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Nachamai, M.
- Facial Recognition Approaches and Methods with Feature Extraction:A Comprehensive Review
Abstract Views :157 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, Christ University, Bangalore-560029, IN
1 Department of Computer Science, Christ University, Bangalore-560029, IN
Source
Digital Image Processing, Vol 7, No 7 (2015), Pagination: 197-204Abstract
One of the huge avenues of research in image processing is facial recognition. Automatic Facial Recognition (FR) has many applications. The paper deals with the various methods of facial recognition system. It includes the various methods of face recognition system along with feature extraction which makes the facial recognition system more versatile and robust. An attempt is made to make a complete survey of all FR systems regarding the methodology adopted along with the researches that took place, the accuracy rate of the methods and also the advantages and the disadvantages of the various methods. The paper discusses all the methods of face recognition system with or without feature extraction and expression recognition. Face recognition system has seen many new methods of development to cover as many different possibilities as possible, along with high rate of accuracy. An extensive and elaborate survey and a walk through of all those methodologies are covered along with the algorithms followed by the methods for facial recognition.Keywords
Face Recognition System, Feature Extraction, Facial Expression Recognition.- Sub-Type Discernment of Attention Deficit Hyperactive Disorder in Children using a Cluster Partitioning Algorithm
Abstract Views :112 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Christ University, Bengaluru - 560029, Karnataka, IN
1 Department of Computer Science, Christ University, Bengaluru - 560029, Karnataka, IN
Source
Indian Journal of Science and Technology, Vol 9, No 8 (2016), Pagination:Abstract
Background/Objectives: Attention deficit hyperactive disorder is one major neuropsychiatric disorder particularly found in children. This medical disorder is difficult to identify and quantify, even if done, it is very subjective as it is the discretion of the psychiatrists or parents. Methods/Statistical analysis: The most exigent task after identifying ADHD children is to find their exact deficiency of what is the category, is it a hyperactive disorder, an impulsive disorder or an attention deficit disorder. Each category insists a diverse form of treatment and training. With the MRI image data the Tr values are estimated and given for clustering, a k-means algorithm was deployed for clustering. Findings: With different distance measures k-means was able to cluster precisely the three categories from the data. The result obtained would be a very substantial data for the medical physicists and an inevitable philanthropic contribution for the children society combating against this disorder. Applications/Improvements: The method adopted is novel and concise approach to identify the type of ADHD prevalent children. The method can be further perfected and completely automated to identify the category of ADHD in children.Keywords
ADHD, Clustering, K-Means Algorithm, MRI Spectroscopy, Unsupervised Learning- Noise Removal and Filtering Techniques Used in Medical Images
Abstract Views :302 |
PDF Views:1
Authors
Nalin Kumar
1,
M. Nachamai
1
Affiliations
1 Department of Computer Science, Christ University, Bengaluru, IN
1 Department of Computer Science, Christ University, Bengaluru, IN
Source
Oriental Journal of Computer Science and Technology, Vol 10, No 1 (2017), Pagination: 103-113Abstract
Noise removal techniques have become an essential practice in medical imaging application for the study of anatomical structure and image processing of MRI medical images. To report these issues many de-noising algorithm has been developed like Weiner filter, Gaussian filter, median filter etc. In this research work is done with only three of the above filters which are already mentioned were successfully used in medical imaging. The most commonly affected noises in medical MRI image are Salt and Pepper, Speckle, Gaussian and Poisson noise. The medical images taken for comparison include MRI images, in gray scale and RGB. The performances of these algorithms are examined for various noise types which are salt-and-pepper, Poisson, speckle, blurred and Gaussian Noise. The evaluation of these algorithms is done by the measures of the image file size, histogram and clarity scale of the images. The median filter performs better for removing salt-and-pepper noise and Poisson Noise for images in gray scale, and Weiner filter performs better for removing Speckle and Gaussian Noise and Gaussian filter for the Blurred Noise as suggested in the experimental results.Keywords
Weiner Filter, Median Filter, Gaussian Filter, Speckle Noise, Gaussian Noise, Salt and Pepper Noise, Blurred Noise, Poisson Noise.References
- Speckle Noise Reduction in Ultrasound Images- A Review Shruthi B, S Renukalatha, M SiddappaDept. of Computer Science and Engg. Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India.
- Comparative Study of Fractional Filters for Alzheimer Disease Detection on MRI Images Samar M. Ismaila , Ahmed G. Radwanb,c, Ahmed H. Madianc,d, Mohamed F. Abu-ElYazeede a Faculty of IET, German University in Cairo (GUC), Egypt. b Dept. of Engineering Mathematics and Physics, Cairo University, Egypt. c NISC Research Center, Nile University, Cairo, Egypt. d Radiation Engineering Dept., NCRRT, Egyptian Atomic Energy Authority. e Electronics and comm. Eng. Dept., Cairo University, Egypt.
- Charles Boncelet (2005).”Image Noise Models”. in Alan C.Bovik. Handbook of Image and Video Processing.
- Research on Removing Noise in Medical Image Based on Median Filter Method NING Chun-yu 1.2, LIU Shu-fen’. QU Ming ‘1. Department ofComputer Science and Technology, Jilin University, Changchun, 130012, China; 2. School ofLife Science and Technology, Changchun University ofScience and Technology, Changchun, 130022, China.
