing applications. In this paper attempt to study the most commonly used edge detection techniques such as canny, Sobel, Prewitt, Roberts, LoG. From the experiments and results observed the canny edge detection gives better results.

Keywords- PCA, ICA, LDA, DCT, LFA, LoG.

1. INTRODUCTION Since last decades, researchers are involved on iris recognition in image processing and they achieved so many mile stone because iris recognition is the critical stage to identify the iris in images due to pose, presence or absence of structural components, iris expression, occlusion, image orientation. Several methods have been evolved to increase recognition rate.

Many biometric recognition methods have been proposed.

Basically, they techniques can be classified into three categories [1]: Holistic or Global-Appearance-based methods [2], Local-feature-based methods or local appearance-based methods, and hybrid methods [3]. In Global-Appearance-based methods, the whole image is used as a raw input to the learning process. Examples of these techniques are Principal Component Analysis (PCA), Discrete Cosine Transforms (DCT), and Linear Discriminate Analysis (LDA). The local appearance-based methods can be divided into two groups: The ones that require the use of specific regions located on a iris such as eyes, nose and mouth, as well as their relationships with each other [4] and the ones that simply partition the input iris image into blocks without considering any specific regions [5]. In hybrid methods, associate feature of the holistic and local techniques. Modular Eigen iriss [6] Hybrid Local Feature Analysis (LFA) [7] and Component-based 3D Models [8] are examples of these methods. The most successful and wellstudied techniques to iris recognition are the appearance-based methods.

360

While capturing the image, due to motion or interference some disturbance, blur etc is added automatically in the image.

This is called noise. The features cannot be extracted correctly from such images. To improve the quality of image, enhancement techniques such as edge sharpening, noise removal etc are used. [9]. The output of image processing can be either an image or a set of characteristics or parameters related to image. The image processing techniques like image restoration, image enhancement, image segmentation. Also, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). Image segmentation is typically used to locate objects and boundaries (lines, curves, edges etc.) in images.

The segmentation process may include edge detection.

Sometimes edge detection is used for feature extraction.

2. EDGE DETECTION TECHNIQUES 2.1 Edge Models Edge models are classified according to their intensity profiles. A step edge involves a transition between two intensity level levels occurring ideally over the distant of 1 pixel. Edges that are blurred and noisy, determined by limitations in the focusing mechanism and noise level determined by the electronic components of imaging system.

In such situations, edges are more closely modeled as having an intensity ramp profile. The slope of the ramp is inversely proportional to the degree of blurring in the edge. It can be seen that in this model, there is no thin (1 pixel thick) path.

Instead, an edge point which is now any point contained in the ramp, and an edge segment would then be a set of such points that are connected. A third model of an edge is called roof edge. Roof edges are models of lines through a region, with the base of a roof edge being determined by the thickness and sharpness of the line [12].

2.2 Sobel Operator Sobel edge detection method was introduced by Irwin Sobel in 1970 (Rafael C. Gonzalez (2004)). The Sobel technique of edge detection for image segmentation finds edges using Sobel approximation derivative. Typically it is used to find the approximate absolute gradient magnitude at each point in an input grayscale image. The operator consist of a pair of 3x3 convolution mask, which is the x and y direction on the image. It is discovered at first derivative level. The

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.7 JUNE 2016, IMPACT FACTOR:0.61 horizontal and vertical pixel masks for Sobel Operator are shown in figure 1. This is very similar to Robert Cross operator.

Gx Gy -1

-2

-1

-1

0

+1

0

0

0

+1

+2

+1

-2 -1

0 0

+2 +1

Fig. 1 Sobel operator The mask will be moved until all the images and each value, R, will be kept into an output array, which is located at the mask centre. The formula to find the gradient magnitude is Equation (1).

R  Rx Ry

...... (equ.1)

+1 0

0 -1

0 -1

gx

+1 0

gy Fig.3 Robert Operator

2.5 Laplacian of Gaussian (LoG) This edge detector was invented by Marr and Hildreth (1980) who combined Gaussian filtering with the Laplacian.

Those who continued his way were Berzins (1984), Shah, Sood and Jain (1986), Huertas and Medioni (1986). The Laplacian is often applied to an image that has first been smoothed with something approximating a Gaussian Smoothing filter in order to reduce its sensitivity to noise. The operator normally takes a single gray level image as input and produces another gray level image as output.

Where Rx and Ry are given by using (equ.2) and (equ.3)

0

+1

-1

0

+1

Fig. 2 Prewitt operator 2.4 Robert Cross Edge Detection The Robert Cross operator performs a simple, quick to compute 2-D spatial gradient measurement on an image.

