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Kanagalakshmi, K.
- Frequency Domain Enhancement Filters for Fingerprint Image:A Performance Evaluation
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Affiliations
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
Source
Digital Image Processing, Vol 3, No 16 (2011), Pagination: 1043-1046Abstract
Filtering and Image Enhancements are the primary need of the automatic identification and authentication system. This paper aims to review and evaluate the frequency domain enhancement techniques: Ideal Low Pass filtering (ILPF), Butterworth Low Pass Filtering (BLPF), Band Pass Filtering (BPF), and Log-Gabor Filtering. Experimental results show the performance measures based on Peak-Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) and also Standard Deviation between original and enhanced image.Keywords
Band-Pass Filter, Butterworth Filter, Domain, Log-Gabor, Low-Pass.
- Noise Elimination in Fingerprint Image Using Median Filter
Abstract Views :115 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore (DT), Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore (DT), Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
Source
International Journal of Advanced Networking and Applications, Vol 2, No 6 (2011), Pagination: 950-955Abstract
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process. The quality of the fingerprint is obtained by the noise free image. To get a noise-free fingerprint image, the pre-processing techniques are applied on image. In this paper, we described the finger print classifications, characteristics and pre-processing techniques. We applied the histogram on 256 gray scale finger print image with the default threshold value;then the histogram-equalized image is obtained. Next, histogram-equalized image is given under the binarization process. Finally the binarized fingerprint image is filtered with the implementation of the Median filtering technique in order to produce the noise free image. The comparison of the median filtered image with the original noisy image shows the depth of the noise spread in the original image. The experimental result shows the noise rate which was eliminated in the input fingerprint image and quality of the filtered image using the Statistical-Correlation tool.Keywords
Authentication, Binarization, Histogram, Identification, Median Filter.Full Text
- An Empirical Analysis of Frequency Domain High Pass Filters on Various Types of Noises
Abstract Views :188 |
PDF Views:4
Authors
Affiliations
1 PG and Research Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
1 PG and Research Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
Source
Digital Image Processing, Vol 10, No 1 (2018), Pagination: 12-14Abstract
Enhancing the pictorial information for human interpretation is always been a challenging task in digital image processing. Preprocessing is used to remove the unwanted data in digital images. Frequency domain techniques are applied on Fast Fourier transformations of an image. High pass filters are often used to sharpen the digitized image and improve minutiae details. It is frequently applied on the fingerprint images. One of the main objectives of this work is to find the suitable high pass filter for desired noise type. Quality of the images is assessed with image quality metrics like PSNR, MSE and MAE.Keywords
Ideal, Butterworth, Gaussian, Filter, High Pass, Noise.References
- Makandar, Aziz, and Bhagirathi Halalli. "Image enhancement techniques using highpass and lowpass filters." International Journal of Computer Applications, Volume 109 – No. 14, January 2015.
- Shaikh, Md Shahnawaz, Ankita Choudhry, and Rakhi Wadhwani. "Analysis of Digital Image Filters in Frequency Domain." International Journal of Computer Applications, Volume 140 – No.6, April 2016.
- Zawaideh, Farah H., Qais M. Yousef, and Firas H. Zawaideh. "Comparison between Butterworth and Gaussian High-pass Filters using an Enhanced Method." international journal of computer science and network security, Vol.No.17, issue. 7 P.No: 113-117, July 2017.
- Hwang, Jae Jeong, and Kang Hyeon Rhee. "Gaussian filtering detection based on features of residuals in image forensics." In Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2016 IEEE RIVF International Conference on, pp. 153-157. IEEE, 2016.
- Tholeti, Thulasi, Priyanka Ganesh, and Pallavi Ramanujam. "Frequency domain filtering techniques of halftone images." In Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on, pp. 427-430. IEEE, 2015.
- Kaur, Jappreet, Manpreet Kaur, Poonamdeep Kaur, and Manpreet Kaur. "Comparative analysis of image denoising techniques." International journal of Emerging Technology and Advanced engineering 2, no. 6 (2012): 296-298.
