Josef Strom Bartunek
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Josef Strom Bartunek has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Ingvar Claesson
Department of Signal Processing, School of Engineering, Blekinge Institute of Technology, Box 520, SE-372 25 Ronneby, Sweden
2 shared publications - Jorgen Nordberg
Department of Signal Processing, School of Engineering, Blekinge Institute of Technology, Box 520, SE-372 25 Ronneby, Sweden
2 shared publications - Mikael Nilsson
Department of Signal Processing, School of Engineering, Blekinge Institute of Technology, Box 520, SE-372 25 Ronneby, Sweden
2 shared publications Benny Sallberg
Department of Electrical Engineering, School of Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
1 shared publicationIngvar Claesson
Department of Electrical Engineering, School of Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
1 shared publicationMikael Nilsson
Centre for Mathematical Sciences, Faculty of Engineering LTH, University of Lund, Lund, Sweden
1 shared publication
Latest Publications
- 21 September 2012
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 22 (2) , 644-656
Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data
This article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of the updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases.
- 21 September 2012
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 22 (2) , 644-656
Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data
This article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of the updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases.
- 1 January 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Neural Network based Minutiae Extraction from Skeletonized Fingerprints
Human fingerprints are rich in details denoted minutiae. In this paper a method of minutiae extraction from fingerprint skeletons is described. To identify the different shapes and types of minutiae a neural network is trained to work as a classifier. The proposed neural network is applied throughout the fingerprint skeleton to locate various minutiae. A scheme to speed up the process is also presented. Extracted minutiae can then be used as identification marks for automatic fingerprint matching
- 1 January 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Neural Network based Minutiae Extraction from Skeletonized Fingerprints
Human fingerprints are rich in details denoted minutiae. In this paper a method of minutiae extraction from fingerprint skeletons is described. To identify the different shapes and types of minutiae a neural network is trained to work as a classifier. The proposed neural network is applied throughout the fingerprint skeleton to locate various minutiae. A scheme to speed up the process is also presented. Extracted minutiae can then be used as identification marks for automatic fingerprint matching
- 1 January 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10586393,p. 598-602
Adaptive Fingerprint Binarization by Frequency Domain Analysis
This paper presents a new approach for fingerprint enhancement by using directional filters and binarization. A straightforward method for automatically tuning the size of local area is obtained by analyzing entire fingerprint image in the frequency domain. Hence, the algorithm will adjust adaptively to the local area of the fingerprint image, independent on the characteristics of the fingerprint sensor or the physical appearance of the fingerprints. Frequency analysis is carried out in the local areas to design directional filters. Experimental results are presented.
- 1 January 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10586393,p. 598-602
Adaptive Fingerprint Binarization by Frequency Domain Analysis
This paper presents a new approach for fingerprint enhancement by using directional filters and binarization. A straightforward method for automatically tuning the size of local area is obtained by analyzing entire fingerprint image in the frequency domain. Hence, the algorithm will adjust adaptively to the local area of the fingerprint image, independent on the characteristics of the fingerprint sensor or the physical appearance of the fingerprints. Frequency analysis is carried out in the local areas to design directional filters. Experimental results are presented.