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International Conference on Digital Signal & Image Processing(ICoDSIP), 7-8 March 2017
International Conference on Digital Signal & Image Processing(ICoDSIP), 7-8 March 2017

International Conference on Digital Signal & Image Processing (ICDSIP)-2017
Date of Conference: March 07-08, 2017 | Organised by Maharashtra Institute of Technology, Aurangabad (Maharashtra), India

S. No

ISSN: 2249 – 8958, Volume-6, Issue-ICDSIP17, March 2017
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.



Mahendra Gupta, Roshan Varghese, Thomson Johnson, Abhimanyu Gupta, Nilashree W

Paper Title:

Remote Controlled Quadcopter for Image Capturing

Abstract: In this paper, we describe system analysis and implementation of a quadcopter controlled by a RC remote. The motion of the quadcopter is controlled based on   measurements of inertial sensors by controlling the speed of the quadcopter using the flight controller from the accurate values obtained from the IMU (Inertial Measurement Unit). We focus on developing a cost effective system capable of stable flight. Next a camera is coupled with the quadcopter to capture images during the flight. The images are processed by resolution improvement, noise removal and by deblurring techniques.

 Thrust, Pitch, Roll, Yaw, Throttle, Gyroscope,   Quadcopter


1.    Samir and Roland,”full control of a quadcopter’, 2007, IEEE conference on Intelligent Robots and Systems,San Diego.
2.    Jakob Engel, Jurgen, Daniel,”Camera based navigatin of a lowcost quadcopter”, 2012, International conference on intelligent robot and systems, Portugal.

3.    Tommaso Bresciani,” Modelling, identification and control of quadrotor.

4.    D.Bacon,” Instruction Mannual”.

5.    Markus, Tianguang, Kolja, Martin,”Visual tracking and control of quadcopter using stereo camera system and inertial sensor’,2009,IEEE international conference on Mechatronics and automation,Changchun China.


7.    Wikipedia: ZYZ Euler’s form, Newton-Euler equations.

8.    Modelling and control of quadcopter by Teppo Luukkonen.

9.    G.M.Hoffman, H.Huang and C.J.Tomlin,”Quadcopter flight dynamics and control”, 2007, Navigation and international conference.




Jadhav Suhas Sayajirao

Paper Title:

Wireless Electricity or Energy Transmission

Abstract:  Power is very important to consumer product, generally the power is transmitted through wires. By using some techniques we can able to deliver power without wires or transmission lines also we can minimize energy consumption and wire resistances. Wireless power transmission is the transmission of electrical energy without using any conductor wire or transmission lines. Wireless power transfer system use basics of magnetic or inductive resonant coupling known as resonant energy transfer. The coupling consists of an inductor along with a capacitor with its own resonating frequency. Another technique includes transfer of power through microwaves using reactances. This is particularly suitable for long range distances ranging kilometers. Within next few years need of wires for energy transfer will outdated.

 Wireless power, Wireless energy, Inductive resonant coupling, Power or energy transmission, Wireless Electricity Transmission


1.       Benson, Thomas W., "Wireless Transmission of Power now Possible”
2.       U.S. Patent 787, 412, "Art of Transmitting Electrical Energy through the Natural Mediums".

3.       IEEE Power Systems Relaying Committee (PSRC). (1999).IEEE Guide for Protective Relay Applications to Transmission Lines, IEEE Std. C37.113-, pp.31

4.       H. Khorashadi-Zadeh, M. Sanaye-Pasand. (2006). Correction of saturated current transformers secondary current using ANNs, IEEE Trans. Power Delivery, 21, 1, pp. 73–79.

5.       Zia A. Yamayee and Juan L. Bala, Jr., “Electromechanical Energy Devices and Power Systems”, John Wiley and Sons, 1947, p. 78

6.       Simon Ramo, John R. Whinnery and Theodore Van Duzer, “Fields and Waves in Communication Electronics”, John Wiley & Sons, Inc.; 3rd edition (February 9, 1994)

7.       Tomohiro Yamada, Hirotaka Sugawara, Kenichi Okada, Kazuya Masu, and Akio Oki, "Battery-less Wireless Communication System through Human Body for invivo Healthcare Chip,"IEEE  Topical Meeting on Silicon Monolithic Integrated Circuits in RF Systems”

8.       Kurs, A. Karalis, R. Moffatt, J. D. Joannopoulos, P. Fisher, M. Soijacic, “Wireless Power Transfer via Strongly Coupled Magnetic Resonances”, Massachusetts Institute of Technology, 2007 Science, Vol. 317. no. 5834, pp. 83— 86, 2007.

9.       Tesla’s Tower of Power. Information available at the following link,

10.    A.C.M de Queiroz, “The Triple Resonance Network with Sinusoidal Excitation”, EE/COPPE, Universidade Federal do Rio de Janerio, Brazil.

11.    Benjamin L. Cannon, James F. Hoburg, Dabiel D. Stancill, Seth Copen Goldstein, Magnetic Resonant Coupling as a Potential Means for Wireless Power Transfer to Multiple Small Receivers, IEEE Transactions on Power Electronics, Vol. 24, No.7, July 2009.

12.    Information available at Wikipedia at the following link,




Shaikh Mudassar Akhtar, Pandit Anand Purushottam, Khirade Prakash Waghji, Sayyad Shafiyoddin Badruddin

Paper Title:

Support Vector Machine (SVM) Based Supervised Classification Analysis for Microwave SIR-C POL SAR Image

Abstract: The classification of microwave Polarimetric Synthetic Aperture Radar (PolSAR) image analysis has become a very important task after the availability of data from satellite by using different existing technique classification techniques. In the last decade, classification of PolSAR imagery has been deliberate using several different approaches. This paper presents a supervised classification method applied on microwave L-band SIR-C SAR data of Kolkata, West Bengal, India image. Here initially for the preprocessing speckle filter and decomposition technique is applied to original selected SAR image and later the image is classified using supervised classification techniques. In this paper Support Vector Machine (SVM) classification technique is used as a supervised classification. The present paper work using confusion matrix the error like omission and commission and the accuracy was assessed for the selected dataset. The accuracy for SVM classified image is improved up to 99.58% and hence it is observed that among the supervised classifiers SVM classification technique is one of the best technique for classification.

  Classification, PolSAR, SIR-C, Support Vector Machine (SVM).


1.       Jensen R. J. 2014. Remote sensing of the environment an earth resource perspective. (2nd ed.) Pearson.
2.       M. A. Shaikh, P. W. Khirade, S. B. Sayyad. 2016. Classification of Polarimetric SAR (PolSAR) image analysis using decomposition techniques. International Journal of Computer Application (IJCA) Proceedings on National Conference on Digital Image & Signal Processing (NCDISP 2016).  1, 20-23.

3.       Chi, M., Feng, R., Bruzzone, L. 2008. Classification of hyperspectral remote sensing data with primal SVM for small-sized training dataset problem. Advances in Space Research. 41, (11), 1793–1799.

4.       M. A. Shaikh, P. W. Khirade, S. B. Sayyad. 2016, July. Unsupervised and supervised classification of PolSAR image using decomposition techniques: an analysis from L- band SIR-C data. Asian Journal of Multidisciplinary Studies (AJMS). 4, (8), 140-144.

