i-manager Publications

i-manager's Journal on Image Processing (JIP)

Current Issue Vol. 10 Issue 4

Multi-Lingual Character Recognition and Extraction using Recurrent Neural Networks
A Novel Approach to Smart Autonomous Monitoring of Indoor Plant Health Based on Leaf Color
Edge Preserving Image Denoising with Deep Networks and Ensemble Convolutional Neural Network Architectures
Neural Network Approach to Detect Renal Calculi
Enhancing Fabric Quality Control: Implementing Real-Time Defect Detection with Image Processing Techniques and Arduino

Most Cited

Volume 5 Issue 1 January - March 2018

Research Paper

Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos

Harihara Santosh Dadi* , Gopala Krishna Mohan Pillutla , Madhavi Latha Makkena *

* Associate Professor, Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Hyderabad, Andhra Pradesh, India.

Professor, Department of Electronics and Communication Engineering, Institute of Aeronautical College of Engineering, Hyderabad, Andhra Pradesh, India.

* Professor, Department of Electronics and Communication Engineering, JNT University, Telangana, India.

Dadi, H.S., Pillutla, G.K.M. and Makkena, M.L. (2018). Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos. i-manager’s Journal on Image Processing, 5(1), 1-11. https://doi.org/10.26634/jip.5.1.14612

Abstract

Detection of human is the first and foremost step in any human tracking system. Color and shape based automated human detection algorithm in surveillance videos have been proposed in this paper. Every frame is first divided into R, G, and B frames. The background images for these three basic colors are formed, and three binary images for three colors are formed for every frame. One binary image overlaps over the other to find the common rectangles. Based on the aspect ratio of the rectangle, humans are detected. The proposed algorithm uses both color and the shape aspect in finding the humans. The performance results show that the proposed algorithm has good metrics.

References

[1]. Beleznai, C., Fruhstuck, B., & Bischof, H. (2004). Human detection in groups using a fast mean shift procedure. In Image Processing, 2004. ICIP'04. 2004 International Conference on (Vol. 1, pp. 349-352). IEEE.

[2]. Cutler, R., & Davis, L. S. (2000). Robust real-time periodic motion detection, analysis, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 781-796.

[3]. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE.

[4]. Elzein, H., Lakshmanan, S., & Watta, P. (2003). A motion and shape-based pedestrian detection algorithm. In Intelligent Vehicles Symposium, 2003. Proceedings. IEEE (pp. 500-504). IEEE.

[5]. Eng, H. L., Wang, J., Kam, A. H., & Yau, W. Y. (2004). A bayesian framework for robust human detection and occlusion handling human shape model. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17 International Conference on (Vol. 2, pp. 257-260). IEEE.

[6]. Gavrila, D. M., & Giebel, J. (2002). Shape-based pedestrian detection and tracking. In Intelligent Vehicle Symposium, 2002. IEEE (Vol. 1, pp. 8-14). IEEE.

[7]. Haga, T., Sumi, K., & Yagi, Y. (2004). Human detection in outdoor scene using spatio-temporal motion analysis. In Pattern Recognition, 2004. ICPR 2004. Proceedings of th the 17 International Conference on (Vol. 4, pp. 331-334). IEEE.

[8]. Han, J., & Bhanu, B. (2003). Detecting moving humans using color and infrared video. In Multisensor Fusion and Integration for Intelligent Systems, MFI2003. Proceedings of IEEE International Conference on (pp. 228-233). IEEE.

[9]. Jiang, L., Tian, F., Shen, L. E., Wu, S., Yao, S., Lu, Z., & Xu, L. (2004, October). Perceptual-based fusion of ir and visual images for human detection. In Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on (pp. 514-517). IEEE.

[10]. Lee, D. J., Zhan, P., Thomas, A., & Schoenberger, R. B. (2004). Shape-based human detection for threat assessment. In Visual Information Processing XIII (Vol. 5438, pp. 81-92). International Society for Optics and Photonics.

[11]. Li, L., Ge, S. S., Sim, T., Koh, Y. T., & Hunag, X. (2004). Object-oriented scale-adaptive filtering for human detection from stereo images. In Cybernetics and Intelligent Systems, 2004 IEEE Conference on (Vol. 1, pp. 135-140). IEEE.

