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Nilsson, Mikael
Publications (10 of 25) Show all publications
Butt, N. R., Nilsson, M., Jakobsson, A., Nordberg, M., Pettersson, A., Wallin, S. & Östmark, H. (2011). Classification of Raman Spectra to Detect Hidden Explosives. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 8(3), 517-521
Open this publication in new window or tab >>Classification of Raman Spectra to Detect Hidden Explosives
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2011 (English)In: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , ISSN 1545-598X , Vol. 8, no 3, p. 517-521Article in journal (Refereed) Published
Abstract [en]

Raman spectroscopy is a laser-based vibrational technique that can provide spectral signatures unique to a multitude of compounds. The technique is gaining widespread interest as a method for detecting hidden explosives due to its sensitivity and ease of use. In this letter, we present a computationally efficient classification scheme for accurate standoff identification of several common explosives using visible-range Raman spectroscopy. Using real measurements, we evaluate and modify a recent correlation-based approach to classify Raman spectra from various harmful and commonplace substances. The results show that the proposed approach can, at a distance of 30 m, or more, successfully classify measured Raman spectra from several explosive substances, including nitromethane, trinitrotoluene, dinitrotoluene, hydrogen peroxide, triacetone triperoxide, and ammonium nitrate.

Place, publisher, year, edition, pages
IEEE, 2011
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-7543 (URN)dx.doi.org/10.1109/LGRS.2010.2089970 (DOI)000289899000027 ()oai:bth.se:forskinfoA2EC6E52E1F1A4B0C12578AF00459A1E (Local ID)oai:bth.se:forskinfoA2EC6E52E1F1A4B0C12578AF00459A1E (Archive number)oai:bth.se:forskinfoA2EC6E52E1F1A4B0C12578AF00459A1E (OAI)
Available from: 2012-09-18 Created: 2011-06-14 Last updated: 2015-06-30Bibliographically approved
Maddala, S., Tangellapally, S. R., Bartunek, J. S. & Nilsson, M. (2011). Implementation and evaluation of NIST Biometric Image Software for fingerprint recognition. Paper presented at ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, BRC. Paper presented at ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, BRC. Vitoria: IEEE
Open this publication in new window or tab >>Implementation and evaluation of NIST Biometric Image Software for fingerprint recognition
2011 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Fingerprints are rich in details which are in the form of discontinuities in ridges known as minutiae and are unique for each person. This paper describes implementation and evaluation of an existing fingerprint recognition system in MATLAB environment. The selected system is developed by National Institute of Standards and Technology (NIST) denoted as Biometric Image Software (NBIS). The NBIS source code is written in ANSI C programming language. To be able to evaluate the algorithm in MATLAB a C language MEX-files has been used. The NBIS support both minutiae extraction and minutiae matching functions that have been employed in the evaluation. The implemented system has been tested on a Fingerprint Verification Competition (FVC) database. The results are presented as Receiver Operating Characteristics (ROC) graphs.

Place, publisher, year, edition, pages
Vitoria: IEEE, 2011
Keywords
Biometric, MEX-file, NIST Fingerprint Verification, ROC graphs
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-7069 (URN)10.1109/BRC.2011.5740672 (DOI)oai:bth.se:forskinfoBC1BBB1A1372A1DCC1257974003DE527 (Local ID)978-142448212-2 (ISBN)oai:bth.se:forskinfoBC1BBB1A1372A1DCC1257974003DE527 (Archive number)oai:bth.se:forskinfoBC1BBB1A1372A1DCC1257974003DE527 (OAI)
Conference
ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, BRC
Available from: 2012-12-18 Created: 2011-12-28 Last updated: 2015-06-30Bibliographically approved
Butt, N. R., Nilsson, M., Jakobsson, A., Nordberg, M., Pettersson, A., Wallin, S. & Östmark, H. (2010). An Improved Classification Scheme for Standoff Detection of Explosives via Raman Spectroscopy. Paper presented at 18th European Signal Processing Conference, EUSIPCO. Paper presented at 18th European Signal Processing Conference, EUSIPCO. Aalborg: Eurasip
Open this publication in new window or tab >>An Improved Classification Scheme for Standoff Detection of Explosives via Raman Spectroscopy
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2010 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Raman spectroscopy is a laser-based vibrational tech- nique that can provide spectral signatures unique to a multitude of compounds. The technique is gaining widespread interest as a method for detecting hidden explosives due to its sensitivity and ease of use. In this work, we present a computationally e±cient clas- si¯cation scheme for accurate stando® identi¯cation of several common explosives using visible-range Raman spectroscopy. Using real measurements, we evaluate and modify a recent correlation-based approach to classify Raman spectra from various both harmful and commonplace substances. The results show that the proposed approach can, at a distance of 30 me- ters, or more, successfully classify measured Raman spectra from several explosive substances, including Nitromethane, TNT, DNT, Hydrogen Peroxide, TATP and Ammonium Nitrate.