5.       V.N. Vapnik. 1998. Statistical learning theory. John Wiley and Sons.

6.       G. Mercier, M. Lennon. 2003. Support vector machines for hyperspectral image classification with spectral-based kernels. In Proceedings of IGARSS, Toulouse, France. 1, 288-290.

7.       M. Chi, R. Feng, L. Bruzzone. 2008. Classification of hyperspectral remote-sensing data with primal SVMS for small-sized training data. Advances in Space Research. 41, 1793-1799.

8.       C. Lardeux, P.L. Frison, J.P. Rudant, J.C. Souyris, C. Tison, B. Stoll. 2006. Use of the SVM classification with polarimetric SAR data for land use cartography. In IGARSS 2006, Denver, Colorado.

9.       J.S. Lee, M.R. Grunes, E. Pottier, L. Ferro-Famil. 2004. Unsupervised terrain classification preserving polarimetric scattering characteristics. IEEE Transactions on Geoscience and Remote Sensing. 42, (4), 722-731.

10.    S. Fukuda, H. Hirosawa. 2001. Polarimetric SAR image classification using support vector machines. In IEICE Transactions on Electronics, E84-C, 12, 1939-1945.

11.    R. Shah, R. Hosseini, S. Homayouni. 2009. A SVMS-based hyperspectral data classification algorithm in a similarity space. In 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS2009), Grenoble, France.

12.    M. Ouarzeddine, B. Souissi. Unsupervised classification using wishart classifier. USTHB, F.E.I, BP No 32 EI Alia Bab Ezzouar, Alger.

13.    R. Touzi, W.M. Boerner, J.S. Lee, E. Lueneburg. 2004. A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction. Can. Journal. Remote Sensing. 30, (3), 380–407.

14.    Y. Dong, B. Forester, Ticehurst. (1997). Radar backscatter analysis for urban environment. International Journal of Remote Sensing. 18, (6), 1352-1364.

15.    J.S. Lee, M.R. Grunes, T.L. Ainsworth, L.J. Du, D.L. Schuler, S.R. Cloude. 1999. Unsupervised classification using polarimetric decomposition and complex wishart distribution. IEEE Transactions Geoscience and Remote Sensing. 37/1, (5), 2249-2259.

16.    Lillesand T. M., Kiefer R.W. (1999). Remote Sensing and Image Interpretation. (4th ed.). John Wiley & Sons, Inc.

17.    V. Vapnik. 1979. Estimation of Dependences Based on Empirical Data. Nauka, Moscow, 27, 5165–5184.
18.    G. Zhu, D.G. Blumberg. 2002. Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel. Remote Sensing of Environment. 80 (2), 233–240.
19.    P. Mantero, G. Moser, S.B. Serpico, 2005. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing. 43 (3), 559–570.

20.    L- Band SIR-C dataset download from




G.V. Avhale, A.D. Shaligram, D.C. Gharpure

Paper Title:

Design and Development of Internet of Things (IoT) based Data Acquisition System for Milk Quality Monitoring in the Dairy Industry

Abstract: The dairy industry is of crucial importance to India as 13% of the world’s total milk production. In the field of dairy industry in India, there is a need to acquire the latest technologies in order to improve the quality of milk. The Dairy industry must have a reliable Data Acquisition System for monitoring the milk quality at various stages in the dairy industry. Nowadays there is a necessity to do the long distance monitoring or monitoring from anywhere at any time. The internet is available worldwide, hence the first choice is the technology which monitors different parameters based on the internet called internet of things (IoT) technology. This paper presents an Internet of things (IoT) based data acquisition system for monitoring the quality of milk in the dairy industry. The system used an Arduino Uno board as processor and GSM SIM 900 module for internet connectivity. The data monitoring is possible by Thingspeak open IoT platform and can be accessed through any device e.g. Computer, laptop, or smart mobile phone which has internet connectivity.

  Dairy industry, Data acquisition system, end to end monitoring, Internet of Things (IoT). it is the world’s largest milk producer, accounting for more than


1.       M. F.AL. Faisal, S Bakar, PS Rudati, “The Development of A Data Acqusition System Based on Internet of Things Framework”, ICT for Smart Society (ICISS), 2014 International Conference on, 24-25 Sept. 2014.
2.       Jie ZHANG, Aicheng LI, Jianlong LI, Qi YANG, Chengcheng GANG, “Research of Real-time Image Acquisition System Based on ARM 7 for Agricultural Environmental Monitoring”, 978-1-4244-9171-1/11/2011 IEEE, pp. 6216 - 6220.

3.       Tengfei Xing, “IoT Identifier Service based-Public Food Quality Safety Traceability Platform and its Social Impacts”, Proc. of World Symposium on Computer Networks and Information Security, DOI: 02. WSCNIS.2014.1.15, pp. 93 - 98.

4.       M. S. Patil, Dr. M. A. Shinde, “SWOT Analysis: Problems and Prospects of Dairy Industry in India”, INDIAN JOURNAL OF APPLIED RESEARCH, Volume: 6, Issue: 3, March 2016, ISSN - 2249-555X, IF: 3.919, pp. 494 - 497.

5.       Feng Tian, “A Quality and Safety Control System for China's Dairy Supply Chain Based on HACCP & GS1”, 978-1-5090-2842-9/16/Q016 IE.

6.       Gh. R. Jahed Khaniki, “Chemical contaminants in milk and public health concerns: A Review”, International journal of dairy science 2 (2): pp. 104-115, 2007. ISSN

7.       Article on “Milk quality incentives” – John H. kirk.

8.       Wu Li, Guofang Nan, Ting Yang, “Service-oriented Sensor Data Query System for Monitoring Milk Storage, Production and Delivery”, 978-1-4244-2972-1/08/ 2008 IEEE.

9.       Jun Jiao, Huimin Ma, Yan Qiao, Yulin Du, Wen Kong and Zhongcheng Wu,  “Design of Farm Environmental Monitoring System Based on the Internet of Things”, Advance Journal of Food Science and Technology 6 (3), 2014 ISSN: 2042-4868; e-ISSN: 2042-4876, pp. 368 - 373.

10.  //used for Arduino Uno  board information

11.    Chao-Hsi Huang, Pin-Yin Shen, Yueh-Cheng Huang, “IoT-Based Physiological and Environmental Monitoring System in Animal Shelter”, ICUFN 2015, 978-1-4799-8993-5/15/ 2015 IEEE, pp. 317-322.

12.  //used for SIM 900 module information

13.  //used for information about TCPIP AT commands for SIM 900 module

14.   //used for information about ThingSpeak IoT platform.




Chaitanya Ahire, Pramod Ambhore

Paper Title:

Noise Reduction Techniques for Medical Images using Transform Domain Approach

Abstract: Medical images suffers from noise introduced during its capturing process. There are two approaches to remove this noise .i. e Transform domain and spatial domain .Spatial domain techniques leads to loss of details or important information. In this paper we have proposed a transform based denoising algorithms. We have used joint approach to denoise the medical images which uses Dual tree DWT and Rotated version of Dual Tree DWT to improve denoising results. We are mainly focusing on the mixed diagonal information which we are able to separate by using the concept of rotated filters. Results which we have obtained with the joint approach are very exciting.