[12]. Ogale, N. A. (2006). A survey of techniques for human detection from video. Survey, University of Maryland, 125(133), 19.

[13]. Sidenbladh, H. (2004). Detecting human motion with support vector machines. In Pattern Recognition, th 2004. ICPR 2004. Proceedings of the 17 International Conference on (Vol. 2, pp. 188-191). IEEE.

[14]. Toth, D., & Aach, T. (2003). Detection and recognition of moving objects using statistical motion detection and Fourier descriptors. In Image Analysis and th Processing, 2003. Proceedings. 12 International Conference on (pp. 430-435). IEEE.

[15]. Utsumi, A., & Tetsutani, N. (2002). Human detection using geometrical pixel value structures. In Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on (pp. 39-44). IEEE.

[16]. Viola, P., Jones, M. J., & Snow, D. (2003). Detecting pedestrians using patterns of motion and appearance. In null (p. 734-741). IEEE.

[17]. Wren, C. R., Azarbayejani, A., Darrell, T., & Pentland, A. P. (1997). Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 780-785.

[18]. Xu, F., & Fujimura, K. (2003). Human detection using depth and gray images. In Advanced Video and Signal Based Surveillance, 2003. Proceedings. IEEE Conference on (pp. 115-121). IEEE.

[19]. Yoon, S. M., & Kim, H. (2004). Real-time multiple people detection using skin color, motion and appearance information. In Robot and Human th Interactive Communication, 2004. ROMAN 2004. 13 IEEE International Workshop on (pp. 331-334). IEEE.

[20]. Zhou, J., & Hoang, J. (2005). Real time robust human detection and tracking system. In Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on (pp. 149-149). IEEE.

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Research Paper

Glaucoma detection using Image Processing

S.P Jagtap* , Pradnya A.Shinde, Akshay S.Manmode, Mohini B.Kalekar, Pratik S.Talware

* Assistant Professor, Department of Electronics & Communication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

-*** UG Scholar, Department of Electronics & Communication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

Jagtap, S.P., Shinde,P.A., Manmode,A.S., Kalekar,M.B., and Talware,P.S. (2018). Glaucoma Detection Using Image Processing. i-manager’s Journal on Image Processing, 5(1), 12-19. https://doi.org/10.26634/jip.5.1.14050

Abstract

Glaucoma is the most common cause of blindness in India. Diagnosis of glaucoma is based on measurement of intraocular pressure by cup area to disc area ratio from the color fundus images. When this intraocular pressure (IOP) is increased in internal eye, vision starts to decline. So, detection of glaucoma is essential for minimizing the vision loss. An approach for detection of glaucoma using fundus images is developed. The Region of interest (ROI) based segmentation is used for the detection of disc. The optic cup and disc localization are used for detecting the region of interest, and Gabor filter is used for edge detection. A Semi automated method using CDR ratio in glaucoma detection of a fundus image has been proposed. The Cup to Disc Ration (CDR) is defined as the ratio of the area between Optic Cup and Optic Disk. Optic cup size increases while the Optic Disc size remains same for a patient in Glaucoma detection and the CDR will be high for a glaucoma patient when comparing with normal fundus image. In this paper the Cup to disc ratio calculation is discussed for detection and classification of glaucoma.

References

[1]. Aqeel, A. F., & Ganesan, S. (2011). Retinal image segmentation using texture, thresholding, and morphological operations. In Electro/Information Technology (EIT), 2011 IEEE International Conference on (pp. 1-6). IEEE.

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[3]. Bock, R., Meier, J., Nyúl, L. G., Hornegger, J., & Michelson, G. (2010). Glaucoma risk index: Automated glaucoma detection from color fundus images. Medical Image Analysis, 14(3), 471-481.

[4]. Chandrika, S., & Nirmala, K. (2013). Comparative Analysis of CDR Detection for Glaucoma Diagnosis. Int. J. of Com. Sc. & App., 2(4).23-27.

[5]. Havagondi, H. M., & Kumbhar, M. S. (2014). Optic cup and disc localization for detection of glaucoma using Matlab. International Journal of Electrical, Electronics and Computer Systems (IJEECS), 2(7), 1-4.