Place, publisher, year, edition, pages
Aalborg: Eurasip, 2010
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-7548 (URN)oai:bth.se:forskinfoE0EE4B0620B4A4FAC12578AF0040A583 (Local ID)oai:bth.se:forskinfoE0EE4B0620B4A4FAC12578AF0040A583 (Archive number)oai:bth.se:forskinfoE0EE4B0620B4A4FAC12578AF0040A583 (OAI)
Conference
18th European Signal Processing Conference, EUSIPCO
Available from: 2012-09-18 Created: 2011-06-14 Last updated: 2015-06-30Bibliographically approved
Maddala, S., Bartunek, J. S. & Nilsson, M. (2010). Implementation and evaluation of NIST biometric image software for fingerprint recognition. In: : . Paper presented at 3rd IEEE International Conference on Signal and Image Processing, ICSIP. Chennai: IEEE
Open this publication in new window or tab >>Implementation and evaluation of NIST biometric image software for fingerprint recognition
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Fingerprints are rich in details which are in the form of discontinuities in ridges known as minutiae and are unique for each person. This paper describes implementation and evaluation of an existing fingerprint recognition system in MATLAB environment. The selected system is developed by National Institute of Standards and Technology (NIST) denoted as Biometric Image Software (NBIS). The NBIS source code is written in ANSI C programming language. To be able to evaluate the algorithm in MATLAB a C language MEX-files has been used. The NBIS support both minutiae extraction and minutiae matching functions that have been employed in the evaluation. The implemented system has been tested on a Fingerprint Verification Competition (FVC) database. The results are presented as Receiver Operating Characteristics (ROC) graphs

Place, publisher, year, edition, pages
Chennai: IEEE, 2010
Keywords
Biometric, MEX-file, NIST fingerprint verification, ROC graphs
National Category
Signal Processing Computer Sciences
Identifiers
urn:nbn:se:bth-7504 (URN)10.1109/ICSIP.2010.5697470 (DOI)oai:bth.se:forskinfo8EA84BCE45A2CE9EC12578BE002B7015 (Local ID)978-142448594-9 (ISBN)oai:bth.se:forskinfo8EA84BCE45A2CE9EC12578BE002B7015 (Archive number)oai:bth.se:forskinfo8EA84BCE45A2CE9EC12578BE002B7015 (OAI)
External cooperation:
Conference
3rd IEEE International Conference on Signal and Image Processing, ICSIP
Available from: 2012-09-18 Created: 2011-06-29 Last updated: 2018-01-11Bibliographically approved
Nilsson, M., Gertsovich, I. & Bartunek, J. S. (2010). Mouth Open or Closed Decision for Frontal Face Images with Given Eye Locations. Paper presented at IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems. Paper presented at IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems. Washington: IEEE
Open this publication in new window or tab >>Mouth Open or Closed Decision for Frontal Face Images with Given Eye Locations
2010 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Abstract—Determination of the open/closed state of a mouth is a desired feature considering the face ISO standard. In this paper a simple score is proposed for automatic mouth open/closed decision. Landmarks around the mouth are explored in order to calculate the score. Analysis of mouth location, scale and rotation introduced by nonreflective similarity transformations utilizing prior knowledge regarding the eye locations with the accompanied Jesorsky error is presented. Further, a novel system is proposed in which a search in the four dimensions; position, scale, rotation and shape variation is combined with a discriminative classifier in order to perform alignment of the landmarks. The system is evaluated on the XM2VTS and IMM face databases indicating the feasibility of the proposed system. The evaluation is presented utilizing a cumulative distribution of the mouth open/closed score error.

