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i-manager's Journal on Pattern Recognition (JPR)

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Volume 1 Issue 1 March - May 2014

Article

Image Encryption Algorithm Based on Arnold Cat Map and Logistic Mapping

Anil Saini* , Shalza Dua**

* PG Scholar, Department of Electronics and Communication Engineering, R.P. Inderaprastha Institute of Technology, Bastara Karnal

** Assistant Professor, Department of Electronics and Communication Engineering, R.P. Inderaprastha Institute of Technology, Bastara Karnal

Saini, A., and Dua, S. (2014). Image Encryption Algorithm Based On Arnold Cat Map And Logistic Mapping. i-manager’s Journal on Pattern Recognition, 1(1), 1- 4. https://doi.org/10.26634/jpr.1.1.2819

Abstract

The paper presents an article on image encryption scheme. The major approach to protect an image is encryption. There are many image encryption techniques. Image encryption schemes have been increasingly studied to meet the demand for real-time secure image transmission over the Internet and through wireless networks. Traditional image encryption algorithm such as data encryption standard (DES), has the weakness of low-level efficiency when the image is large. The chaos-based encryption has suggested a new and efficient way to deal with the intractable problem of fast and highly secure image encryption. One-dimensional chaotic system with the advantages of high-level efficiency and simplicity, such as Logistic map, has been widely used now developed. The encryption use the Arnold cat map to scramble the image. Arnold's cat map's feature is that image being apparently randomized by the transformation but returning to its original state after a number of steps. The overall objective of this paper is to propose an integrated technique for image encryption.

References

[1]. Yicong Zhou, Long Bao, and C. L. Philip Chen, (2011). ”Image Encryption Using a New Parametric Switching Chaotic System”. Elsevier Ltd.

[2]. Haojiang Gao, Yisheng Zhang, Shuyun Liang, and Dequn Li, (2005). ”A new chaotic algorithm for image encryption”. Elsevier Ltd.

[3]. Shiguo Lian, Jinsheng Sun, and Zhiquan Wang, (2005). ”Security Analysis of A Chaos-based Image Encryption Algorithm”. Phisica A, Elsevier Science.

[4]. Pia Singh, and Karamjeet Singh, (2013). “Image Encryption And Decryption Using Blowfish Algorithm In Matlab”. International Journal of Scientific & Engineering Research, Vol.4, No.7.

[5]. Varsha, S. Nemade, and R.B. Wagh, (2012). ”Review of different image encryption techniques”. Proceedings of National Conference on Emerging Trends in Computer Technology (NCETCT-2012).

[6]. Shubhangini, P. Nichat, and S.S. Sikchi, (2013). ”Image Encryption using Hybrid Genetic Algorithm”. International Journal of Advanced Research in Computer Science and Software Engineering, Vol.3, No.1, ISSN: 2277 128X.

[7]. Jolly Shah, and Vikas Saxena, (2011). ” Performance Study on Image Encryption Schemes”. IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1.

[8]. Ruisong Ye, and Wenping Yu, (2012). ”An Image Hiding Scheme Using 3D Sawtooth Map and Discrete Wavelet Transform” I.J. Image, Graphics and Signal Processing, 6, pp.52-60.

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Research Paper

Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions

J. Andrew Howe* , Hamparsum Bozdogan**

*-** Department of Statistics, Operations, and Management Science, Knoxville, Tennessee, USA

Howe, J. A., and Bozdogan, H. (2014). Flexible Generalized Mixture Model Cluster Analysis With Elliptically-Contoured Distributions. i-manager’s Journal on Pattern Recognition, 1(1), 5 - 22. https://doi.org/10.26634/jpr.1.1.2820

Abstract

The traditional mixture model assumes that, a dataset is composed of several clusters of Gaussian distributions. In real life, however, data often do not fit the restrictions of normality very well. It is likely that data from a single cluster exhibiting non- Gaussian shape characteristics could be erroneously modeled as multiple clusters, resulting in suboptimal inference and decision making. More flexibility is given to the mixture model by generalizing the Gaussian mixture model to use Elliptically-Contoured Distributions. While still symmetric, multivariate Elliptically-Contoured Distributions encompass a wide range of peak and tail characteristics. Distributions that can be generated as special cases include the Power Exponential, Gaussian, Laplace, Student’s T, Cauchy, and Uniform. This generalization makes mixture modeling more robust against nonnormality. Two computational algorithms, GARM and GEM,were adapted to optimize the Elliptically Contoured Mixture model and use results from robust estimation theory in order to data-adaptively regularize both. Finally, the Fisher information matrix and information criterion ICOMP are extended to score the new mixture model. These tools are used simultaneously to select the best mixture model and classify all observations without making any subjective decisions. The performance of the proposed mixture model is first demonstrated on a simulated dataset with extreme overlap. Secondly, the Elliptically-Contoured Mixture model is used on a medical dataset in which, the clinicallydetermined cluster structure are recovered For both datasets, the proposed mixture model substantially improves classification rates over the Gaussian mixture model.