[6]. Ho, C. Y., Pai, T. W., Chang, H. T., & Chen, H. Y. (2011). An atomatic fundus image analysis system for clinical diagnosis of glaucoma. In Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International Conference on (pp. 559-564). IEEE.

[7]. Kumar, B. N., Chauhan, R. P., & Dahiya, N. (2016). Detection of Glaucoma using image processing techniques: A review. In Microelectronics, Computing and Communications (MicroCom), 2016 International Conference on (pp. 1-6). IEEE.

[8]. Minhas, S., & Javed, M. Y. (2009). Iris feature extraction using gabor filter. In Emerging Technologies, 2009. ICET 2009. International Conference on (pp. 252-255). IEEE.

[10]. Ranjith, N., Saravanan, C., & Bibin, M. R. (2015). Glaucoma Diagnosis by Optic Cup to Disc Ratio Estimation. International Journal of Inventive Engineering and Sciences (IJIES), 3(5), 1-5.

[11]. Rangayyan, R. M., Zhu, X., Ayres, F. J., & Ells, A. L. (2010). Detection of the optic nerve head in fundus images of the retina with Gabor filters and phase portrait analysis. Journal of Digital Imaging, 23(4), 438-453.

[12]. Vision and Eye Health. (2010). Glaucoma Treatments: Eye Drops. Retrieved from http://www.visionand- eye-health.com/glaucoma-eyedrops.html

[13]. Wong, D. W. K., Liu, J., Lim, J. H., Jia, X., Yin, F., Li, H., & Wong, T. Y. (2008). Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI. In Engineering in Medicine and Biology Society, 2008. th EMBS 2008. 30 Annual International Conference of the IEEE (pp. 2266-2269). IEEE.

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Research Paper

Sunflower Seeds Classification by using GLBP

G. Silpalatha* , C Nagaraju**

* Lecturer, Department of Electrical and Communication Engineering, YSR Engineering College, Yogi Vemana University, Andhra Pradesh, India.

** Associate Professor and Head, Department of Computer Science and Engineering, YSR Engineering College, Yogi Vemana University, Andhra Pradesh, India.

Silpalatha,G. and Nagaraju,C. (2018). Sunflower Seeds Classification by using GLBP. i-manager’s Journal on Image Processing, 5(1), 20-24. https://doi.org/10.26634/jip.5.1.14284

Abstract

In India sixty percentage of the people are depending on Agriculture. The product of the Agriculture depends on the quality of the seeds. Most of the farmers are unaware of what seeds they are using. They may use defected seeds in their fields. To identify the defective seeds, many techniques have been existed. Among those techniques, LBP and its derivatives are good to classify the defected seeds. However, it fails for low contrasted seed images. To overcome these limitations, in this paper GLBP technique is proposed. In this proposed technique, noise pixel, edge pixel, and intensity value of gray level are used to classify the defected seeds. The experimental results in the form of table and graphs show that the proposed method produced better results than the existing systems.

References

[1]. Abdesselam, A. (2013). Improving local binary patterns techniques by using edge information. Lecture Notes on Software Engineering, 1(4), 360-363.

[2]. Maheswari, C., & Vardhan, D. V. (2015). DRLBP based Face-Recognition. International Journal of Innovative Research in Computer and Communication Engineering (NC'e-TIDES -15), 3(4),203-210.

[3]. Mbilinyi, M., & Nyoni, T. (2000). Agricultural and livestock policy, 1997. Dar es Salaam: Rural Food Security Policy and Development Group. Institute of Development Studies.

[4]. Nagaraja, S., & Prabhakar, C. J. (2014). Extraction of curvelet based RLBP features for representation of facial expression. In Contemporar y Computing and Informatics (IC3I), 2014 International Conference on (pp. 845-850). IEEE.

[5]. Nagaraju, C., Sharadamani, D., Maheswari, C., & Vardhan, D. V. (2015). Evaluation of LBP-based facial emotions recognition techniques to make consistent decisions. International Journal of Pattern Recognition and Artificial Intelligence, 29(06), 1556008.

[6]. RLDC (2010). Growing Sunflower for Employment, Dodoma, Tanzania.

