| 1. |
- Faraj, Maycel Isaac, 1979-, et al.
(författare)
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Motion Features from Lip Movement for Person Authentication
- 2006
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Ingår i: The 18th International Conference on Pattern Recognition. - Washington, D.C. : IEEE Computer Society. - 0769525210 ; 3, s. 1059- 1062
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Konferensbidrag (refereegranskat)abstract
- This paper describes a new motion based feature extraction technique for speaker identification using orientation estimation in 2D manifolds. The motion is estimated by computing the components of the structure tensor from which normal flows are extracted. By projecting the 3D spatiotemporal data to 2D planes, we obtain projection coefficients which we use to evaluate the 3D orientations of brightness patterns in TV like image sequences. This corresponds to the solutions of simple matrix eigenvalue problems in 2D, affording increased computational efficiency. An implementation based on joint lip movements and speech is presented along with experiments which confirm the theory, exhibiting a recognition rate of 98% on the publicly available XM2VTS database
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| 2. |
- Alabdallah, Abdallah, 1979-, et al.
(författare)
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The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models
- 2024
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Ingår i: Artificial Intelligence in Medicine. - Amsterdam : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 148
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Tidskriftsartikel (refereegranskat)abstract
- The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
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| 3. |
- Järpe, Eric, 1965-
(författare)
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Räkna med rester : Matematik att tillämpas inom kryptologi
- 2013
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Bok (övrigt vetenskapligt/konstnärligt)abstract
- Ämnet kryptologi, dvs. kryptering, dekryptering och kodknäckning omfattar såväl matematik, datorprogrammering som allmän finurlighet. Denna bok behandlar Caesarkrypto, substitutionskrypto, Vignèrekrypto, RSA-krypto och den bakomliggande matematiken (ekvationslösning, räkning med exponentialuttryck, resträkning, primtalsteori och rekursion) samt angränsande matematik (såsom kombinatorik, statistiska metoder för t.ex. detektering av krypterad kod och beräkning av överföringskvalitet). Boken innehåller även en så pass bred genomgång av elementär algebra (mängdlära, logik, trigonometri, komplexa tal och rekurrensekvationer) och analys (funktioner i en variabel, derivata och integraler) att den kan användas vid inledande studier i matematik inom en mängd olika utbildningar. Boken syftar till att ge en introduktion till kryptologisk problemlösning och visa på de stora synergieffekter som uppnås genom att tillämpa en välbalanserad kombination av grundläggande matematik och elementär programmering inom området. Förhoppningen är också att läsaren, sporrad av de nyvunna insikterna om kryptologi, lockas till vidare kunskapsfördjupning. Boken vänder sig i första hand till blivande IT-forensiker som kan behöva kompetensen att kryptera och knäcka krypton i sin yrkesroll men som inte har en omfattande matematisk förkunskap. Den vänder sig även till studenter på landets tekniska högskolor och universitet.
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| 4. |
- Alves, Dimas irion, et al.
(författare)
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Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem : An Iterative Approach
- 2023
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Ingår i: IEEE Access. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 84734-84743
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author
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| 5. |
- Bengtsson, Ola, et al.
(författare)
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Localization in changing environments - Estimation of a covariance matrix for the IDC algorithm
- 2001
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Ingår i: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180). - Piscataway, N.J. : IEEE. - 0780366123 ; , s. 1931-1937
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Konferensbidrag (refereegranskat)abstract
- Previously we have presented a new scan-matching algorithm, based on the IDC - Iterative Dual Correspondence- algorithm, which showed a good localization performance even in the case of severe changes in the environment. The Problem of the IDC-algorithm is that there is no good way to estimate the covariance matrix of the position estimate, which prohibits an effective fusion with other position estimates from other sensors, e.g by means of the Kalman filter. In this paper we present a new way to estimate the covariance matrix, by estimating the Hessian matrix of the error function that is minimized by the IDC scan-matching algorithm. Simulation results show that the estimated covariance matrix correspond well to the real one.
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| 6. |
- Bigun, Josef, 1961-, et al.