- Dewangan, Swati, and Anup Kumar Sharma. "Image Smoothening and Sharpening using Frequency Domain Filtering Technique.", International Journal of Emerging Technologies in Engineering Research (IJETER) Volume 5, Issue 4, pp:169-174, April (2017).
- Umbaugh, S. E. 1998, Computer Vision and Image Processing, Prentice Hall PTR, New Jersey.
- Narinder Kaur, Seema Baghla, Sunil Kumar, ”A Review: Image Enhancement and Its Various Techniques”, International Journal of Advances in Science Engineering and Technology, ISSN: 2321-9009 Volume- 3, Issue-3, July-2015.
- Hasinoff, Samuel W., Frédo Durand, and William T. Freeman. "Noise-optimal capture for high dynamic range photography." In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 553-560. IEEE, 2010.
- Chandra, E., and K. Kanagalakshmi. "Frequency Domain Enhancement Filters for Fingerprint Image: A Performance Evaluation." Digital Image Processing 3, no. 16 (2011): 1043-1046.
- Image Noise and Filtering Techniques-A Survey
Abstract Views :174 |
PDF Views:4
Authors
Affiliations
1 PG & Research Department Computer Science, Nehru Arts and Science College, Coimbatore, IN
1 PG & Research Department Computer Science, Nehru Arts and Science College, Coimbatore, IN
Source
Digital Image Processing, Vol 10, No 1 (2018), Pagination: 15-17Abstract
An image is a collection of pixels, which can be acquired from the different types of sources. The heterogeneous image sources have high dense noise, this cause several performance related issues in which the image associated with. So, every application under image processing needs an effective technique to perform noise removal on digital images. Image de-noising is an essential step that should be performed before any image analysis process begins. Image noise reduction involves the manipulation of an image to produce a high quality image. This paper gives a survey on recent techniques of image filters. Finally the merits and demerits of existing techniques are identified from the comprehensive study.References
- . Talebi, Hossein, and Peyman Milanfar. "Global image denoising." IEEE Transactions on Image Processing 23, no. 2 (2014): 755-768.
- . Kaur, Sukhjinder. "Noise types and various removal techniques." International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 4 (2015).
- . Vijaykumar, V. R., P. T. Vanathi, and P. Kanagasabapathy. "Fast and efficient algorithm to remove gaussian noise in digital images." IAENG International Journal of Computer Science 37, no. 1 (2010): 300-302.
- . Rahman, Tanzila, Mohammad Reduanul Haque, Liton Jude Rozario, and Mohammad Shorif Uddin. "Gaussian noise reduction in digital images using a modified fuzzy filter." In Computer and Information Technology (ICCIT), 2014 17th International Conference on, pp. 217-222. IEEE, 2014.
- . Roy, Amarjit, Joyeeta Singha, Salam Shuleenda Devi, and Rabul Hussain Laskar. "Impulse noise removal using SVM classification based fuzzy filter from gray scale images." Signal Processing 128 (2016): 262-273.
- . Yang, Jian, Jingfan Fan, Danni Ai, Xuehu Wang, Yongchang Zheng, Songyuan Tang, and Yongtian Wang. "Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image." Neurocomputing 195 (2016): 88-95.
- . Singh, Prabhishek, and Raj Shree. "A comparative study to Noise models and Image restoration Techniques." International Journal of Computer Applications 149, no. 1 (2016).
- . Thakur, Kirti V., Omkar H. Damodare, and Ashok M. Sapkal. "Poisson Noise Reducing Bilateral Filter." Procedia Computer Science 79 (2016): 861-865.
- . Chinnasamy, Gokilavani, and S. Vanitha. "Implementation and Comparison of Various Filters for the Removal of Fractional Brownian Motion noise in Brain MRI Images." IJTET (International Journal for trends in Engineering & Technology), ISSN (2015): 2349-9303.