Place, publisher, year, edition, pages
Washington: IEEE, 2010
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-7746 (URN)oai:bth.se:forskinfo053DB4C1F0E577C9C125778B002DB75B (Local ID)978-1-4244-7581-0 (ISBN)oai:bth.se:forskinfo053DB4C1F0E577C9C125778B002DB75B (Archive number)oai:bth.se:forskinfo053DB4C1F0E577C9C125778B002DB75B (OAI)
Conference
IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems
Available from: 2012-09-18 Created: 2010-08-26 Last updated: 2015-06-30Bibliographically approved
Aibinu, A., Iqbal, M. I., Shafie, A., Salami, M. & Nilsson, M. (2010). Vascular intersection detection in retina fundus images using a new hybrid approach. Computers in Biology and Medicine, 40(1), 81-89
Open this publication in new window or tab >>Vascular intersection detection in retina fundus images using a new hybrid approach
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2010 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 40, no 1, p. 81-89Article in journal (Refereed) Published
Abstract [en]

The use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN methods.

Place, publisher, year, edition, pages
Elsevier, 2010
Keywords
Bifurcation, Crossover, Diabetic retinopathy, Fundus image, Vascular intersection
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-7874 (URN)10.1016/j.compbiomed.2009.11.004 (DOI)000274948300009 ()oai:bth.se:forskinfo68862D594DFFE8CAC12576D4003BE976 (Local ID)oai:bth.se:forskinfo68862D594DFFE8CAC12576D4003BE976 (Archive number)oai:bth.se:forskinfo68862D594DFFE8CAC12576D4003BE976 (OAI)
Available from: 2012-09-18 Created: 2010-02-24 Last updated: 2017-12-04Bibliographically approved
Muhammad, A. S., Lavesson, N., Davidsson, P. & Nilsson, M. (2009). Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images. Paper presented at 13th International Conference on Computer Analysis of Images and Patterns Munster, GERMANY, SEP 02-04, 2009. Paper presented at 13th International Conference on Computer Analysis of Images and Patterns Munster, GERMANY, SEP 02-04, 2009. Munster: Springer
Open this publication in new window or tab >>Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images
2009 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Traffic Sign Recognition is a widely studied problem and its dynamic nature calls for the application of a broad range of preprocessing, segmentation, and recognition techniques but few databases are available for evaluation. We have produced a database consisting of 1,300 images captured by a video camera. On this database we have conducted a systematic experimental study. We used four different preprocessing techniques and designed a generic speed sign segmentation algorithm. Then we selected a range of contemporary speed sign classification algorithms using shape based segmented binary images for training and evaluated their results using four metrics, including accuracy and processing speed. The results indicate that Naive Bayes and Random Forest seem particularly well suited for this recognition task. Moreover, we show that two specific preprocessing techniques appear to provide a better basis for concept learning than the others.

Place, publisher, year, edition, pages
Munster: Springer, 2009
Keywords
road sign, classification, supervised learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-7955 (URN)000273458100148 ()oai:bth.se:forskinfo5C2249D7A13C5FD8C125762C005964CD (Local ID)978-3-642-03766-5 (ISBN)oai:bth.se:forskinfo5C2249D7A13C5FD8C125762C005964CD (Archive number)oai:bth.se:forskinfo5C2249D7A13C5FD8C125762C005964CD (OAI)
Conference
13th International Conference on Computer Analysis of Images and Patterns Munster, GERMANY, SEP 02-04, 2009
Note
Source: COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS Book Series: Lecture Notes in Computer Science Volume: 5702 Pages: 1220-1227 Published: 2009Available from: 2012-09-18 Created: 2009-09-09 Last updated: 2018-01-11Bibliographically approved
Iqbal, M. I., Aibinu, A., Nilsson, M., Tijani, I. B. & Salami, M. (2008). Detection of Vascular Intersection in Retina Fundus Image Using Modified Cross Point Number and Neural Network Technique. Paper presented at Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development. Paper presented at Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development. Kuala Lumpur, Malaysia
Open this publication in new window or tab >>Detection of Vascular Intersection in Retina Fundus Image Using Modified Cross Point Number and Neural Network Technique
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2008 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Vascular intersection can be used as one of the symptoms for monitoring and diagnosis of diabetic retinopathy from fundus images. In this work we apply the knowledge of digital image processing, fuzzy logic and neural network technique to detect bifurcation and vein-artery cross-over points in fundus images. The acquired images undergo preprocessing stage for illumination equalization and noise removal. Segmentation stage clusters the image into two distinct classes by the use of fuzzy c-means technique, neural network technique and modified cross-point number (MCN) methods were employed for the detection of bifurcation and cross-over points. MCN uses a 5x5 window with 16 neighboring pixels for efficient detection of bifurcation and cross over points in fundus images. Result obtained from applying this hybrid method on both real and simulated vascular points shows that this method perform better than the existing simple cross-point number (SCN) method, thus an improvement to the vascular point detection and a good tool in the monitoring and diagnosis of diabetic retinopathy. ©2008 IEEE.