[7]. RLDC (2011). Action learning among Sunflower Oil Processors for systematic change.

[8]. Shan, C., Gong, S., & McOwan, P. W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27(6), 803-816.

[9]. URT (2006). Country Profile of the United Republic of Tanzania (Agricultural Trade) Department of Agriculture.

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Survey Paper

A Survey of Techniques for License Plate Detection and Recognition

Athul Suresh Nair* , Sabitha Raju, K. J. Harikrishnan*, Akhil Mathew****

,- UG Scholar, Department of Computer Science and Engineering, Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India.

Assistant Professor, Department of Computer Science and Engineering, Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India.

Nair, A.S., Raju,S., Harikrishnan,K.J., and Mathew,A. (2018). Survey of Techniques For License Plate Detection and Recognition. i-manager’s Journal on Image Processing, 5(1), 25-32. https://doi.org/10.26634/jip.5.1.13984

Abstract

This paper aims to comprehensively map out developments in Image Processing and Machine Vision and their implementation in the application of License Plate Recognition. This paper examines the implementation of new technologies in both the plate detection, and number identification aspects of the process. The paper appreciates the advancements made in the methodology for the application, and underlines the resulting accuracy and finesse of the system. All methods considered are from papers published 2010 onwards.

References

[1]. Automatic License Plate Recognition (n.d). License Plate Recognition Applications, Retrievd from http://www.platerecognition.info/index.htm

[2]. Badr, A., Abdelwahab, M. M., Thabet, A. M., & Abdelsadek, A. M. (2011). Automatic number plate recognition system. Annals of the University of Craiova- Mathematics and Computer Science Series, 38(1), 62- 71.

[3]. Chengqun, Q. (2013). Design of Automobile License Plate Recognition System Based on MATLAB and Fuzzy PID. In Measuring Technology and Mechatronics Automation (ICMTMA), 2013 Fifth International Conference on (pp. 438-441). IEEE.

[4]. Chhabra, S., Jain, H., & Saini, S. (2016). FPGA based hardware implementation of automatic vehicle license plate detection system. In Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on (pp. 1181-1187). IEEE.

[5]. Choudhury, A., & Negi, A. (2016). A new zone based algorithm for detection of license plate from Indian vehicle. In Parallel, Distributed and Grid Computing (PDGC), 2016 Fourth International Conference on (pp. 370-374). IEEE.

[6]. Davis, A. M., & Arunvinodh, C. (2015). Automatic license plate detection using vertical edge detection method. In Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on (pp. 1-6). IEEE.

[7]. Davix, X. A., Christopher, C. S., & Christine, S. S. (2017). License plate detection using channel scale space and color based detection method. In Circuits and Systems (ICCS), 2017 IEEE International Conference on (pp. 82-86). IEEE.

[8]. Elbamby, A., Hemayed, E. E., Helal, D., & Rehan, M. (2016). Real-time automatic multi-style license plate detection in videos. In Computer Engineering th Conference (ICENCO), 2016 12 International (pp. 148- 153). IEEE.

[9]. Farooq, U., Marrakchi, Z., & Mehrez, H. (2012). FPGA architectures: An overview. In Tree-based Heterogeneous FPGA Architectures (pp. 7-48). Springer, New York, NY.

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[12]. Palekar, R. R., Parab, S. U., Parikh, D. P., & Kamble, V. N. (2017). Real time license plate detection using OpenCV and tesseract. In Communication and Signal Processing (ICCSP), 2017 International Conference on (pp. 2111-2115). IEEE.

[13]. Parasuraman, K., & Kumar, P. V. (2010). An efficient method for indian vehicle license plate extraction and character segmentation. In IEEE International Conference on Computational Intelligence and Computing Research (Vol. 18), pp.1-4.

[14]. Paris, S., Kornprobst, P., Tumblin, J., & Durand, F. (2007). A gentle introduction to bilateral filtering and its applications. In ACM SIGGRAPH 2007 (p. 1). ACM.

[15]. Prakash, A. R., Jagannath, M., Adalarasu, K., & Babu, G. M. (2017). Vehicle license plate detection and recognition using non-blind image de-blurring algorithm. In Nextgen Electronic Technologies: Silicon to Software (ICNETS2), 2017 International Conference on (pp. 46-49). IEEE.