  Medical images, noise, Dual tree DWT, Rotated Dual Tree DWT, PSNR, SSIM, Rotated Wavelet Filters


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Dipak Shelar, Arvind Shaligram, Damayanti Gharpure

Paper Title:

The Design of Low Power Wireless Sensor node for Food grain Warehouse Monitoring

Abstract:  Grain storage is an important issue related to the National economy and people’s livelihood, and all are valued through the ages. Pest free storage is needed for handling crops at harvest time and to carry over reserve from year to year. Considerable losses both in quality and quantity of food grains take place due to a several factors. Organisms directly responsible for causing loss in store food grains are insects, mites, rodents, fungi and bacteria. Grain temperature and moisture content are the primary factors that affect the grain storage. As temperature and moisture content increase, grain will deteriorate faster. Therefore, it necessary to monitor the physical parameters such as temperature and humidity inside food grain storages in time and take effective measures. In this paper, we have designed low power and high performance wireless sensor node using MSP430G2553 microcontroller, XBee series 1 transceiver and DHT11 temperature and Humidity sensor. Temperature and relative humidity were measured and transmitted wirelessly. We have measured and analyzed the current consumption of sensor node in active mode. The experimental result shows that the developed sensor node has good low power characteristics.

  MSP430, Wireless Sensor Networks, lifetime, Current Consumption, sensor node


1.       Xuan He, QunHao and Longhai Zhao, “The Design of Low-power Wireless Sensor Node”, 2010 IEEE Instrumentation & Measurement Technology Conference.
2.       He Jianfeng, Qu Jinhui,Wang Yuan, Pan Hengya, “The Designing and Porting of Temperature &Humidity Sensor Node Driver Based”, 2014 IEEE Workshop on Electronics, Computer and Applications with ARM-Linux.

3.       LiqiangZhengy, Jingxuan Li, Michael Hayes, Brendan O'Flynn, “Design of a Low Power Wireless Sensor Node for Environmental Monitoring”, ISSC 2011.

4.       Robert Faludi, “Building Wireless Sensor Networks”, Published by O’Reilly Media.

5.       Datasheet of MSP430G2x53 microcontroller,Avilable:

6.       Datasheet of XBee,Avilable:

7.       Datasheet of Osong DHT11 Sensor,Avilable:




S. N. Patil, A. M. Pawar, B. P. Ladgaonkar

Paper Title:

Synthesis and Deployment of Nanoferrites to Design Embedded System for Monitoring of Ammonia Gas

Abstract: Keeping pace with facets of nanotechnology and its applications in the field of development of sensors of promising features, nanoparticle spinel Manganese-Zinc ferrites have been synthesized by co-precipitation method. The formation of the materials is confirmed by characterization of the same with X-ray powder diffraction, FTIR absorption and SEM as well. These materials were used for development of the sensor for monitoring of ammonia gas. The detection of ammonia gas is essential in various industries and for environmental prediction as well. The sensing elements of the sensors are developed using thick film technology and ammonia gas sensitive electrical properties are investigated. An operating temperature was optimized to 125 0C. Ammonia gas is reducing gas and hence it plays remarkable role on the electrical properties. The electrical resistance R, measured against in concentration of ammonia, shows decreasing trend with increase in concentration of ammonia. From sensitivity data, it is confirmed that, the materials are mostly suitable for sensing of ammonia gas. Employing embedded technology and using thus prepared sensor, a sensor module is designed about AVR ATmega 8L microcontroller and deployed for monitoring of the ammonia gas. Analog part of the system is wired about TLC 271. Deployment of on-chip ADC of 10-bit resolution causes to increase in the preciseness of the results. The system is calibrated ammonia gas in %. From performance analysis, it is found that sensor module is suitable for further development of measurement instrumentation. Results of implementation are interpreted in this paper.

 Polycrystalline ferrite, X-ray, FTIR spectroscopy, ammonia gas sensor, embedded system, on-chip ADC


1.       K. Konstantinos, X. Apostolos, K. Panagiotis and S. George, “Topology Optimization in Wireless Sensor Networks for Precision Agriculture Applications,” Sensor Communication, (2007) 526-530.
2.       Baggio, “Wireless Sensor Networks in Precision Agriculture,” Delft University of Technology, Delft, (2009).

3.       W. Zhang, G. Kantor and S. Singh, “Integrated Wireless Sensor/Actuator Networks in Agricultural Applications,” In 2nd ACM International Conference on Embedded Networked Sensor Systems, (2004) 317.

4.       S.N. Patil, A.M. Pawar, S.K. Tilekar and B.P. Ladgaonkar “Investigation of magnesium substituted nano particle zinc ferrites forrelative humidity sensors” Sensors and Actuators A 244 (2016) 35–43.

5.       T. G. G. Maffeis, “Nano-Crystalline SnO2 Gas Sensor Response to O2 and CH4 At Elevated Temperature Investigated by XPS”, Surface Science, 520 1 (2002)29–34 -1.

6.       Abdel-Latif, “Fabrication of Nano - Size Nickel Ferrites for Gas Sensors Applications”, J. Phys., 1 2 (2012) 50 – 53.

7.       Y. Koseoglu, I. Aldemir, F. Bayansal, S. Kahraman and H. A. Çetinkara, “Synthesis, Characterization and Humidity Sensing Properties of Mn0.2Ni0.8Fe2O4 Nanoparticles”, Mater. Chem. and Phys., 139 (2013) 789-793.                      

8.       J. Chargles, N. O’Connor, E. Kolesnichenko, C. Carpenter, S. W. Zheu, A. Kumbhar, S. Jessica and A. Fabrice, “Fabrication and Properties of Magnetic Particles with Nanometer Dimensions”, Synthetic Metals, 122 3 (2001)547-555.                                

9.       Dias, “Microstructural Evolution of Fast-Fired Nickel- Zinc Ferrites from Hydrothermal Nanopowders”, Materials Research Bulletin, 35 9 (2000)1439- 1446.  

10.    S. T. Mahmud,  A. K. M. AktherHossain, , A.K. M. Abdul Hakim, M. Seki, T. Kawai, and H. Tabata, “Influence of Microstructure on the Complex Permeability of Spinel Type Ni–Zn Ferrite”, J. Magn. Magn. Mater., 305 (2006) 269– 274.                              

11.    R. Sridhar, D. Ravinder and K. Vijaya Kumar, “Synthesis and Characterization of Copper Substituted Nickel Nano-Ferrites by Citrate-Gel Technique”, Advan. Mater. Phys.& Chem., 2(2012)192-199.   

12.    M. Ahmadipour and K.VenkateswaraRao “Preparation of Nano Particle Mg0.2Fe0.8O by Solution Combustion Method and Their Characterization”, Int. J. Engg. & Adv. Techno., 1 6 (2012) 135-137. 