- Enhancement Methods For Reduction Of Speckle In Ultrasound B- Mode Images 1 Kinita B Vandara, 2 DR. G. R. Kulkarni 1 Research Scholar, Department of Electronics and Communication, Shri J.J.T.University, vidyanagari, jhunjhunu, rajasthan 2 Principal, R.W.M.C.T’S Dnyanshree Institute of Engineering & Technology, Satara, Maharashtra.
- Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique C.Lakshmi Devasena Department of Software Systems Karpagam Univeristy, M.Hemalata Department of Software Systems Karpagam Univeristy.
- Despeckling of Medical Ultrasound ImagesOleg V. Michailovich and Allen Tannenbaum, Member, IEEE.
- References
- A Comparative Study on Approaches to Speckle Noise Reduction in Images Alenrex Maity, Anshuman Pattanaik, Santwana Sagnika, Santosh Pani School of Computer Engineering, Kalinga Institute of Industrial Technology, KIIT University, Bhubaneswar,India.
- Comparative Analysis of Noise Removal Techniquesin MRI Brain Images B.Deepa Assistant Professor, dept of ECE Jayaram College of Engineering & Technology Trichy, India. Dr.M.G. Sumithra Professor, dept of ECE Bannari Amman Institute of Technology Erode, India.mgsumithra@rediffmail.com
- Performance Evaluation Of Filters In Noise Removal Of Finger Print Image 1 Ms.K.Kanagalakshmi, 2 Dr.E.Chandra1 Doctoral Research Scholar, 2 Director, Dept. of Computer Science, DJ Academy Managerial for Excellence.
- A Comparative Analysis of Filters on Brain MRI Images Neha Jain*, D S Karaulia Department of Computer Engineering and Application National Institute of Technical Teachers Training and Research Bhopal, India.
- Noise Through Removal of High Density Salt &Pepper Noise Through Removal of High Density Salt & Pepper Noise Through Super Mean Filter for Natural Images.Shyam Lal1, Sanjeev Kumar 2 and Mahesh Chandra3 1ECE Department, Moradabad Institute of Technology, Moradabad-244001(UP), India 2,3, ECE Department, Birla Institute of Technology,Mesra,Ranchi-835215(JH),India.
- A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques Mr. Rohit Verma Dr. Jahid Ali School of Information Technology SSCIMT, Badhani, Pathankot, APJIMTC, Jalandhar, India India
- Image De-noising by Various Filters for Different Noise Pawan Patidar Research Scholar (M. Tech.), Computer Science Department, Poornima College of Engineering, Jaipur, India, Sumit Srivastava Associate Professor, Computer Science Department, Poornima College of Engineering, Jaipur (Rajasthan), India.
- Study of Noise Detection and Noise Removal Techniques in Medical Images 1 Bhausaheb Shinde Head, Department of Computer Science, R.B.N.B. College, Shrirampur. Affiliated to Pune University Maharashtra, India Shinde.bhausaheb@gmail.com 2 Dnyandeo Mhaske Principal, R.B.N.B. College, Shrirampur. Affiliated to Pune University Maharashtra, India 3 A.R. Dani Head, International Institute of Information Technology, Hinjwadi, Pune Maharashtra, India.
- A Comparative Analysis of Filters on Brain MRI Images Neha Jain*, D S Karaulia Department of Computer Engineering and Application National Institute of Technical Teachers Training and Research Bhopal, India.
- Performance Investigation of Antivirus-A Comparative Analysis
Abstract Views :157 |
PDF Views:6
Authors
Remya Thomas
1,
M. Nachamai
1
Affiliations
1 Department of Computer Science, Christ University, Bangalore, IN
1 Department of Computer Science, Christ University, Bangalore, IN
Source
Oriental Journal of Computer Science and Technology, Vol 10, No 1 (2017), Pagination: 201-206Abstract
Antivirus as name implies prevent the devices such as computers, mobiles and pen-drive from viruses. All gadgets which interact with open network are prone to virus. Virus is a malicious software program which replicates by copying its code multiple times or by infecting any computer program (like modifying the existing program) which can affect its process. Virus perform harmful task on affected host computer such as possessing on hard disk, CPU time, accessing private information etc. This paper specifies the performance of (McAFee, Avast, Avira, Bitdefender, Norton) antivirus and its effectiveness on the computer. The performance is tested based on the time acquired by each antivirus to act on a computer. The parameters used to analyze the performance are quick scan, full scan and custom scan with respect to time. Through the analysis Bitdefender performance is better than other selected antivirus.Keywords
Antivirus, Computer Scan, Virus, Performance Testing.References
- https//www.howtogeek.com/125650/htgexplainshow-antivirus-software-works/
- Sulaiman Al Amro and Ali Alkhalifah,” A Comparative Study of Virus Detection Techniques”.
- Ankush R Kakad, Siddharth G Kamble, Shrinivas S Bhuvad and Vinayak N Malavade,” Study and Comparison of Virus Detection Techniques”.
- Sarah Gordon,” Antivirus Software Testing for the New Millenium”.
- https://www.opswat.com/blog/antivirusperformancestudy-shows-diversificationkey 6. http://in.pcmag.com/antivirus-frompcma/ 37090/guide/the-best-free-antivirusprotectionof-2016
- http://thinhlong.vn/upload/download/avc_ report25.pdf
- https://www.raymond.cc/blog/test-theeffectivenessof-your-antivirus-firewall-andhipssoftware/
- https://www.sans.org/readingroom/ whitepapers/commerical/anti-virus-softwarechallengeprepared-tomorrows-malwaretoday782
- http://www.toptenreviews.com/software/ security/best-antivirus-software/