Pixel values at each point in the output represent the estimated absolute magnitude of the spatial gradient of the input image at that point. The operator consists of a pair 2x2 convolution masks as shown in figure 3. This is very similar to the Sobel operator. It uses {+1,–1} operator that will calculate the value p (x i ) - p(x j) ...... (equ.5)

For (i,j) pixel at neighborhood pixel. Mathematically, this equation is known as forward differences. p  p (x+1,y) - p(x,y) ...... (equ.6) x ( g . f )2  x

g

y

1

3164444

678

578

688

1394

1854

2

2685513

725

735

768

1485

1997

3

2761159

557

598

654

1229

1785

4

2438330

468

415

575

1045

1859

5

2209490

486

489

532

1245

1563

Table.3.1 Pixels Values of Edge Detected Image

Calculation for gradient magnitude is, G 

Canny

-1

LoG

The convolution masks of prewitt operator are shown in figure 2. Prewitt operator is similar to the Sobel operator and is used for detecting vertical and horizontal edges in images.

-1 0 +1

3. EXPERIMENTATION & RESULTS In this paper, the edge detection technique is used as a preprocessing technique where localization of iris is more important. With edge detection techniques, other algorithms can also be used such as Hough Transform, Integrodifferential operator etc to localize the iris. For our experiments, the sample iris print images from the standard iris image database are used. From the below Table.3.1 it is shows that pixels values of the original image and output of Sobel edge, Roberts edge, Prewitt edge, LoG edge and Canny edge. By considering all the pixels values and the results shows canny edge detected images high compared with other edges.

Robert

p p (x+1,y) - p(x-1,y)  ...... (equ.4) x 2

Prewitt

2.3 Prewitt Edge Detection Prewitt Operator is pioneered by Judy Prewitt and is based on the central difference concept and is given by,

Sobel

Where c is a constant with a value 2.

Original Image

Ry = (a0+ca1+a2) - (a6+ca5+a4) ...... (equ.3)

2.6 Canny Edge Detection The current standard edge detection scheme widely used around the world is the canny edge detector. John Canny did for his Masters degree in MIT in 1983. The canny edge detector first smoothes the image, to eliminate noise and then finds the image gradient to highlight regions with high spatial derivatives. The algorithm then tracks along these regions and suppresses any pixel that is not at the maximum.

Images

Rx = (a2+ca3+a4) - (a0+ca7+a6) ...... (equ.2)

. f ) 2 ...... (equ.7)

Convolution mask of Robert Cross Operator is shown in Figure 3.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.7 JUNE 2016, IMPACT FACTOR:0.61 4. CONCLUSION The edge detection is the initial step for recognition technique, it is important to show the differences between various edge detection techniques. In this paper we studied the most commonly used edge detection techniques such as Canny, Prewitt, Sobel and Roberts. The Prewitt operator has a major drawback that it is very sensitive to noise and it does not consider the high frequency variation. The Roberts operator works well only when the acquired image has very less noise and well defined edges. The Sobel operator is having smoothing capability, which smoothen all the edges. So it is difficult to find the edges correctly. Canny operator finds all the strong and weak edges those are connected to strong edges only. Therefore, edge features can be extracted easily and correctly with canny operator. Computationally Prewitt, Sobel and Roberts operators are simple whereas Canny is more expensive, even though it provides better results.

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5. Hazim K. and Rainer S.2005. Local Appearance based Iris Recognition Using Discrete Cosine Transform.

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6. Pentland A., MoghaddamB. and Starner T. 1994. ViewBased and Modular Eigen spaces for Iris Recognition.

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Network: Computation in Neural Systems, vol. 7, No. 3, pp. 477-500.

8. Bronstein A.M.,Bronstein M.M., Kimmel R. c 2003.

Expression invariant 3D iris recognition, in: International Conference on Audio and Video Based Person Authentication, LNCS, vol. 2688, pp. 62–70.

9. S. Jayaraman, S. Esakkirajan, T.Veerakumar, Digital Image Processing, Tata McGraw Hill publication, 2010.

10. Rajesh M. Bodade, Dr. Snajay N. Talbar, Lt. Shailendra K. Ojha, Iris Recognition using Rotational Complex Wavelet Filters: A Novel Apporach,978-1-4244-3397-1/08, 2008 IEEE. 11. Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Third Edition, Pearson Publication, 2011.

12. S. Sridhar, Digital Image Processing, Oxford University Press, 2011.

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