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Barman, Bandana
- Finger Print Recognition by Background Subtraction and Image
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PDF Views:127
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, Dist. Nadia, West Bengal, IN
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, Dist. Nadia, West Bengal, IN
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Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 15 (2016), Pagination: 54-60Abstract
The fingerprint identification based on Image enhancement technique is essential for crime scene investigation, authentication of a person. The most challenging fields of computer aided design is to identify a person by his or her fingerprint. In this paper, the quality of each image in the input sequence is assessed and a clear fingerprint is selected from such a sequence for subsequent recognition. After preprocessing, an effective fingerprint image is extracted from the original image. Thereafter, features are extracted from image and those features are analyzed to match with a reference image feature. For this, an algorithm is developed and coded in MATLAB (R2015a).Keywords
Finger Print, Image Analysis, Gamma Correction, Image Enhancement.Full Text
References
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- Maio, D. and Maltoni, D., Direct gray-scale minutiae detection in fingerprints, IEEE Trans.Pattern Anal. and Machine Intell,Vol. 19(1), pp. 27-40, 1997.
- Jain, A.K., Hong, L., and Bolle, R, On-Line Fingerprint Verification, IEEE Trans. On Pattern Anal and Machine Intell, Vol. 19(4), pp. 302-314, 1997.
- Coetzee, L. and Botha, E. C., Fingerprint Recognition in Low Quality Images, Pattern Recognition, Vol. 26, No. 10, pp. 1441-1460, 1993.
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- Hong, L., Automatic Personal Identification Using Fingerprints, Ph.D. Thesis, 1998.
- Germain, R., Califano, A., and Colville, S., Fingerprint matching using transformation parameter clustering, IEEE Computational Science and Engineering, Vol. 4, No. 4, pp. 42–49, 1997.
- Sudiro, S. A., Paindavoine, M., and Kusuma, T. M., Simple Fingerprint Minutiae Extraction Algorithm Using Crossing Number On Valley Structure, Automatic Identification AdvancedTechnologies, IEEE Workshop on, Alghero, Italy, DOI: 10.1109/AUTOID.2007.380590, 2007.
- Parra, P., Fingerprint minutiae extraction and matching for identification procedure, University of California, San Diego La Jolla, CA (2004): 92093-0443, 2004.
- Zaeri, N., Minutiae-based Fingerprint Extraction and Recognition, Biometrics, Jucheng Yang (Ed.), ISBN: 978-953-307-618-8, InTech, http://www.intechopen.com/ bo oks/biomet r ics /minut iae-base dfingerprintextraction-and-recognition, 2011.
- Shin, J. H., Hwang, H. Y., and Chien, S. I., Minutiae Extraction from Fingerprint Images Using Run-Length Code, Proceedings of ISMIS 2003: Foundations of Intelligent Systems, pp. 577-584, 2003.
- Fronthaler, H., Kollreider, K., and Bigun, J., Local features for enhancement and minutiae extraction in fingerprints, IEEE Trans Image Process. Vol. 17(3), pp. 354-63, DOI: 10.1109/TIP.2007.916155, 2008.
- Singh, B., and Singh, I., Fingerprint Minutiae Extraction and Compression Using LZW Algorithm, International Journal for Scientific Research& Development| Vol. 2, Issue 07, ISSN (online): 2321-0613, 2014.
- Pawar, S., Ghodke, A., Gaikwad, B. P., and Wakhude, G. P., A Survey of Minutiae Extraction from Various Fingerprint Images, IJARCSSE, Vol. 6(6), pp. 169-173, 2016.
- Singh, I. and Sharma, R., A Survey on Fingerprint Minutiae Extraction, International Journal of Advance Research, Ideas and Innovations in Technology, Vol. 3(3), pp. 264-267, 2017.