[16]. Qi, L., Dong, J., Liu, Y., Duan, Y., & Zhang, C. (2008). Removing color stain on vehicle license plates based on Photometric Stereo and a Hole-Filling Algorithm. In Image and Signal Processing, 2008. CISP'08. Congress on (Vol. 3, pp. 394-397). IEEE.

[17]. Rashid, A. E. (2013). A fast algorithm for license plate detection. In Signal Processing Image Processing & Pattern Recognition (ICSIPR), 2013 International Conference on (pp. 44-48). IEEE.

[18]. Soojey, D., Kamat, S., Patil, V., Patil, M., & Patil, P. (2015). Use of horizontal and vertical edge processing technique to improve number plate detection. IJRET: International Journal of Research in Engineering and Technology, 5(12), 286-292.

[19]. Subha, and Sudha. (2014). An efficient way of detecting a numbers in car license plate using Genetic Algorithms. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2, 17-22.

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[22]. Yang, R., Yin, H., & Chen, X. (2015). License plate detection based on Sparse Auto-Encoder. In th Computational Intelligence and Design (ISCID), 2015 8 International Symposium on (Vol. 2, pp. 465-469). IEEE.

[23]. Yang, S. Y., Lu, Y. C., Chen, L. Y., & Cherng, D. C. (2011). Hardware-Accelerated Vehicle License Plate Detection at High-Definition Image. In Robot, Vision and Signal Processing (RVSP), 2011 First International Conference on (pp. 106-109). IEEE.

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Survey Paper

Flame Monitoring System of Power Station Plant - A Survey

Mamta C. Mane* , J. S. Kulkarni**

* P.G. Scholar, Department of Electronic & Telecommunication Engineering , Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

** Assistant Professor, Department of Electronic & Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

Mame,M.C., and Kulkarni,J.S.(2018). Flame Monitoring System of Power Station Plant - A Surveyi-manager’s Journal on Image Processing, 5(1), 33-37. https://doi.org/10.26634/jip.5.1.14289

Abstract

A thermal power plant plays an important role for generating the electricity. The thermal power plants require essential elements, which are fossil fuels, such as coal, oil, etc. Coal is burned to generate heat energy in the furnace. Hence, analysis of flame is very important when fuel combustion takes place in a furnace. Combustion quality plays an important role to reduce the wastage of fuel. The combustion quality of fuel is high and has the less wastage of fuel. As fossil fuels are more expensive in cost, Flame image monitoring system plays an important role in analysing the combustion quality. Flame image monitoring system involves capturing the flame image in different instants of time to check the quality of combustion with the help of Back Propagation Algorithm (BPA) and Ant Colony Optimization (ACO) technique (Sujatha et al., 2017). The quality of combustion also depends on the flame temperature. The intelligent sensors are used to monitor the flame temperature and Internet of things (IoT) is used to make the flame monitoring system smarter.

References

[1]. BBC (n.d.). Generating Electricity. Retrieved from http://www.bbc.co.uk/schools/gcsebitesize/science/ orgateway/energyresources/generatingelectricityrev4s html

[2]. Bhavani, N. P., & Sujatha, K. (2006). Intelligent Estimation of NO Emissions by Flame Monitoring In Power x Station Using Internet of Things. ARPN Journal of Engineering and Applied Sciences, 12(3), 6677-6688.

[3]. Krishnan, P. H., & Vinoth, R. (2014). Monitoring and controlling the combustion quality in thermal power plant boiler using image processing and robotic arm. In Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on (pp. 1-4). IEEE.

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[7]. Sujatha, K., Bhavani, N. P., Reddy, T. K., & Kumar, K. R. (2017). Internet of Things for flame monitoring power station boilers. In Trends in Industrial Measurement and Automation (TIMA), 2017 (pp. 1-7). IEEE.

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[9]. Sujatha, K., Pappa, N., Kumar, K. S., & Nambi, U. S. (2013). Monitoring Power Station Boilers Using ANN and Image Processing. In Advanced Materials Research (Vol. 631, pp. 1154-1159). Trans Tech Publications.