13.    X. Wang, N. Miura and N. Yamazoe, “Study of WO3-based sensing materials for NH3 and NO detection”. Sens. Actuat. B-Chem. 66(2000)74–76.B. Karunagaran, P. Uthirakumar, S. J. Chung, S. Velumani and E. K. Suh, “TiO2 thin film gas sensor for monitoring ammonia”. Mater. Charact. 58(2007)680–684.        

14.    S.N. Patil and B.P. Ladgaonkar, “Synthesis and Implementation of NiZnFe2O4 Ferrites to Design Embedded System for Humidity Measurement”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 8, August 2013,3813-3821.

15.    P. N. Kumar, N. S. M. Sharma, M. S. M. Mohan and D. Raj, “Design and Implementation of ARM Intelligent Monitoring System Using Zigbee”, International Journal Research In Computer and Communication Team, 1, 7 (2012) 465-470.

16.    A.M. Pawar, S. N. Patil, A. S. Powar and B. P. Ladgaonkar, “Wireless Sensor Network To Monitor Spatio-Temporal Thermal Comfort of Polyhouse Environment”, International Journal of Innovative Research in Science, Engineering and Technology, 2 10 (2013) 4866-4875.

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Anilkumar C. Korishetti, V.S. Malemath

Paper Title:

Comparison of Different Motion Estimation Algorithms for H.264 Video Compression

Abstract: This paper is the review of the different block matching motion estimation algorithms including the basic algorithms to the new motion estimation algorithms, which are accepted by all the people who are working in this field. The main goal of this paper is to understand the basic and new motion estimation algorithms which help the new researchers to understand the algorithms and help them to develop the new algorithms in this field. The algorithms that are evaluated in this paper are accepted by the video compressing community for different input standards like MPEG1 to H.261 and MPEG4 to H.263, and H.264. This paper also gives the brief introduction of video compression flow.

 block matching; video compression; MPEG; H.261; H.263 and H.264.


1.    E.Richardson ,“ H.264 Advanced video compression standard” (Second edition), 2010, John Wiley & Sons, Ltd.
2.    Study materials and tutorials from website.

3.    Wiegand, G.Sullivan, G.Bjontegaard, and A.Luthra, “ Overview of the H.264/AVC video coding standards”, IEEE transactions on circuits and systems for video technology, Vol.13, No.7, July 2003.

4.    Faizul Jamil, Ali Chekima, Rosalyn R. Porle, Othman Ahmad, and Nor-farariyanti Parimon, “BMA Performance of Video Coding for Motion Estimation”, IEEE 2012 3rd International Conference on Intelligent Systems Modelling and Simulation.

5.    Renxiang Li, Bing Zeng, and Ming L. Liou,   A New        3-Step Search Algorithm for Block Motion Estimation, IEEE Trans.Circuits And Systems For Video Technology, vol 4., no. 4, pp. 438-442, August 1994.

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8.    Lai-Man Po, and Wing-Chung Ma, “A Novel Four-Step Search Algorithm for Fast Block Motion Estimation “, IEEE Trans. Circuits And Systems For Video Technology, vol 6, no. 3, pp. 313-317, June 1996.




Nikita P.Nagori, Vandana Malode

Paper Title:

Gesture Recognition for Communication between Physically Challenged People and Normal People

Abstract: Sign Language is the most essential communication path between hearing impaired group and ordinary people. There are roughly three hundred Sign Languages that are utilized the world over today and it is thought to be the 6th biggest language utilized around the world. The Sign Languages are created relying upon the country and area of the hard of hearing individuals. Since there are a wide range of Sign Dialects, it represents a trouble for a hard of hearing individual to speak with ordinary people from various areas. The point is to build up a Sign Language interpreter that facilitates the correspondence of the hard of hearing individuals. Advances in information technology inspirit the development of systems that can facilitate the automatic transcription between sign language and spoken language, and these lines help in expelling obstructions confronting the incorporation of hard of hearing individuals with the typical individuals in the general society. In this arose devices which are used capable of capturing the movements of a person and, through it, control these gestures. One of the devices that came with this purpose is Microsoft Kinect. The objective is to develop a communication interface between a normal person and a deaf-mute person using Kinect. In this paper a set of experiments is used to develop a statistical system for translating sign language to text for deaf people and then text to speech for normal people, which helps to reduce communication gap.

 MATLAB, Microsoft Kinect, Sign Language, Skeletal Tracking


1.       Aleem Khalid Alvi, M. Yousuf Bin Azhar, Mehmood Usman, Suleman Mumtaz, Sameer Rafiq, Razi Ur Rehman, Israr Ahmed T , ―Pakistan Sign Language Recognition Using Statistical Template Matching,‖ World Academy of Science,Engineering and Technology, 2005.
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3.       M.W. Kadous, ―Machine recognition of Australian signs using power gloves: Toward large-lexicon recognition of sign language,‖ Proc. Workshop Integration Gesture Language Speech, 1996,pp. 165–174,

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5.       H. Matsuo, S. Igi, S. Lu, Y. Nagashima, Y. Takata, and T. Teshima, ―The recognition algorithm with noncontact for Japanese sign language using morphological analysis,‖ Proc. Int. Gesture Workshop, 1997, pp. 273–284.

6.       C. Wang, W. Gao, and Z. Xuan, ―A Real-Time Large Vocabulary Continuous Recognition System for Chinese Sign Language,‖ Proc. IEEE Pacific Rim Conf. Multimedia, 2001, pp. 150-157.

7.       K Assaleh, M Al-Rousan, ―Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers,‖ EURASIPJournal on Applied Signal Processing, 2005, vol. 2005, no. 13, pp. 2136-2145.

8.       T. Shanableh, K. Assaleh and M. Al-Rousan, ―Spatio-Temporal feature extraction techniques for isolated gesture recognition in Arabic sign language,” IEEE Trans. Syst., Man, Cybern. B, 37(3), June 2007.

9.       Hernandez-Rebollar, J. L., Kyriakopoulos, N., & Lindeman, R. W. (2004, May). A new instrumented approach for translating American Sign Language into sound and text. In Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on (pp. 547-552). IEEE.




Dipak V. Sose, Sayyad Ajij D.

Paper Title:

Data Acquisition of Weather Parameters and Wireless Monitoring

Abstract:  Weather forecasting is the application of science and technology to predict the state of the atmosphere for a given location. Human beings have attempted to predict the weather informally for millennium and formally since the nineteenth century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere on a given place and using scientific understanding of atmospheric processes to project how the atmosphere will evolve on that place. Wireless technology has been tremendously growing day by day. The need of wireless technology is that it replaces the conventional methods including long wires for communication and thereby increasing the redundancy of the whole system. The measurements of temperature, atmospheric pressure and relative humidity remotely by using the appropriate sensors are not only important in environmental or weather monitoring but also crucial for many industrial processes. A system for weather monitoring is described in this project to monitor and display the, Temperature (0C), Humidity (RH%), Light Intensity (lx), Atmospheric Pressure (Kpa), Vibration or Acceleration (g), Wind Speed(Km/h), Rainfall (mm), LPG leakage (ppm) and CO2 contents in air (ppm) using analogue and digital components. The analogue outputs of the sensors are connected to a microcontroller through an ADC for digital signal conversion.