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Research Paper

Disease Diagnosis by Means of Segmented tongue Image Classification Exhausting Neural Networks

N. Dhanaraj* , R. Sam Wesley, S. Santhiya, K. Sathya, G. Sivasathya

* Assistant Professor, Department of Electronics and Communication Engineering, Jay Shriram Group of Institutions, Tirupur.

--- Department of Electronics and Communication Engineering, Jay Shriram Group of Institutions, Tirupur

Dhanaraj, N., Wesley, R. S., Santhiya, S., Sathya, K., and Sivasathya, G. (2014). Disease Diagnosis by Means of Segmented Tongue Image Classification Exhausting Neural Networks. i-manager’s Journal on Pattern Recognition, 1(1), 23-29. https://doi.org/10.26634/jpr.1.1.2821

Abstract

The tongue has many relationships and acquaintances in the body, both to the peaks and the internal organs. It can present strong visual pointers of a person's overall agreement or disagreement. Tongue diagnosis has played such a prominent role in the diagnosis. The tongue is a muscular organ in the oral cavity that is associated with the function of deglutition, taste and speech. It acts as an easily accessible mirror of the health of a person, and indicates the state of hydration of the body. Some characteristic changes occur in the tongue in some particular diseases. In this paper, the tongue image of the patient is captured by digital camera and then the disease affected part of the tongue is segmented by threshold based algorithm which separate tongue from all other features like teeth, skin etc. The image will be classified based on the features extracted from the segmented part exhausting Neural Networks.

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Research Paper

Extended Rule Based Local Binary Pattern Technique for Texture Classification and Analysis

O. Rama Devi* , E. V. Prasad, L. S. S. Reddy*

* Assistant Professor, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada.

Director, Lakireddy Bali Reddy College of Engineering, Mylavaram.

* Pro.Vice Chancellor, K.L University, Vaddeswaram.

Devi, O. R., Prasad, E. V., and Reddy, L. S. S. (2014). Extended Rule Based Local Binary Pattern Technique For Texture Classification And Analysis. i-manager’s Journal on Pattern Recognition, 1(1), 30-35. https://doi.org/10.26634/jpr.1.1.2822

Abstract

Texture analysis is one of the important and most useful tasks in image processing applications. Several texture models have been developed over the past few years and Local Binary Patterns (LBP) are one of the simple and efficient approaches among them. The main disadvantage of “LBP” method is the complex computation of vector generation. Here an innovative classifier, called Extended Rule Based Local Binary Pattern (ERLBP) is given, which is an efficient model and its performance has been compared with other widely used texture models to show the computational superiority, robustness to gray scale variations and improved discriminating capability.

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Research Paper

Interactive Grow Cut Segmentation on CapsuleEndoscopy Image

S. Padmapriya* , P. Shanmugasundaram, N. Santhiyakumari*

* PG Scholar, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem, India.

Assistant Professor, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem, India.

* Professor and Head, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem, India.

Padmapriya, S., Sundaram, P S., and Kumari, N. S. (2014). Interactive Grow cut Segmentation on Capsule Endoscopy Image. i-manager’s Journal on Pattern Recognition, 1(1), 36-41. https://doi.org/10.26634/jpr.1.1.2823

Abstract

Wireless Capsule Endoscopy (WCE) is a transitional endoscopy, diagnosing disease in gross area of the Gastrointestinal (GI) tract ahead the reach of other endoscopy. Cancer is a leading cause of death. As per WHO report 13 million people were affected by cancer disease every year. Bowel cancer is a third most cancer occurring in the GI system. Recent works addresses various screening methods, and adaptive controls to improve the analysis completion. This paper describes a robust method of segmenting the bowel images and to discriminate the normal and affected location using Lab VIEW. The system shows, that the threshold adjusted (segregation) capsule endoscopic images are emphatic, and sophisticate classification by adequate software used to afford an image as clarion.