[10]. Wikipedia. (n.d.). Power Station. Retrieved from https://en.wikipedia. org/wiki/Power-Station

i-manager's Journal on Image Processing (JIP)

Current Issue Vol. 10 Issue 4

Most Read

Most Cited

Volume 5 Issue 1 January - March 2018

Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos

Harihara Santosh Dadi* , Gopala Krishna Mohan Pillutla **, Madhavi Latha Makkena ***

Dadi, H.S., Pillutla, G.K.M. and Makkena, M.L. (2018). Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos. i-manager’s Journal on Image Processing, 5(1), 1-11. https://doi.org/10.26634/jip.5.1.14612

Abstract

References

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Glaucoma detection using Image Processing

S.P Jagtap* , Pradnya A.Shinde**, Akshay S.Manmode***, Mohini B.Kalekar****, Pratik S.Talware*****

* Assistant Professor, Department of Electronics & Communication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India. **-***** UG Scholar, Department of Electronics & Communication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

Jagtap, S.P., Shinde,P.A., Manmode,A.S., Kalekar,M.B., and Talware,P.S. (2018). Glaucoma Detection Using Image Processing. i-manager’s Journal on Image Processing, 5(1), 12-19. https://doi.org/10.26634/jip.5.1.14050

Abstract

References

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Sunflower Seeds Classification by using GLBP

G. Silpalatha* , C Nagaraju**

* Lecturer, Department of Electrical and Communication Engineering, YSR Engineering College, Yogi Vemana University, Andhra Pradesh, India. ** Associate Professor and Head, Department of Computer Science and Engineering, YSR Engineering College, Yogi Vemana University, Andhra Pradesh, India.

Silpalatha,G. and Nagaraju,C. (2018). Sunflower Seeds Classification by using GLBP. i-manager’s Journal on Image Processing, 5(1), 20-24. https://doi.org/10.26634/jip.5.1.14284

Abstract

References

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A Survey of Techniques for License Plate Detection and Recognition

Athul Suresh Nair* , Sabitha Raju**, K. J. Harikrishnan***, Akhil Mathew****

*,***-**** UG Scholar, Department of Computer Science and Engineering, Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India. ** Assistant Professor, Department of Computer Science and Engineering, Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India.

Nair, A.S., Raju,S., Harikrishnan,K.J., and Mathew,A. (2018). Survey of Techniques For License Plate Detection and Recognition. i-manager’s Journal on Image Processing, 5(1), 25-32. https://doi.org/10.26634/jip.5.1.13984

Abstract

References

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Flame Monitoring System of Power Station Plant - A Survey

Mamta C. Mane* , J. S. Kulkarni**

* P.G. Scholar, Department of Electronic & Telecommunication Engineering , Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India. ** Assistant Professor, Department of Electronic & Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

Mame,M.C., and Kulkarni,J.S.(2018). Flame Monitoring System of Power Station Plant - A Surveyi-manager’s Journal on Image Processing, 5(1), 33-37. https://doi.org/10.26634/jip.5.1.14289

Abstract

References

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Harihara Santosh Dadi* , Gopala Krishna Mohan Pillutla , Madhavi Latha Makkena *

S.P Jagtap* , Pradnya A.Shinde, Akshay S.Manmode, Mohini B.Kalekar, Pratik S.Talware

* Assistant Professor, Department of Electronics & Communication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

-*** UG Scholar, Department of Electronics & Communication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

* Lecturer, Department of Electrical and Communication Engineering, YSR Engineering College, Yogi Vemana University, Andhra Pradesh, India.

** Associate Professor and Head, Department of Computer Science and Engineering, YSR Engineering College, Yogi Vemana University, Andhra Pradesh, India.

Athul Suresh Nair* , Sabitha Raju, K. J. Harikrishnan*, Akhil Mathew****

,- UG Scholar, Department of Computer Science and Engineering, Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India.

Assistant Professor, Department of Computer Science and Engineering, Viswajyothi College of Engineering and Technology, Vazhakulam, Kerala, India.

* P.G. Scholar, Department of Electronic & Telecommunication Engineering , Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.

** Assistant Professor, Department of Electronic & Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India.