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Exploring Innovation| ISSN:2319-9598(Online)| Reg. No.:68563/BPL/CE/12| Published by BEIESP| Impact Factor:3.47
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Volume-3 Issue-6: Published on May 20, 2015
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Volume-3 Issue-6: Published on May 20, 2015
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Volume-3 Issue-6, May 2015, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Megha Sara Alex, Biju V. G

Paper Title:

An Improvement of Multi-Scale Fusion Technique for Image Dehazing

Abstract: The visibility of an outdoor imagery can be decayed by the hazy weather condition. In this paper, a modified multi-scale fusion technique is proposed to remove dense homogenous haze. The method first finds the airlight from the fused image. Then the patch-wise transmission function of the fused image is estimated by applying boundary constraints. After that a weighting function is applied on the patch-wise transmission to enhance the image structures. Using the patch-wise transmission and the weighting function, the transmission function is estimated. The dehazed image is hence recovered based on the correct estimation of both airlight and transmission function. The proposed method is then compared against the existing multi-scale fusion method by Qualitative and Quantitative evaluation. The results obtained show that the new method enhances the image visibility in dense hazy regions.

Keywords:
Outdoor imagery, Dehazing, Multi-Scale fusion, Airlight, Transmission function.


References:

1.        C.O Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” IEEE Trans. Image Processing, Aug.2013, vol.22, pp. 3271-3282.
2.        Gaofeng Meng, Ying Wang, Jiangyong Duan, Shiming Xiang and Chunhong Pan, "Efficient image dehazing with boundary constraint and contextual regularization." In Computer Vision (ICCV), 2013 IEEE Int.  Conf. on, pp. 617-624. IEEE, 2013.

3.        S. Narasimhan and S. Nayar, “Vision in bad wheather,” in Proc. IEEE Int. Conf. Comput. Vis., Sep. 1999, pp.820–827.

4.        Schechner, Y. Yoav, G. Srinivasa Narasimhan, and Shree K. Nayar. "Polarization-based vision through haze." In ACM SIGGRAPH ASIA 2008 courses, p. 71. ACM, 2008.

5.        Kopf, Johannes, Boris Neubert Billy Chen Michael Cohen Daniel Cohen-Or, Oliver Deussen, Matt Uyttendaele and Dani Lischinski. "Deep photo: Model-based photograph enhancement and viewing." In ACM Transactions on Graphics (TOG), vol. 27, no. 5, p. 116. ACM, 2008.

6.        Tan, T. Robby. "Visibility in bad weather from a single image." In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conf. on, pp. 1-8. IEEE, 2008.

7.        Fattal, Raanan. "Single image dehazing." In ACM Transactions on Graphics (TOG), vol. 27, no. 3, p. 72. ACM, 2008.

8.        Kaiming He, Jian Sun, and Xiaoou Tang. "Single image haze removal using dark channel prior," Pattern Analysis and Machine Intelligence, IEEE Trans. on 33, no. 12 (2011): 2341-2353.

9.        Finlayson, D. Graham, and Elisabetta Trezzi. "Shades of gray and colour constancy." In Color and Imaging Conference, vol. 2004, no. 1, pp. 37-41. Society for Imaging Science and Technology, 2004.

10.     Burt, J.  Peter, and E.  H. Adelson. "The Laplacian pyramid as a compact image code," Communications, IEEE Trans. on 31, no. 4 (1983): 532-540.

11.     J.-P. Tarel, N. Hautière, A. Cord, D. Gruyer and H. Halmaoui, "Improved Visibility of Road Scene Images under Heterogeneous Fog", in Proceedings of IEEE Intelligent Vehicles Symposium (IV’10), San Diego, CA, USA, June 21-24, 2010

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2.

Authors:

H. Lookman Sithic, R. Uma Rani

Paper Title:

A Grouping of Cancer in Human Health using Clustering Data Mining Technique

Abstract: Data mining is a collection of exploration techniques based on advanced analytical methods and tools for handling a large amount of information. The techniques can find novel patterns that may assist as enterprise in understanding the business better and in forecasting. Much research is being carried out in applying data mining to a variety of applications in healthcare [1].This article explores data mining techniques in healthcare management.  Particularly, it talk about data mining and its various application in areas where people are mostly affected rigorously by cancer in Erode District, Tamil Nadu, India. The people affected by cancer using tobacco, chemical water. This paper identifies the cancer level using clustering algorithms and finds meaningful hidden patterns which gives meaningful decision making to this socio-economic real world health venture.

Keywords:
Data Mining, Cancer, Clustering Algorithms.


References:

1.        Introduction to Data Mining with Case Studies – G.K.Gupta
2.        Langdon JD, Russel RC , Williams NS, Bulstrode CJK Arnold, Oral and Oropharyngeal cancer practice of surgery, London: Hodder Headline Group;2000.

3.        Werning, John W (may 16,2007). Oral cancer : Diagnosis, Management, and rehabilitation. P.1.ISBN 978 – 1588903099.

4.        crispian scully, Jose.V.Bagan, Colin Hopper, Joel.B.Epstien, “oral Cancer: Current and future diagnostics Techniques – A review article”, American journal of Dentistry, vol. 21,No.4,pp 199-209, Augest 2008.

5.        Arun K.Pujari, “Data mining Techniques”, University Press, First edition, fourteenth reprint,  2009.

6.        Peter Reutemann, Ian H. Witten,“The WEKA Data Mining Software: An Update ”,SIGKDD Explorations, Volume 11, issue 1  pages 10 to 18, 2005.

7.        Weka 3.6.4 data miner manual. 2010.

8.        P.Rajeswari, G.Sophia Reena, ”Analysis of Liver Disorder Using Data mining Algorithms”, Global Journal of computer science and Technology, Volume 1, issue 1, November 2010 page 48 to 52. ISSN:0975-4172


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3.

Authors:

Shival Abhilasha Shamsundar, Suryawanshi Rupali Vitthalrao

Paper Title:

Real Time Hand Gesture Recognition System

Abstract: Hand gestures can be used for natural and intuitive human-computer interaction. Our new method combines existing techniques of skin color based ROI segmentation and Viola-Jones Haar-like feature based object detection, to optimize hand gesture recognition for mouse operation. A mouse operation has two parts, movement of cursor and clicking using the right or left mouse button. In this paper, color is used as a robust feature to first define a Region of Interest (ROI). Then within this ROI, hand postures are detected by using Haar-like features and AdaBoost learning algorithm. The Ada Boost learning algorithm significantly speeds up the performance and constructs an accurate cascaded classifier by combining a sequence of weak classifiers.

Keywords:
Human Computer Interaction, Hand Detection, Segmentation, Hand Tracking and Gesture Recognition.


References:

1.        Conic, N., Cerseato, P., De Natale, F. G. B.,: Natural Human- Machine Interface using an Interactive Virtual Blackboard, In Proceeding of ICIP 2007, pp.181-184, (2007) pdf.
2.        R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,” In Proceedings of ICIP02, pp. 900-903, 2002.  

3.        Nguyen Dang Binh, Enokida Shuichi, Toshiaki Ejima, “Real-Time Hand Tracking and Gesture Recognition System”, 2005.

4.        “New Hand Gesture Recognition Method for Mouse Operations”, IEEE 2011

5.        R.C.Gonzales, R.E.Woods, Digital Image Processing. 2-nd Edition, Prentice Hall, 2002.

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