| 1. |
- Aibinu, A.M., et al.
(författare)
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Vascular intersection detection in retina fundus images using a new hybrid approach
- 2010
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 40:1, s. 81-89
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Bartunek, Josef Ström, et al.
(författare)
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Adaptive Fingerprint Binarization by Frequency Domain Analysis
- 2006
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 3. |
- Bartunek, Josef Strom, et al.
(författare)
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Adaptive Fingerprint Image Enhancement with Emphasis on Preprocessing of Data
- 2013
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Ingår i: IEEE Transactions on Image Processing. - : IEEE. - 1941-0042 .- 1057-7149. ; 22:2, s. 644-656
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Tidskriftsartikel (refereegranskat)abstract
- 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; preprocessing, global analysis, local analysis and matched filtering. In the pre-processing 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 towards the NIST developed NBIS software for fingerprint recognition on FVC databases.
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| 4. |
- Bartunek, Josef Ström, et al.
(författare)
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Improved Adaptive Fingerprint Binarization
- 2008
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Konferensbidrag (refereegranskat)abstract
- In this paper improvements to a previous work are presented. Removing the redundant artifacts in the fingerprint mask is introduced enhancing the final result. The proposed method is entirely adaptive process adjusting to each fingerprint without any further supervision of the user. Hence, the algorithm is insensitive to the characteristics of the fingerprint sensor and the various physical appearances of the fingerprints. Further, a detailed description of fingerprint mask generation not fully described in the previous work is presented. The improved experimental results are presented.
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| 5. |
- Bartunek, Josef Ström, et al.
(författare)
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Neural Network based Minutiae Extraction from Skeletonized Fingerprints
- 2006
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Butt, Naveed, et al.
(författare)
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Classification of Raman Spectra to Detect Hidden Explosives
- 2011
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE. - 1545-598X. ; 8:3, s. 517-521
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Butt, Naveed R., et al.
(författare)
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An Improved Classification Scheme for Standoff Detection of Explosives via Raman Spectroscopy
- 2010
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Gertsovich, Irina, et al.
(författare)
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A novel methodology for the interoperability evaluation of an iris segmentation algorithm
- 2013
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Konferensbidrag (refereegranskat)abstract
- The performance of an iris recognition system depends greatly on how well the iris segmentation part of the system performs its task. The performance of an iris segmentation algorithm can be evaluated using different criteria and methods. Some of the methods evaluate the performance of the segmentation algorithm based on the performance of the whole iris recognition system. Other methods evaluate the performance of an iris segmentation subsystem independent of the performance of the system's other subsystems. To our knowledge there do not exist a generally accepted method or criteria for the evaluation of the standalone iris segmentation subsystem. This paper proposes a novel methodology to compare the performance of different iris segmentation algorithms, applied to different image datasets in a consistent way. The methodology employs the F1 score and an empirical cumulative distribution function. The implementation of the F1 score estimation, adapted to the iris segmentation task is described. Finally the application of the proposed methodology is demonstrated and discussed.
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| 9. |
- Iqbal, Muhammad Imran, et al.
(författare)
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Detection of Vascular Intersection in Retina Fundus Image Using Modified Cross Point Number and Neural Network Technique
- 2008
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 10. |
- Javadi, Mohammad Saleh, 1986-
(författare)
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Computer Vision Algorithms for Intelligent Transportation Systems Applications
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In recent years, Intelligent Transportation Systems (ITS) have emerged asan efficient way of enhancing traffic flow, safety and management. Thesegoals are realized by combining various technologies and analyzing the acquireddata from vehicles and roadways. Among all ITS technologies, computervision solutions have the advantages of high flexibility, easy maintenanceand high price-performance ratio that make them very popular fortransportation surveillance systems. However, computer vision solutionsare demanding and challenging due to computational complexity, reliability,efficiency and accuracy among other aspects. In this thesis, three transportation surveillance systems based on computervision are presented. These systems are able to interpret the imagedata and extract the information about the presence, speed and class ofvehicles, respectively. The image data in these proposed systems are acquiredusing Unmanned Aerial Vehicle (UAV) as a non-stationary sourceand roadside camera as a stationary source. The goal of these works is toenhance the general performance of accuracy and robustness of the systemswith variant illumination and traffic conditions. This is a compilation thesis in systems engineering consisting of threeparts. The red thread through each part is a transportation surveillancesystem. The first part presents a change detection system using aerial imagesof a cargo port. The extracted information shows how the space isutilized at various times aiming for further management and developmentof the port. The proposed solution can be used at different viewpoints andillumination levels e.g. at sunset. The method is able to transform the imagestaken from different viewpoints and match them together. Thereafter,it detects discrepancies between the images using a proposed adaptive localthreshold. In the second part, a video-based vehicle's speed estimationsystem is presented. The measured speeds are essential information for lawenforcement and they also provide an estimation of traffic flow at certainpoints on the road. The system employs several intrusion lines to extractthe movement pattern of each vehicle (non-equidistant sampling) as an inputfeature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are alsotaken into account to address the uncertainty in the measurements and toobtain the probability density function of the vehicle's speed. In the thirdpart, a vehicle classification system is provided to categorize vehicles into\private car", \light trailer", \lorry or bus" and \heavy trailer". This informationcan be used by authorities for surveillance and development ofthe roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. Thesystem has been constructed by using prior knowledge of traffic regulationsregarding each class of vehicle in order to enhance the classification performance.
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