Low Level Algorithms in Processing Foot Print Images
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2016 by IJCTT Journal|
|Year of Publication : 2016|
|Authors : Osisanwo F.Y, Adetunmbi A.O|
|DOI : 10.14445/22312803/IJCTT-V39P112|
Osisanwo F.Y, Adetunmbi A.O "Low Level Algorithms in Processing Foot Print Images". International Journal of Computer Trends and Technology (IJCTT) V39(2):66-71, September 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
In person identification possibly at crime scene investigation, accident victims identification or any form of biometric verification or authentication that involves the use of foot print, divers foot prints images are acquired, stored and scrutinized by extracting features that can identify persons base on their uniqueness. Various modus operandi are needed to achieve this desire, starting from noise removal to segmentation then extraction of important features, different algorithms are involved at each of this stage. This research looks at using low level algorithms for processing foot print images for the purpose of person identification.
 A.O.Adetunmbi and F.YOsisanwo (2013) “Crime Suspect Identification System Based on Footprints”, Proceedings of 2013 IEEE international Conference on Emerging and Sustainable Technologies for Power and ICT in a Developing Society, pages 89-92.
 S. HDwayne. (2000) “Footwear, the Missed evidence”, CLPE Lead Latent Print Examiner Scottsdale Police Crime Lab retrieved on August 04, 2011 from http:// www.staggspublishing.com/footwear.html
 G. Hoffmann. (2002) “Gaussian Filter” retrieved on July 17, 2014 from http://www.fho-emden.de/~hoffmann
 PKamboj. and V.Rani. (2013). “A Brief Study of Various Noise Model and Filtering Techniques” Journal of Global Research in Computer Science, Vol 4 No 4 pages 166- 171.
 R. R. Mishra and Y.Saraf. (2006) “Algorithms for Image Segmentation” Thesis submitted to Birla Institute of Technology and Science, Pilani, Rajasthan
 F.Y. Osisanwo, A. O. Adetunmbi and B.K.Alese. (2014) “Barefoot Morphology: A Person Unique Feature for Forensic Identification” Conference Proceedings of the 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014)
 Seamann, T. (2003). Digital Image Processing Using Local Segmentation. Ph.D Thesis, School of Computer Science and Software Engineering, Faculty of Information Technology Monash University , Australia
 Silver, B. (2000). An Introduction to Digital Image Processing. Cognex Corporation, Modular Vision System Division Natick
 Singh K. K. and Singh A. (2010) “A Study Of Image Segmentation Algorithms For Different Types Of Images” International Journal of Computer Science Issues, Vol. 7, Issue 5.
 Verma R and Ali J. (2013) “A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques”, published in the International Journal of Advanced Research in Computer Science and Software Engineering, vol 3 No 10, pages 617-622.
 Ian T., Young, ,Jan J., Gerbrands and Lucas J. Van Vliet (1998). “Fundamentals of Image Processing.” Delft University of Technology, Netherlands.
 Shih F. (2010) “Image Processing and Pattern Recognition”, Institute of Electrical Electronic Engineers, John Wiley & Sons, Inc., Hoboken, New Jersey.
low level algorithm, foot print images, noise removal, segmentation, feature extraction.