- . Okada, Mami, Tomoe Ishikawa, and Yuji Ikegaya. "A Computationally Efficient Filter for Reducing Shot Noise in Low S/N Data." PloS one 11, no. 6 (2016): e0157595
- An Analysis of Biometric Transformative Schemes
Abstract Views :171 |
PDF Views:4
Authors
Affiliations
1 PG & Research Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamilnadu, IN
1 PG & Research Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamilnadu, IN
Source
Biometrics and Bioinformatics, Vol 10, No 1 (2018), Pagination: 6-7Abstract
There is a complexity in creating a secure Biometric system is protecting the templates of the user stored in Central Database. If there is any Compromise in saving templates then it will cause serious problem in security. The Cancellable Biometric is one of the safest methods used to store the template without duplication. In order to avoid the theft of iris biometric pattern, it is very must to design irrevocable and non- invertible transformation to create cancellable biometric template. In cancellable biometric many transformation techniques are used. In this attempt, two transformation techniques, i.e., Bio-hashing and Huffman Encoding with DCT are taken for comparative study in creating cancellable iris Biometric.Keywords
Biometrics, Cancelable Biometric Templates, BioHashing, Huffman Encoding & DCT.References
- Dr.E.Chandra,& Ms.K.Kanagalakshmi ,”Cancelable Biometric Template Generation and Protection Schemes: a Review”., 978-1-4244-8679-3/11/©2011 pp.15-20.
- Nalini K. Ratha, and Jonathan H. Connell ,”Cancelable Iris Biometric”. IBM Watson Research Center, 978-1-4244-2175-6/08/©2008 IEEE
- Sanjay Kanade, Dijana Petrovska-Et.Al. “Cancelable Iris Biometrics and Using Error Correcting Codes to Reduce Variability in Biometric Data”. IEEE, 978-1-4244-3991-2009,pp 120-127.
- Alessandra Lumini,et.al “Improved BioHashing For Human Authentication”,Pattern Recognition,Volume 40 Issue 3,March 2007,Pages 1057-1065.
- Vishal M. Patel, Nalini K. Ratha, ET.AT..”Cancelable Biometrics: A Review”. IEEE Signal Processing Magazine, 32 (3), 53-69.
- Kenta Takahashi.”Cancelable Biometrics With Provable Security And Its Application To Finger Print Verification”. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Volume E94.A, Issue 1, pp. 233-244 (2011).
- Nalini K. Ratha, ,Sharat Chikkerur.EtAl.” Generating Cancelable Fingerprint Templates”.IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 29, NO. 4, APRIL 2007
- Emanuela Piciucco, Emanuele Maiorana,Et.Al..”Cancelable Biometrics for Finger Vein Recognition”Section of Applied Electronics, Department of Engineering, Roma, Italy 978-1-4244-2175-6/08/©2008 IEEE
- Bismita Choudhury, Patrick .Et.Al.”Cancelable Iris Biometrics Based on Data Hiding Schemes”. 2016 IEEE Student Conference on Research and Development (SCOReD) 9781-5090-2948-8/16©2016 IEEE
- L. Masek, “Recognition of Human Iris Patterns for Biometric.Identification”. The University of Western Australia, Perth, 2003.
- Y. J. Chang, Z. Wende, and T. Chen, “Biometrics-based cryptographic key generation," IEEE Interna-tionalConferenceon Multimedia and Expo, vol. 3,pp. 2203-2206, 2004
- J. Hammerle,Uhrl,E.Pschernig,A.Uhl, ”Cancelable Iris Biometrics using block Re-Mapping And Image Warping”,Springer Lecture Noted on Computer Science Information Security,Vol.5735,pp.135-142
- J.K.Pillai,V.M.Patel,R.Chellappa,N.K.Ratha,”Sectored Random Projection for Cancelable Iris Biometrics”,Proc. Of ICASSP,PP1838-1841,2010
- Rathgeb, F. Breitinger and C. Busch. J. K. Pillai, Et.Al.“ Alignment-Free Cancelable Iris Biometric Templates based on Adaptive Bloom Filters”.. In Proc. IEEE Int’l Conf. on Acoustics Speech and Signal Processing, pages 1838–1841. IEEE, 2010.