(författare)
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Frequency map by Structure Tensor in Logarithmic Scale Space and Forensic Fingerprints
- 2016
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Ingår i: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016). - Piscataway, NJ : IEEE. - 9781509014378 ; , s. 204-213
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Konferensbidrag (refereegranskat)abstract
- Increasingly, absolute frequency and orientation maps are needed, e.g. for forensics. We introduce a non-linear scale space via the logarithm of trace of the Structure Tensor. Therein, frequency estimation becomes an orientation estimation problem. We show that this offers significant advantages, including construction of efficient isotropic estimations of dense maps of frequency. In fingerprints, both maps are shown to improve each other in an enhancement scheme via Gabor filtering. We suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. Furthermore, we present new evidence that frequency maps are useful attributes of minutiae. We verify that the suggested method compares favorably with state of the art using forensic fingerprints as test bed, and test images where the ground truth is known. In evaluations, we use public data sets and published methods only.
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| 7. |
- Dai, Liang, 1984-, et al.
(författare)
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Testing Exchangeability With Martingale for Change-Point Detection
- 2021
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Ingår i: International Journal of Ambient Computing and Intelligence (IJACI). - Hershey, PA : IGI Global. - 1941-6237 .- 1941-6245. ; 12:2, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- This work proposes a new exchangeability test for a random sequence through a martingale-based approach. Its main contributions include 1) an additive martingale which is more amenable for designing exchangeability tests by exploiting the Hoeffding-Azuma lemma and 2) different betting functions for constructing the additive martingale. By choosing the underlying probability density function of p-values as a betting function, it can be shown that, when a change-point appears, a satisfying trade-off between the smoothness and expected one-step increment of the martingale sequence can be obtained. An online algorithm based on beta distribution parametrization for constructing this betting function is discussed in detail as well. © 2021, IGI Global.
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| 8. |
- Englund, Cristofer, et al.
(författare)
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A novel approach to estimate proximity in a random forest : An exploratory study
- 2012
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Ingår i: Expert systems with applications. - Amsterdam : Elsevier BV. - 0957-4174 .- 1873-6793. ; 39:17, s. 13046-13050
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Tidskriftsartikel (refereegranskat)abstract
- A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel.
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| 9. |
- Faraj, Maycel Isaac, 1979-, et al.
(författare)
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Person verification by lip-motion
- 2006
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Ingår i: Conference on Computer Vision and Pattern Recognition Workshop. - Piscataway, N.J. : IEEE Press. - 0769526462 ; , s. 37-37
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Konferensbidrag (refereegranskat)abstract
- This paper describes a new motion based feature extraction technique for speaker recognition using orientation estimation in 2D manifolds. The motion is estimated by computing the components of the structure tensor from which normal flows are extracted. By projecting the 3D spatiotemporal data to 2-D planes we obtain projection coefficients which we use to evaluate the 3-D orientations of brightness patterns in TV like 2D image sequences. This corresponds to the solutions of simple matrix eigenvalue problems in 2D, affording increased computational efficiency. An implementation based on joint lip movements and speech is presented along with experiments which confirm the theory, exhibiting a recognition rate of 98% on the publicly available XM2VTS database.
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| 10. |
- Grek, Åsa, 1989-, et al.
(författare)
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An Inductive Approach to Quantitative Methodology—Application of Novel Penalising Models in a Case Study of Target Debt Level in Swedish Listed Companies
- 2024
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Ingår i: Journal of Risk and Financial Management. - Basel : MDPI. - 1911-8074. ; 17:5
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of this study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the methodology. The case aims to select explanatory variables correlated with the target debt level in Swedish listed companies. The survey respondents were matched with accounting information from the companies’ annual reports. However, missing data were present: to fully utilise penalising models, we employed classification and regression tree (CART)-based imputations by multiple imputations chained equations (MICEs) to address this problem. The imputed data were subjected to six penalising models: grouped multinomial lasso, ungrouped multinomial lasso, parallel element linked multinomial-ordinal (ELMO), semi-parallel ELMO, nonparallel ELMO, and cumulative generalised monotone incremental forward stagewise (GMIFS). While the older models yielded several explanatory variables for the hypothesis formation process, the new models (ELMO and GMIFS) identified only one quick asset ratio. Subsequent testing revealed that this variable was the only statistically significant variable that affected the target debt level. © 2024 by the authors.
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