- Construction and Comparison of Gene Regulatory Networks of Human Hiv-1 VPR Microarray Datasets by Radial Basis Neural Network Approach
Abstract Views :343 |
PDF Views:115
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, Dist. Nadia, West Bengal, IN
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, Dist. Nadia, West Bengal, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 15 (2016), Pagination: 61-69Abstract
Gene Regulatory Network (GRN) construction by using neural network approach is very important and useful approach for analyzing microarray gene expression microarray datasets. The human HIV-1 Vpr mutant microarray time series gene expression value carries the experimentally validated interaction records. Firstly, the subtractive clustering approach is used to cluster the microarray data. Secondly, GRN is constructed within cluster centers of HIV-1 Vpr mutant dataset using Radial Basis Neural Network approach. The optimized output of genetic network is found using genetic algorithm. Then the influence of range of cluster centers of data clusters and also GRN outputs are compared. It is found that proximity of gene expressed values in wild type cell line HIV-1 is higher than other two HIV-1 Vpr mutants. In this paper, the nature of network output functions are also identified.Keywords
AIDS, HIV-1 Vpr Mutants, Time Series Microarray Data, Subtractive Clustering, Genetic Network, Radial Basis Neural Network, Genetic Algorithm.Full Text
References
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- Barman, B. and Mukhopadhyay, A., Construction of GA-optimized Radial Basis Neural Network from HIV-1 Vpr Mutant Microarray Gene Expression Data, Proceedings of International Conference on Computational Intelligence: Modeling, Techniques and Applications (CIMTA 2013),Kalyani, India, Procedia Technology, Vol. 10, pp. 450-456, 2013.
- Barman, B.,Biswas, P. and Mukhopadhyay, A., Comparison of gene regulatory networks usingadaptive neural network and selforganising map approach over Huh7 hepatoma cell microarray datamatrix, International Journal of Bio-Inspired Computation, Vol. 8, No.4, pp. 240-247, 2016.
- Detection of Moving Object Using Morphological Filters
Abstract Views :313 |
PDF Views:103
Authors
Affiliations
1 Department of Electronics & Communication Engineering, Bengal College of Engineering. & Technology, Durgapur, IN
2 Department of Electronics & Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia,, IN
1 Department of Electronics & Communication Engineering, Bengal College of Engineering. & Technology, Durgapur, IN
2 Department of Electronics & Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia,, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 16 (2017), Pagination: 36-45Abstract
In this paper, novel morphological filters are developed under the scope of traffic system in India is proposed. The algorithms of three filters are developed and implemented with their proper coding using Matlab (R2017a) software to detect the moving objects from CCTV video signal. For this aim, three filters are designed with gaining concepts of linear filters and also non-linear operators i.e., morphological operators. Noise reducing is also important to identify or detect a moving object. As the most of traffic videos contain background images and also different noise signals, it is necessary to minimize or to eliminate noise by subtracting background images from the images of traffic video. After detecting moving object using three morphological filters developed, PSNR and SNR values are also calculated for identified object to get the best filter designed. It is seen from the result, that moving object i.e., only white car detected after removing noise and applying median filter followed by morphological filter on background subtracted image, gives highest PSNR and SNR values.Keywords
Morphological Filters, Binary Erosion and Dilation, Median Filter, Mean Filter, MATLAB Simulation.Full Text
References
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- Metabolic Pathway of Hereditary Cancer Disease from PPI-network of DEGS Detected using Mean-of-Mean Method
Abstract Views :356 |
PDF Views:151
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani-741235, Nadia, West Bengal, IN
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani-741235, Nadia, West Bengal, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 19 (2020), Pagination: 64-80Abstract
Uncontrolled growth of cells often results Cancer disease in human body. When it is eventually transmitted throughout the generations in a family, it is referred to as hereditary diseases. Metabolic pathway explains several chemical reactions occurred for growth of a disease and KEGG pathway analysis identifies key genes involved with that disease. At first, differentially expressed genes (DEGs) are detected from cancer gene microarray time series datasets using a new and simple method, Mean of Mean (MoM). The MoM concept is developed from Gregor Johann Mendel's First Law of Heredity or Segregation rule of heredity. Highly expressed (HG) and lowly expressed (LG) genes in two different groups of microarray dataset are identified first using MoM. Then DEGs are found by implementing intersection operation between HG and LG genes. Performance of MoM method is analyzed by Support Vector Machine classifiers (SVMs) on some binary class cancer microarray data samples. Then all results are compared with the performance of other statistical parametric and nonparametric hypothetic tests. It is noticed that performance ofMoMis better than other statistical methods in almost all data sets. Finally, Protein-Protein Interaction Networks (PPINs) are constructed within identified DEGs using web based tool. Lastly, KEGG pathway analysis is performed for all proteins involved in PPINs to obtain list of key genes for growth of cancer disease.Keywords
Hereditary Disease, Cancer, Mean of Mean (MoM), DEGs, SVM Classifier, PPI-Networks, Metabolic Pathway, KEGG Pathway.Full Text
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