Journal of image processing & pattern recognition progress (vol1, issue2) by STM Journals Publication

Journal of

Image Processing &

Pattern Recognition Progress May-August 2014

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Dr. Abhishek Das Assistant Professor Dept. of Information Technology, Tripura University (A Central University) Suryamaninagar, Agratala.

Dr. Rameswar Debnath Head Computer Science and Engineering Discipline. Khulna University, Khulna 9208, Bangladesh.

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Dr. Bijan Karimi Professor Electrical & Computer Engineering and Computer Science Tagliatela College of Engineering, India.

Dr. Hari Om Assistant Professor Department of Computer Science & Engineering Indian School of Mines Dhanbad826 004 Jharkhand India.

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Director's Desk

STM JOURNALS

I take the privilege to present the print version for the [Volume 1 Issue (2)] of Journal of Image Processing & Pattern Recognition Progress. The intension of JoIPPRP is to create an atmosphere that stimulates creativeness, research and growth in the area of image processing. The development and growth of the mankind is the consequence of brilliant Research done by eminent Scientists and Engineers in every field. JoIPPRP provides an outlet for Research findings and reviews in areas of image processing found to be relevant for National and International recent developments & research initiative. The aim and scope of the Journal is to provide an academic medium and an important reference for the advancement and dissemination of Research results that support high level learning, teaching and research in the domain of image processing. Finally, I express my sincere gratitude and thanks to our Editorial/ Reviewer board and Authors for their continued support and invaluable contributions and suggestions in the form of authoring write ups/ reviewing and providing constructive comments for the advancement of the journals. With regards to their due continuous support and co-operation, we have been able to publish quality Research/Reviews findings for our customers base. I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.

Dr. Archana Mehrotra Director STM Journals

Journal of Image Processing & Pattern Recognition Progress

Contents

1. Data Embedding Technique for Palette Scale Image using Derived Genetic Algorithm (DEP-DGA) P.M. Siva Raja, E. Baburaj

2. A Machine Vision Approach for Grading of Onions B. L. Benisha Bennet

3. Improved Global Region based Chan-Vese Model Nitesh L. Rudani, Prof. Rahul G. Patel

4. Multi-Modal (Hybrid) More Efficient and Highly Secured Enhanced Hand Geometry Authentication using 3 Steps Authentication by Means of Biometrics, Steganography and Encryption Nader A. Rahman Mohamed 22 5. Performance Evaluation of Segmentation Algorithms for Apple Fruit Grading Aarti Abasaheb Sawant, V. N. Kshirsagar

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 2 www.stmjournals.com

Data Embedding Technique for Palette Scale Image using Derived Genetic Algorithm (DEP-DGA) P.M. Siva Raja1*, E. Baburaj2 1

Sathyabama University, Chennai, Tamil Nadu, India Department of Computer Science and Engineering, Sun College of Engineering and Technology, Nagercoil

Abstract Steganography method is used for exchanging the hidden information. It is defined as the study of hidden communication that deals with the existence of communicated messages. The existing methodology for minimizing additive distortion in steganography fails in measuring the level of distortion for the given source palette digital image. Perfect sphere shaped palette digital images are not suitable for selection, as cover models because the hidden data might be easily observed even after specific modification by embedding method. To shape constraints in palette digital images and to observe the distortion, data embedding technique using derived genetic algorithm for palette scale image has been proposed. Row major order is used to embed huge quantity of stego message on source palette digital image. Four bits of data hiding message is embedded per byte of the source palette digital image onto the rightmost 4 bit of each pixel. Mutation is applied on the embedded palette digital image. In the process of embedding, dimension of the data hiding message is followed by the content of the original palette based digital image. Reverse procedure is followed during decoding. Derived Genetic algorithm is used to enhance the security level without any extraction of data by attackers. Several statistical parameters computed are compared with the existing additive distortion syndrome trellis code method using Statlog Dataset from UCI repository. DEP-DGA approach obtained better results in terms of security level of hidden information, 3.50 % improved throughput on using the palette based digital image for embedding and minimize the RMS error between the stego message and the palette based original image.