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[21]. Y. Y. Wang, Y. N. Sun, C.C.K. Lin, and M.S. Ju, (2006). ”Nerve Cell Segmentation via Multi-Scale Gradient Watershed Hierarchies”. Engineering in Medicine and Biology Society.

i-manager's Journal on Pattern Recognition (JPR)

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Volume 1 Issue 1 March - May 2014

Image Encryption Algorithm Based on Arnold Cat Map and Logistic Mapping

Anil Saini* , Shalza Dua**

* PG Scholar, Department of Electronics and Communication Engineering, R.P. Inderaprastha Institute of Technology, Bastara Karnal ** Assistant Professor, Department of Electronics and Communication Engineering, R.P. Inderaprastha Institute of Technology, Bastara Karnal

Saini, A., and Dua, S. (2014). Image Encryption Algorithm Based On Arnold Cat Map And Logistic Mapping. i-manager’s Journal on Pattern Recognition, 1(1), 1- 4. https://doi.org/10.26634/jpr.1.1.2819

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Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions

J. Andrew Howe* , Hamparsum Bozdogan**

*-** Department of Statistics, Operations, and Management Science, Knoxville, Tennessee, USA

Howe, J. A., and Bozdogan, H. (2014). Flexible Generalized Mixture Model Cluster Analysis With Elliptically-Contoured Distributions. i-manager’s Journal on Pattern Recognition, 1(1), 5 - 22. https://doi.org/10.26634/jpr.1.1.2820

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Disease Diagnosis by Means of Segmented tongue Image Classification Exhausting Neural Networks

N. Dhanaraj* , R. Sam Wesley**, S. Santhiya***, K. Sathya****, G. Sivasathya*****

* Assistant Professor, Department of Electronics and Communication Engineering, Jay Shriram Group of Institutions, Tirupur. **-***-****-***** Department of Electronics and Communication Engineering, Jay Shriram Group of Institutions, Tirupur

Dhanaraj, N., Wesley, R. S., Santhiya, S., Sathya, K., and Sivasathya, G. (2014). Disease Diagnosis by Means of Segmented Tongue Image Classification Exhausting Neural Networks. i-manager’s Journal on Pattern Recognition, 1(1), 23-29. https://doi.org/10.26634/jpr.1.1.2821

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Extended Rule Based Local Binary Pattern Technique for Texture Classification and Analysis

O. Rama Devi* , E. V. Prasad**, L. S. S. Reddy***

* Assistant Professor, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada. ** Director, Lakireddy Bali Reddy College of Engineering, Mylavaram. *** Pro.Vice Chancellor, K.L University, Vaddeswaram.

Devi, O. R., Prasad, E. V., and Reddy, L. S. S. (2014). Extended Rule Based Local Binary Pattern Technique For Texture Classification And Analysis. i-manager’s Journal on Pattern Recognition, 1(1), 30-35. https://doi.org/10.26634/jpr.1.1.2822

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Interactive Grow Cut Segmentation on CapsuleEndoscopy Image

S. Padmapriya* , P. Shanmugasundaram**, N. Santhiyakumari***

Padmapriya, S., Sundaram, P S., and Kumari, N. S. (2014). Interactive Grow cut Segmentation on Capsule Endoscopy Image. i-manager’s Journal on Pattern Recognition, 1(1), 36-41. https://doi.org/10.26634/jpr.1.1.2823

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* PG Scholar, Department of Electronics and Communication Engineering, R.P. Inderaprastha Institute of Technology, Bastara Karnal

** Assistant Professor, Department of Electronics and Communication Engineering, R.P. Inderaprastha Institute of Technology, Bastara Karnal

N. Dhanaraj* , R. Sam Wesley, S. Santhiya, K. Sathya, G. Sivasathya

* Assistant Professor, Department of Electronics and Communication Engineering, Jay Shriram Group of Institutions, Tirupur.

--- Department of Electronics and Communication Engineering, Jay Shriram Group of Institutions, Tirupur

O. Rama Devi* , E. V. Prasad, L. S. S. Reddy*

* Assistant Professor, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada.

Director, Lakireddy Bali Reddy College of Engineering, Mylavaram.

* Pro.Vice Chancellor, K.L University, Vaddeswaram.

S. Padmapriya* , P. Shanmugasundaram, N. Santhiyakumari*