Flame Monitoring System of Power Station Plant - A Survey

Volume 5 Issue 1 January - March 2018

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.

A Survey of Techniques for License Plate Detection and Recognition

Volume 5 Issue 1 January - March 2018

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.

Sunflower Seeds Classification by using GLBP

Volume 5 Issue 1 January - March 2018

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.

Glaucoma detection using Image Processing

Volume 5 Issue 1 January - March 2018

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.

Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos

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.

Histogram Based Face Recognition

Volume 4 Issue 4 October - December 2017

Research Paper

Histogram Based Face Recognition

Ch. Srinivasa Reddy*, G.Ravi Kumar**, S. Chinni Krishna Reddy***

*-** Assistant Professor, Vignan's Institute of Information Technology, Andhra Pradesh, India.

*** PG Scholar, Vignan's Institute of Information Technology, Andhra Pradesh, India.

Reddy, S., Kumar, G.R. and Reddy, C.K. (2017). Histogram Based Face Recognition. i-manager’s Journal on Image Processing, 4(4), 31-35. https://doi.org/10.26634/jip.4.4.14162

Abstract

Human face conveys more information about identification, expression, and emotions of a person. In today's world every individual in the society wants to be more secure from unauthorized authentication. In order to provide more security, “Facial Recognition” has come into the picture and lead a most challenging role of detecting the face with more accurate results without any false identities. To increase the efficiency of the face recognition, histogram based facial recognition is chosen, where a face region is fragmented into a number of regions and histogram values are extracted and they are linked together into a single vector. This vector is compared for the similarities between the facial images and provides a most efficient outcome.

Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems

Volume 4 Issue 4 October - December 2017

Research Paper

Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems

Sri Harsha*, S. Phani Kumar**

* PG Scholar, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

** Professor and Head, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

Harsha, A.S. and Kumar, S.P. (2017). Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems. i-manager’s Journal on Image Processing, 4(4), 22-30. https://doi.org/10.26634/jip.4.4.14161

Abstract

Accuracy is one of the major concerns for any face recognition algorithm. A good preprocessing technique can increase the accuracy of a face recognition system. For this purpose, the authors present a preprocessing method for face recognition, which is helpful for training under-sampled images. For this purpose, different preprocessing techniques are taken and those methods are used during training and the resultant images are added back to the training set. Each individual preprocessing technique is helpful to tackle different conditions like lighting, disguise, etc. The training will be done using a deep convolutional neural network and a Voting Classifier. Further the results of the existing preprocessing methods are compared with the proposed model on LFW dataset. They have also used a technique to increase the accuracy of the system.