Keywords: Data embedding technique, derived genetic algorithm, root mean square (RMS), data hiding, palette based original image, security, stego message

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 2 www.stmjournals.com

A Machine Vision Approach for Grading of Onions B. L. Benisha Bennet* Department of ECE, Velammal College of Engineering and Technology (Affiliated to Anna Univ., Chennai), Madurai, India

Abstract Grading of fruits and vegetables (on the basis of various external and internal quality attributes) is an important unit operation in the food industry. For products such as onions, grading is performed mainly on the basis of size. In the present study, a machine vision system was used to grade onions into three categories (extra-large, medium and small) based on variations in size. The machine vision system consisted of a CCD camera, illumination source, holding unit, sample platform, and computer. The geometric mean diameter of the sample was used as the classification parameter. It was observed that the system gave an overall classification efficiency of 93% with better results for extra-large grade. Similar approaches can be successfully implemented for classification of other agricultural products using various grading parameters.

Keywords: CCD camera, size sorting, machine vision, image processing, grading

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 2 www.stmjournals.com

Improved Global Region based Chan-Vese Model Nitesh L. Rudani*,Prof. Rahul G. Patel Sankalchand Patel College of Engineering, Visnagar, India Abstract Chan-Vese model is one of the fundamental active contour models for image segmentation. In this paper, the authors propose an improved global region based ChanVese model which provides solution to some limitations of basic Chan-Vese model. The proposed model avoids re-initialization requirement to increase speed of algorithm by reaction-diffusion method. The stopping criterion to stop segmentation process is also added in the model. To make model automatic, initialization free feature is also added via Poissonâ&#x20AC;&#x2122;s inverse gradient technique. Experimental results on different images justify the improvement in the basic Chan-Vese model.

Keywords: Chan-Vese, active contour without edges model, initialization free, re-initialization free, stopping criteria

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 2 www.stmjournals.com

Multi-Modal (Hybrid) More Efficient and Highly Secured Enhanced Hand Geometry Authentication using 3 Steps Authentication by Means of Biometrics, Steganography and Encryption Nader A. Rahman Mohamed* Department of Biomedical Engineering, Faculty of Engineering, Misr University for Science and Technology (MUST), Egypt

Abstract “Biometrics” is the science, which is used to verify the identity of the persons through either behavioral traits or physical characteristics. This area has gained great importance in maintaining the security of the places that require high security accuracy. Hand geometry is considered one of the biometrics, which derived its reputation from the ease of use and the acceptance of many people to use it. Hand geometry based biometric systems are gaining acceptance in low to medium security applications. Hand biometrics is extensively used for personal authentication. The widespread dissemination of verification systems using biometrics and continuous attempts to break the security of these systems, such as the use of fingerprints rubber to break the security systems that use fingerprint, or use a voice recorder to break the security systems that use voice tag, and so on. Although the efficiency of biometrics in identification of people is very accurate and highly secured, but still the data concerning it is not a secret, and if it is compromised, it would compromise the integrity of the system where protection is required. Protecting biometric data has become an important issue, so that it cannot be misused by attackers. In order to increase security of biometric data there are different methods in which digital steganography could be widely accepted. Steganography is defined as the science of hiding or embedding information in a transmission medium in a way to be undetectable by observers. To address these issues, this paper proposes a novel encryption method with password protection based on an enhanced version of multimodal hand geometry authentication using three authentication layers based on hand geometry verification, digital steganography, and password encryption.

Keywords: Biometrics, hand geometry authentication, digital steganography

Journal of Image Processing & Pattern Recognition Progress Volume 1, Issue 2 www.stmjournals.com

Performance Evaluation of Segmentation Algorithms for Apple Fruit Grading Aarti Abasaheb Sawant*, V. N. Kshirsagar RMD Sinhgad School of Engineering, Pune University, India Abstract For supplying high-quality food products within a short time, automated grading of fruits is getting special attention. Fruit grading technique will grade the fruits using their shapes, color and outer look. This emphasizes the necessity of image segmentation which extracts important features from an image. This paper consists of analysis of different segmentation algorithms for detecting damage part of fruit. Marker-based watershed algorithm uses internal and external markers to locate catchment basins which are regions of interest. Quadtree and merge algorithm divides an image within a complete tree representation and then merges the regions based on some criteria. Seed region growing algorithm selects the seed point and grows the region according to pre-defined criteria. The comparison of the present approach in terms of the measures like energy, discrete entropy, relative entropy, mutual information, normalized mutual information and redundancy is also carried out.

Keywords: Fruit grading, image segmentation, marker based watershed algorithm, quadtree and merge algorithm, seed region growing algorithm