 Microcontroller, Sensors, Wireless, ADC etc.


1.          Nisha Gahlot, Varsha Gundkal, Sonali Kothimbire, Archana Thite,  Zigbee based weather monitoring system, The International Journal Of Engineering And Science (IJES) Volume 4 Issue 4 Pages  PP.61-66, 2015 
2.          Luis Diego Bricen, Anthony A. Maciejewski, Heuristics for robust resourse allocation of satellite weather data processing on heterogeneous parallel system, IEEE transactions on parallel and distributed systems, vol. 22, no. 11, November 2011.

3.          Dushyant Pande, Jeetender Singh Chauhan, A real time hardware design to automatically monitor and control light & temperature, International Journal of Innovative Research in Science, Engineering and Technology Vol. 2, Issue 5, May 2013.

4.          Iswanto And Helman Muhammad, Weather Monitoring Station With Remote Radio Frequency Wireless Communications, International Journal of Embedded Systems and Applications (IJESA) Vol.2, No.3, September 2012.

5.          Nisha singh, Prof. Ravi Mishra, Microcontroller Based Wireless Temperature And Heart Beat Read-Out, International Organization of Scientific Research Journal of Engineering (IOSRJEN), Vol. 3, Issue 1,  PP 01-06, Jan. 2013.

6.          K C Gouda1, Preetham V R and M N Shanmukha Swamy, Microcontroller Based Real Time Weather Monitoring Device With GSM, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014.

7.          P.Susmitha and  G.Sowmyabala, Design and Implementation of Weather Monitoring and Controlling System, International Journal of Computer Applications Volume 97– No.3, July 2014.

8.          Mr. P. K. Jayalaxmi Mr. A. Pritviraj, A Real Time Weather Monitoring System With Fm Channel, International Journal of Advanced Information and Communication Technology  Vol 1, Issue-1, May 2014”.

9.          Pranita Bhosale and V.V.Dixit, Agricon-Weather Monitoring System and Irrigation Controller, International Organization of Scientific Research Journal of Electronics and Communication Engineering (IOSRJECE), Volume 1, Issue 6, PP 05-11, July-Aug 2012.

10.       Nitant Sabharwal, Rajesh Kumar, Abhishek Thakur, Jitender Sharma, A  low cost Zigbee based automatic wireless weather station with GUI and web hosting facility, ICRTEDC, Vol. 1, Spl. Issue 2, May 2014.

11.       Karthik Krishnamurthi, Suraj Thapa, Lokesh Kothari, Arun Prakash, Arduino based weather monitoring system, International Journal of Engineering Research and General Science, Volume 3, Issue 2, March-April 2015.

12.       Keshav kumar singh, S.styline chirmaxo, Design of wireless weather monitoring system, Bachelor of Technology, National Institute of Technology Rourkela, India, May 2013.

13.       National Space Weather Program Council, Space weather observing system.

14.       Muthoni Masinde and Antoine Bagula, A Calibration Report for Wireless Sensor-Based Weatherboards, Journal of Sensor and Actuator Networks, Central University of Technology Free State, Private Bag X20539, Bloemfontein 9300, Republic of South Africa 2015.

15.       Anna Maria Sempreviva, Sven-Erik Gryning and Gregor Giebel, Weather intelligence for Renewable Urban Areas Gaps, Challenges and future perspectives, National Council of Research of Italy Wind Energy Department, Technical University of Denmark, 2nd 3rd June 2014. 

16.       Kamrul AriffinNoordin Chow chee onn and Mohamad faizal ismail, A low cost microcontroller based weather monitoring system, Carnegie Mellon University, vol 5 pp 33-38, 2006

17.       Kefei Zhang, Toby Manning, Suqin Wu, Witold Rohm, D. Silcock, and Suelynn Choy, Capturing the signature of severe weather events in austrilia using GPS measurement, Institute of Electrical and Electronics Engineers journal of selected topics in applied earth observations and remote sensing, vol. 8, no. 4, april 2015

18.       Field visit, Hydrology Project, Godavari Pathbandhare Vikas Mahamandal, Aurangabad (MS), India, 22nd December 2015.

19.       Field visit, Department of Meteorology, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani (MS) India, 24th December 2015




Kajal Patel, Bijith Marakarkandy

Paper Title:

Feature Selection using Clusters

Abstract:   Feature selection involve identifying a subset of the most useful feature that produces compatible results as the original entire set of features. The feature selection algorithm can be evaluated on the basis of two criteria: efficiency and effectiveness. In fact feature selection, as a pre-processing step which is effective in reducing dimensionality, removing irrelevant data, removing redundant data etc. Feature selection clustering mechanism works in five stages. In the first stage, T-relevance test is used to eliminate irrelevant features. In the second stage, F-correlation is calculated between attributes. In third stage, map and reduce function accomplishes groups of features. In fourth stage, actual clusters are evaluated. And in the final stage, the most representative feature from each cluster are considered. Features in different clusters are comparatively efficient and independent features. For the lack of density-based clustering algorithm in dealing with large data sets, MapReduce programming model is proposed to accomplish the clustering of DBSCAN. Map function completes the data analysis, and gets clustering rules in different data attributes; Then Reduce functions merge these clustering rules to get a final result. Experimental results shows the DBSCAN with MapReduce running on the cloud computing platform Hadoop has good speed-up and scalability approach. An analysis has been done between the most representative feature of DBSCAN clustering using MapReduce with the existing FAST feature subset algorithm on the basis of execution time & number of clusters performed. Experimentally it has been verified that proposed algorithm alleviates the problem of time delay caused by large data sets.

 Big Data, DBSCAN, Fast Clustering, Feature Selection, Hadoop, MapReduce.


1.       F. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money, Hoboken, N.J, USA: Wiley, 2013.
2.       Q. Song, J. Ni and G. Wang, “A Fast Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data”, IEEE transaction on Knowledge and Data Engineering ,vol. 25, no. , pp. 1-14, Jan. 2013.

3.       M. Noticewala and D. Vaghela,"MR-IDBSCAN: Efficient Parallel Incremental DBSCAN algorithm using MapReduce". International Journal of Computer Applications 93(4):13-18, May 2014

4.       R. Shettar Professor, B.V.Purohit,"A Review on Clustering Algorithms Applicable for Map Reduce", Proceedings of the International Conference , Computational Systems for Health & Sustainability, 17-18, April, 2015.

5.       V. Bawane and S. Kale ," Clustering Algorithms in MapReduce: A Review," National Conference on Recent Trends in Computer Science & Engineering (MEDHA 2015).

6.       V.A.Kokane and A.C.Lomte,"CBFAST- Efficient Clustering Based Extended Fast Feature Subset Selection Algorithm for High Dimensional Data", International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),Volume 4 Issue 5, May 2015.

7.       J. Dean and S. Ghemawat, "MapReduce: Simplified data processing on large clusters," Commun ACM, 51(1), pp. 107-113, 2008.

8.       V. Kumar and S. Minz, ”Feature Selection: A literature Review,” Smart Computing Review, vol. 4, no. 3, Jun 2014.