Place, publisher, year, edition, pages
Kuala Lumpur, Malaysia: , 2008
Keywords
Bifurcation, cross point, fundus image, fuzzy c-means, neural network
National Category
Telecommunications
Identifiers
urn:nbn:se:bth-8343 (URN)000259601400049 ()oai:bth.se:forskinfo787A38AB87DF09A7C1257506002D47BF (Local ID)978-1-4244-1691-2 (ISBN)oai:bth.se:forskinfo787A38AB87DF09A7C1257506002D47BF (Archive number)oai:bth.se:forskinfo787A38AB87DF09A7C1257506002D47BF (OAI)
Conference
Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development
Available from: 2012-09-18 Created: 2008-11-19 Last updated: 2015-06-30Bibliographically approved
Swartling, M., Nilsson, M. & Grbic, N. (2008). Distinguishing True and False Source Locations when Localizing Multiple Concurrent Speech Sources. Paper presented at IEEE Sensor Array and Multichannel Signal Processing Workshop. Paper presented at IEEE Sensor Array and Multichannel Signal Processing Workshop. Darmstadt, GERMANY: IEEE
Open this publication in new window or tab >>Distinguishing True and False Source Locations when Localizing Multiple Concurrent Speech Sources
2008 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

A permutation problem arises in the case of locating multiple speech sources using several sensor arrays in the far field. The intersection of different direction of arrival (DOA) estimates between sensor arrays leads to a set of real source locations as well as a set of false intersections. This paper presents a novel method for pairing DOA estimates from different sensor arrays, resulting in the corresponding real intersection points. The algorithm presented is numerically efficient and suitable for real time implementations. Real room recordings are used to evaluate the method.

Place, publisher, year, edition, pages
Darmstadt, GERMANY: IEEE, 2008
Keywords
Array signal processing, Position measurement
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-8498 (URN)000260566500080 ()oai:bth.se:forskinfoEA817F5196A3600CC12574A4005A056C (Local ID)978-1-4244-2240-1 (ISBN)oai:bth.se:forskinfoEA817F5196A3600CC12574A4005A056C (Archive number)oai:bth.se:forskinfoEA817F5196A3600CC12574A4005A056C (OAI)
Conference
IEEE Sensor Array and Multichannel Signal Processing Workshop
Available from: 2012-09-18 Created: 2008-08-13 Last updated: 2015-06-30Bibliographically approved
Nilsson, M., Bartunek, J. S., Nordberg, J. & Claesson, I. (2008). Human Whistle Detection and Frequency Estimation. Paper presented at CISP. Paper presented at CISP. Sanya: IEEE
Open this publication in new window or tab >>Human Whistle Detection and Frequency Estimation
2008 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Human whistle could be a way to perform activation of different kind of devices, for example turn on and off a light in a smart room. Therefore, in this paper a human whistle detection and frequency estimation system is presented. Further, an investigation of human whistling and a robust non-linear feature extraction is presented. A system for robust performance due to sensor change and various noise situations is proposed using these features. Experiments in various noise situations are conducted.

Place, publisher, year, edition, pages
Sanya: IEEE, 2008
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-8501 (URN)000258873900152 ()oai:bth.se:forskinfo67A079F0676C546FC12574A4002D6D38 (Local ID)oai:bth.se:forskinfo67A079F0676C546FC12574A4002D6D38 (Archive number)oai:bth.se:forskinfo67A079F0676C546FC12574A4002D6D38 (OAI)
Conference
CISP
Available from: 2012-09-18 Created: 2008-08-13 Last updated: 2015-06-30Bibliographically approved
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