9.       Hadoop [online]. Available:




G. S. Sable, M. R. Rajput, H. R. Gite

Paper Title:

Analogy between Statistical and CHT Segmentation for Human Iris

Abstract: As Iris is the most safe and reliable biometric pattern, Iris biometrics provides high recognition rate and high accuracy for person identification as compared with the other biometric techniques. The crucial stage in iris recognition is iris segmentation. Segmentation of iris from captured eye image is a difficult task as iris images are affected by different factors such as poor illumination, motion blur, improper focus, large standoff distances and occlusion by eyelashes and eyelids etc. This ultimately affects the iris recognition rate. In this  paper two methods of iris localization are discussed The first is a statistical method and the second is a CHT method. Paper shows that CHT method overcomes the limitations of statistical method and is better suited for iris segmentation.

 CHT segmentation, Iris segmentation, recognition rate


1.       Adegoke, B. O. , Omidiora, E.  , Falohun, S. A.  Oja, J.A “Iris Segmentation: a survey ”,International Journal of Modern Engineering Research (IJMER) ,Vol.3, Issue.4, Jul - Aug. 2013 pp-1885-1889 ISSN: 2249-6645
2.       John Daugman, “How Iris Recognition Works” IEEE Transactions on circuits and systems for video technology, vol. 14, no. 1, january 2004.

3.       Richard P. Wildes,”Iris Recognition: An Emerging Biometric Technology”, Proceedings of the IEEE , Vol. 85, No. 9, September 1997.

4.       Lim, K. Lee, O. Byeon and T. Kim, “Efficient iris recognition through improvement of feature vector and classifier”, ETRI Journal,Vol. 23, No. 2 Korea, pp 1220-1225, 2001.

5.       W. Kong and D. Zhang, “Accurate iris segmentation based on novel reflection and eyelash detection model”, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, 2001.

6.       L. Ma, Y. Wang and T. Tan, “Iris recognition using circular symmetric filters”, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 2002.

7.       Jiali Cui, Yunhong Wang, Tieniu Tan, Li Ma, Zhenan Sun, “An Iris Recognition Algorithm Using Local Extreme Points”,

8.       Zhonghua Lin, “A Novel Iris recognition method based on the natural      open eyes.”, IEEE sponsored (ICSP) 2010 conference Proceedings , pp 1090-   1093.

9.       Bowyer, Kevin W. , Burge, Mark J.   “Introduction to the Handbook on Iris Recognition ” , Springer Publication.
10.    Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson  Prentice Hall Publication
11.    Min Han ,Weifeng Sun, Mingyan Li., “Iris recognition based on a Novel Normalization method and contourlet transform , Proceedings of    2nd International Congress on Image and Signal Processing 2009.

12.    Chia-Te Chou, Sheng-Wen Shih, “on Orthogonal View Iris Recognition system” IEEE transactions on circuit and systems for video technology, 2010

13.    Vishnu Boddeti, B.V.K. Vijay Kumar, “Extended depth of field Iris  Recognition using unrestored wavefront coded Imagery,” IEEE transactions on systems, MAN and cybernetics Part A: system   and humans, vol.40, no.3, May, 2010, pp495-508.

14.    Amir shahram, Hematian, Aziazh, Abdul Manaf, “Field  programmable gate array system for real time-Iris Recognition”, IEEE conference on open systems , 2012.

15.    Hiew Moi, Hishammudi Asmuni, Rohyanti, Hassan, RazibM.Othman,  “A Unified approach for unconstrained off angle Iris Recognition”IEEE sponsored International Symposium on biometrics and security technologies,2014,pp-39-44 .

16.    Osman M .Kurtunui, Mahmut Karkaya, “Limbus Impact removal for off- angle Iris Recognition using eye models” IEEE 7th International conference on Biometrics theory, applications and Systems,pp-1-6 .

17.    Kumal Hajari ,Kishor Bhoyar, “A review of issues and challenges in   designing Iris recognition systems for noisy image environment, IEEE transaction on International conference on Pervasive computing 2015.

18.    Chun-wei,Tan, Ajaykumar, “Accurate Iris recognition at a distance using  stabilized Iris encoding and zernike moment phase features”,IEEE Transactions on image processing ,vol-23,no-9,September 201,pp-3962-3974.

19.    Dongdong Zhao, WenjianLuo, RanLiu,LihuaYue, “Negative Iris recognition”, Proceedings of IEEE transactions on dependable and secure computing.,2015 pp-1-15.

20.    Kiennguyen, Sridha Sridharan, Simon Denman, “Quality driven super resolution for less constrained iris recognition at distance and on the move.” , IEEE transactions on information forensics and security,vol-6,no-4, December2011.

21.    Karen P. Hollingsworth ,Kelvin W.Bower, Patrick J. Flynn. “ Improved Iris recognition through fusion of hamming distance and fragile bit distance”,IEEE transactions on pattern analysis and machine intelligence,vol-33,no-12,December2011,pp-2465-2476.

22.    Jaishankar K. Pillai, Vishal M.Patel, Rama Chellappa , Nalini Ratha, “Secure and robust Iris Recognition using random projections and sparse representations”,IEEE transactions on pattern analysis and machine intelligence.vol-33,no.9,2011.

23.    Arjun Agarwal, Gundeep Singh Bindra,Priyanka Sharma, “Feature based  Iris Recognition system functioning on extraction of 2D features”,IEEE sponsored International conference on System engineering and technology  2012,pp-1-5.

24.    Sudeep Thepade, Pushpa Manchal , “Energy compaction based novel iris recognition techniquesusing partial energies of transformed Iris images with Cosine,Walsh,Harr,Kekre,Hartley transforms and their wavelet transforms”, Annual  IEEE India conference,2014.

25.    S.Hariprasath, V.Mohan, “Biometric Personal Identification based on Iris recognition using complex wavelet transforms. ”,IEEE International conference on computing, communication and networking,2008.

26.    Min Han ,Weifeng Sun, Mingyan Li., “Iris recognition based on a Novel Normalization method and contourlet transform , Proceedings of   2009 2nd International Congress on Image and Signal Processing 2009.

27.    Mrinalini I.R., Pratusha B.P.,Manikanton K. S. Ramachandran, “Enhanced Iris Recognition using Discrete cosine Transform and radon transform.”, IEEE sponsored,2nd International conference on

28.    R.Meenakshi Sundaram, Bibhas Chandra Dhara, “Neural network based Iris recognition system using Haralick features, Procededings of 3rd International Conference on Electronics Computer Technology (ICECT) 2011, pp-19-23,2011.

29.    Mrunal M.Khedakar, S.A. Ladhake, “Neural network based Iris pattern  recognition system using Discrete Walsh Hadamard transform features”, IEEE sponsored International conference on Advances in computing ,communications and Informatics,2013 ,pp-388-393

30.    Naresh Babu NT, Vaidehi V., “Fuzzy based Iris recognition system for person identification.”, IEEE sponsored International conference on recent trends in information technology, 2011 pp-1005-1010.

A.      Aldhahab, G. Atia and W. Mikhail, “Supervised Facial Recognition based on Multiresolution Analysis with Radon Transform,” 48th Asilomar Conference on Signals, Systems and Computers, Pacific Grove CA, Nov 2014.

31.    Yuanning Liu and FeiHe, Yonghua Zhao, Ning Wang, “An Iris recognition based on GHM multiwavelet transformation”, IEEE 4th International conference on Innovative computing ,Information and control,2009,pp-793-796.

32.    Aalaa Albadarneh, Israa Albadarneh, Jafar Alquatawna, “Iris recognition system for secure authentication based on texture and shape feature.”IEEE International Jordan conference on applied electrical engineering and computing technologies, 2015.

33.    B.V.Bharath,Vilas, S.,K.Manikantan,S.Ramachandran. “Iris Recognition using Radon transform thresholding based feature extraction with gradient based isolation as a preprocessing technique.”, 9th International Conference on Industrial and Information Systems,2014,pp.1-8.

34.    Lin Zhonghua, Lu Bibo, “Iris recognition method based on the coefficients of morlet wavelet transform,” IEEE sponsored International conference on Intelligent Computation technology and automation,2010 pp-576-580.

35.    Izem Hamouchene, Saliha,Aoqat, “Texture matching using local and global descriptor.”, IEEE sponsored 5th European workshop on Visual image processing, (EUVIP),  2014.

36.    Mohammad Javed, Aligholizadeh Shahram Javedi, Kareh Kangarloo., “Eyelid and eyelash segmentation based on wavelet transform for Iris recognition.”, IEEE sponsored  4th International congress on Image and signal processing, 2011, pp-1231-1235.

37.    Lydia Elizabeth, Duraipandi C, AnjuPratap, Rhymed Uthariraj W. “ A Grid based Iris Biometric watermarking using wavelet transform.”, IEEE sponsored International conference on recent trends in Information Technology 2014 ,pp.1-6,.

38.    Salim Lahmiri, Mounir Bhukadoum, “DWT and DT based approach for feature extraction and classification of mammograms with SVM”,IEEE sponsored International conference on Biomedical Circuits and Systems Conference , 2011, pp-412-415.

39.    Ameya Deshpande, Sumitkumar Dubey, Satishkumar Chavan, Aditya Potnis, “Iris Recognition system using Block based approach with DWT and DCT”, Annual IEEE India Conference, 2014.

40.    Prajoy Poddev, Tanvir Zaman Khan, Mamdulul Haque Khan, Rafi Ahmed ,MdSaifur Rahman, “An efficient Iris segmentation model based on eyelid and eyelashes detection in Iris recognition systems”, IEEE sponsored International conference on Computer communication and Informatics,Coimbatore,India,2015.

41.    Jianxu Chen, Feng Shen, DannyZ.Chen, Patrick J.Flynn , “Iris recognition system based on Human Interpretable features”, IEEE  Transactions on Identity, Security , and Behaviour analysis, 2015,”pp 1-6.

42.    V.V. Satyanarayana, Tallapragada, Dr.E.G.Rajan, “Morphology based Non-ideal Iris recognition using decision tree classifier.”, IEEE sponsored International conference on pervasive computing.,” 2015,pp 1-14.

43.    Libor Masek, “Recognition of Human Iris Patterns for BiometricIdentification”, libor/Libor  MasekThesis.pdf    

44.    Nitin K. Mahadeo, Gholamreza Haffari, Andrew P. Paplinski “Predicting Segmentation errors in an Iris recognition.” International conference on Biometrics  2015,pp23 -30.

45.    Zhonghua Lin, “A Novel Iris recognition method based on the natural   open eyes.”, IEEE sponsored (ICSP) 2010 conference Proceedings , pp 1090-    1093.

46.    Prateek Verma, Maheedhar Dubey, Somak Basu, Praveen Verma , “Hough Transform Method for Iris Recognition-A Biometric Approach “, International Journal of Engineering and Innovative Technology (IJEIT) ,Volume 1, Issue 6,   June 2012




Bhagyashree V. Shivpuje, Ganesh S. Sable, Kartik Raman

Paper Title:

Feature Extraction of Dental Radiographic Images for Disease Identification and Classification using ANN

Abstract:  The early detection of disease is one of important matter of diagnostic imaging. In this paper we developed a system to analysis the dental x-ray images and diagnosis the tooth which has abnormalities of caries. Enhancement applied to improve the quality of x-ray images and Thresholding method performed to simplify the images. Segmentation has been done by applying the integral projection technique to extract the individual tooth and therefore feature map of tooth generated and detection and classification process can be done by using artificial neural network. Nevertheless, experiments show the accurate segmentation and caries detection with proposed method which achieves high accuracy for diseased images and promising result.

 ANN, caries detection, integral projections, segmentation, feature extraction


1.       J. Solanki, K. R. Jain & N. P. Desai, "ISEF Based Identification of RCT/Filling in Dental Caries of Decayed Tooth", International Journal of Image Processing (IJIP), Volume (7) : Issue (2) : 2013
2.       Ştefan OPREA1, Costin MARINESCU 2, Ioan Liţă, Daniel Alexandru Vişan, Ion Bogdan Cioc, "Image Processing Techniques used for Dental X-Ray Image Analysys".

3.       Jincy Raju, Dr. Chintan K. Modi," A Proposed Feature Extraction Technique for Dental X-Ray Images Based on Multiple Features", Conference Paper July 2011 DOI: 10.1109/CSNT.2011.116 • Source: IEEE Xplore

4.       Vijayakumari Pushparaj, Ulaganathan Gurunathan, Banumathi Arumugam, " An Effective Dental Shape Extraction Algorithm Using Contour Information and Matching by Mahalanobis Distance", Journal of Digit Imaging. 2013 Apr; 26(2): 259–268.

5.       Eyad Haj Said, Diaa Eldin M. Nassar, Gamal Fahmy, Hany H. Ammar," Teeth Segmentation in Digitized Dental X-ray Films using Mathematical Morphology", IEEE, ISSN: 1556-6013 Transactions on Information Forensics and security

6.       Sharmila. M, Dr. R. Ganesan, R. Kartika Devi, “Detection of Dental Plaque using Image Processing”, IJAIST, Vol.18, No. 18, October 2013

7.       M.Thamarai, M.Kalpa, “Automated Diagnosis of Periodontal Diseases Using Image Processing Techniques”, IJIRSET, Vol. 3 Issue 1, January 2014

8.       A.Farzana Shahar Banu, M. Kayalvizhi, Dr. Banumathi Arumugam, “Texture Based Classification of Dental Cysts”, ICCICCT, 2014

9.       M.V. Bramhananda Reddy, Varadala Sridhar, M. Negendra, “Dental X-Ray Image Analysis by using Image Processing Techniques”, IJARCSSE, Vol.2,Issue 6, June 2012

10.    Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, AlirezaNorouzi, “Digital Dental X-Ray Image Segmentation and feature Extraction”, TELKOMNIKA, Vol.11, No.6, June 2013

11.    Dipali Rindhe, Ganesh Sable,“Teeth Feature Extraction and Matching for Human Identification using SIFT Algorithm”, EJAET, Vol.2 No.1, 2015

12.    Kavindra R. Jain, Narendra C. Chauhan, “Efficacy of Digital Image Processing Techniques in intra Oral Dentistry”, TROI, Vol. 2, Issue 2, 2015

13.    Adhar Vashishth, Bipan Kaushal, Abhishek Srivastava, "Caries Detection Technique for Radiographic and Intra Oral Camera", IJSCE Vol-4, Issue-2, May 2014

14.    Chintan K. Modi and Nirav P. Desai, "A Simple and Novel Algorithm for Automatic Selection of ROI for Dental Radiograph Segmentation", IEEE CCECE 2011
15.    Azam Amini Harandi, Hossein Pourghassem, " A Semi Automatic Algorithm Based on Morphology Features for Measuring of Root Canal Length", IEEE 2011, 978-1-61284-486-2/11.
16.    Omaima Nomir  and Mohamed Abdel-Mottaleb, " Human Identification From Dental X-Ray Images Based on the Shape and Appearance of the Teeth",  IEEE 2007, 556-6013.

17.    Martin L. Tangel, Chastine Fatichah, and Fei Yan, " Dental Classification for Periapical Radiograph based on Multiple Fuzzy Attribute",  IEEE 2013, 978-1-4799-0348-1/13.

18.    Khin Mya Mya Tun, Aung Soe Khaing, "Feature Extraction and Classification of Lung Cancer Nodule using Image Processing Techniques", IJERT 2014, 2278-0181-3/3.

19.    Abdolvahab Ehsani Rad, Ismail Bin Mat Amin, Mohd Shafry Mohd Rahim, and Hoshang Kolivand, " Computer-Aided Dental Caries Detection System from X-Ray Images", Springer 2015, 978-3-319-13513-5_23

20.    A.N Shaikh, G.S.Sable, “Clinical Applications and Importance of Cone Beam Computed Tomography (CBCT) artifacts in Dental Imaging",  IEEE ICETECH, 2015.

21.    Bhagyashree V. Shivpuje, Dr. G. S. Sable, "A Review on Digital Dental Radiograph Images for Disease Identification and Classification", Int. Journal of Engineering Research and Application, ISSN : 2248-9622, Vol. 6, Issue 7, ( Part -5) July 2016.

22.    White and Pharoah , "Oral Radiology-Principles and Interpretation".

23.    Gonzalez, Woods, and Eddins, "Digital Image Processing using MATLAB", 3rd Edition.

24.    Rafael Gonzalez, Richard Woods, "Digital Image Processing", 2nd Edition

25.    Prof. D. Cavouras, " Medical Image Processing".




P. S. Tiwari, S. R. Patil

Paper Title:

Principal Component Analysis Based Face Recognition System

Abstract: This paper focuses on a software framework to support face recognition and Security and authentication, specific area of image processing. For the processing approach, we use principal component analysis (PCA), a data dimensionality reduction approach.   There are many techniques used for this purpose. One of them is face recognition. Face recognition is an effective means of authenticating a person. The advantage of this approach is that, it enables us to detect changes in the face pattern of an individual to an appreciable extent. The recognition system can tolerate local variations in the face expression of an individual. Hence face recognition can be used as a key factor in crime detection mainly to identify criminals. The system consists of a database of a set of facial patterns for each individual. The characteristic features called ‘Eigen faces’ are extracted from the stored images using which the system is trained for subsequent recognition of new images The goal of this study is to understand the entire face recognition process with PCA and to present a software framework supporting multiple variations, which can be used to help users create customized face recognition applications efficiently. The proposed method is tested using FERET database

 PCA, Face Recognition, Eigen faces.


1.       Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, and Ric hard Russell,   “Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About” Proceedings of the IEEE, Vol. 94, No. 11, November 2006
2.       G. Shakhnarovich, J. Fisher, and T. Darrell. Face recognition from long-term observations. In ECCV, 2002.

3.       K. Chang, K. Bowyer, and P. Flynn, “Multi-modal 2d and 3d biometrics for face recognition,” to appear in IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003.

4.       B. Moghaddam and A. Pentland. Probabilistic visual learning for object representation. IEEE Trans. Pattern Analysis and Machine Intell, 19:696–710, 1997.

5.       N. Rajagopalan, K. S. Kumar, J. Karlekar, R. Manivasakan, M. M. Patil, U. B. Desai, P. G. Poonacha, and S. Chaudhuri. Locating human faces in a cluttered scene. Graphical Models in Image Processing, 62:323–342, 2000.

6.       D. A. Socolinsky and A. Selinger, “A comparative analysis of face recognition performance with visible and thermal infrared imagery,” in International Conference on Pattern Recognition, pp. IV: 217–222, August 2002.

7.       A.J. GoldStein, L.D Harmon, and A.B Lesk, “Identification of human faces”, Proc IEEE, May 1971, Vol. 59, No. 5, 748-760.

8.       L. Sirovich and M Kirby, “A low dimensional Procedure for the characterization of human faces,” J Optical. Soc. Am. A, 1987, Vol. 4, No. 3, 519-524

9.       M. A .Turk and A. P. Pentland, “Face Recognition using Eigen Faces,” Proc. IEEE, 1991, 586-591.

10.    Kwang In Kim, Keechul Jung, and Hang Joon Kim, “Face recognition using kernel principal component analysis,” Signal Processing letters IEEE, vol. 9 Issue. 2 page 40-42 Feb, 2002.

11.    Xin Chen, Patrick J. Flynn, Kevin W. Bowyer, "PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative   Studies," amfg, pp.127, IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003

12.    Peng, Peng, and Yehu Shen. ”Efficient face verification in mobile environment using component-based PCA.” Image and Signal Processing (CISP), 2013 6th International Congress on. Vol. 2. IEEE, 2013.

13.    The Code Project, “EMGU Multiple Face Recognition using PCA and Parallel Optimisation", 05 October 2011.

14.    L. Sirovich and M Kirby, “A low dimensional Procedure for the characterization of human faces,”

15.    Wikipedia, the free encyclopedia, "Natural language processing", 15 September.




Kalyanee S. Patil

Paper Title:

Wireless Body Area Network

Abstract:  The project in designing a system which is capable of tracking the location of patients and also monitoring of heart rate and health parameters also alerts in case of emergency through SMS to predefined number. This aims in design and present the novel wearable system with the bio recognition sensors based on the GPS and GSM communication Network. In this project sensors (3-axis accelerometer, temperature sensor, heartbeat sensor, humidity sensor, ECG sensor) added and located at patient body for getting the postures information and activity by them always monitored. The system sends alert messages in emergency times.

 ARM7, ECG Sensor, GPS, GSM, Heart Beat, MEMs Sensor


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3.    MirHojjat Seyedi, Student Member, IEEE, Behailu Kibret, Student Member, IEEE, Daniel T. H. Lai, Member, IEEE, and Michael Faulkner, Member, IEEE. “A survey on intrabody communications for body area network applications” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 8, AUGUST 2013

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6.    “Interoperability and Security in Wireless Body Area Network Infrastructures” Steve Warren1, Jeffrey Lebak1, Jianchu Yao3,