Browsing 2. Ερευνητικές δημοσιεύσεις | Research publications by Author "Anastassopoulos, Vassilis"
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- ItemOpen AccessDrunk person identification using thermal infrared images
Τμήμα Φυσικής (Δημοσ. Π.Π. σε περιοδικά)(Inderscience Enterprises Ltd Publishers, ) Koukiou, Georgia; Anastassopoulos, Vassilis; Κουκίου, Γεωργία; Αναστασόπουλος, ΒασίλειοςDrunk person identification is carried out using thermal infrared images. Two different approaches are proposed for distinguishing a drunk person by means of radiometric values on its face. The features used in the first approach are simply the pixel values of specific points on the face of the person. It is proved that the cluster of a specific person moves in the feature space as the person consumes alcohol. Fisher linear discriminant approach is used for space dimensionality reduction. The feature space is found to be of very low dimensionality. The majority of the clusters moves towards the same direction and the feature space can easily be separated into the ‘sober’ and ‘drunk’ regions. Thus the ‘drunk’ feature space is introduced. In the second approach thermal differences between various locations on the face are evaluated and their value is monitored. It was found that specific areas in the face of a drunk person present an increased thermal illumination. These areas are the best candidates for identifying a drunk person. The concept behind this second proposed approach relies on a physiology-based face identification procedure.
- ItemOpen AccessInterpolation in multispectral data using neural networks
Τμήμα Φυσικής (Δημοσ. Π.Π. σε συνέδρια)(2011-12-08) Tsagaris, Vassilis; Panagiotopoulou, Antigoni; Anastassopoulos, Vassilis; Τσαγκάρης, Βασίλειος; Παναγιωτοπούλου, Αντιγόνη; Αναστασόπουλος, ΒασίλειοςA novel procedure which aims in increasing the spatial resolution of multispectral data and simultaneously creates a high quality RGB fused representation is proposed in this paper. For this purpose, neural networks are employed and a successive training procedure is applied in order to incorporate in the network structure knowledge about recovering lost frequencies and thus giving fine resolution output color images. MERIS multispectral data are employed to demonstrate the performance of the proposed method.
- ItemOpen AccessRegularized super-resolution image reconstruction employing robust error norms
Τμήμα Φυσικής (Δημοσ. Π.Π. σε περιοδικά)(2011-12-08) Panagiotopoulou, Antigoni; Anastassopoulos, Vassilis; Παναγιωτοπούλου, Αντιγόνη; Αναστασόπουλος, ΒασίλειοςA high-resolution image is reconstructed from a sequence of subpixel shifted, aliased low-resolution frames, by means of stochastic regularized super-resolution (SR) image reconstruction. The Tukey (T), Lorentzian (L), and Huber (H) cost functions are employed for the data-fidelity term. The performance of the particular error norms, in SR image reconstruction, is presented. Actually, their employment in SR recon-struction is preceded by dilating and scaling their influence functions to make them as similar as possible. Thus, the direct comparison of these norms in rejecting outliers takes place. The bilateral total variation (BTV) regularization is incorporated as a priori knowledge about the solution. The outliers effect is significantly reduced, and the high-frequency edge structures of the reconstructed image are preserved. The proposed TTV, LTV, and HTV methods are directly compared with a former SR method that employs the L1-norm in the data-fidelity term for synthesized and real sequences of frames. In the simulated experiments, noiseless frames as well as frames corrupted by salt-and-pepper noise are employed. Experimental results verify the robust statistics theory. Thus, the Tukey method performs best, while the L1-norm technique performs inferiorly to the proposed techniques.
- ItemOpen AccessScanned images resolution improvement using neural networks
Τμήμα Φυσικής (Δημοσ. Π.Π. σε περιοδικά)(2011-12-08) Panagiotopoulou, Antigoni; Anastassopoulos, Vassilis; Παναγιωτοπούλου, Αντιγόνη; Αναστασόπουλος, ΒασίλειοςA novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques.
- ItemOpen AccessSearch for chameleons with CAST
Τμήμα Φυσικής (Δημοσ. Π.Π. σε περιοδικά)Anastassopoulos, Vassilis; Gardikiotis, Antonios; Zioutas, Konstantinos; Αναστασόπουλος, Βασίλειος; Γαρδικιώτης, Αντώνης; Ζιούτας, ΚωνσταντίνοςIn this work we present a search for (solar) chameleons with the CERN Axion Solar Telescope (CAST). This novel experimental technique, in the field of dark energy research, exploits both the chameleon coupling to matter (βm) and to photons (βγ) via the Primakoff effect. By reducing the X-ray detection energy threshold used for axions from 1 keV to 400 eV CAST became sensitive to the converted solar chameleon spectrum which peaks around 600 eV. Even though we have not observed any excess above background, we can provide a 95% C.L. limit for the coupling strength of chameleons to photons of βγ 1011for 1 <βm<106.
- ItemOpen AccessSuper-resolution image reconstruction employing Kriging interpolation technique
Τμήμα Φυσικής (Δημοσ. Π.Π. σε συνέδρια)(2011-12-08) Panagiotopoulou, Antigoni; Anastassopoulos, Vassilis; Παναγιωτοπούλου, Αντιγόνη; Αναστασόπουλος, ΒασίλειοςIn this paper a high-resolution (HR) image is reconstructed from a sequence of subpixel shifted, aliased low-resolution (LR) frames by means of a novel nonuniform interpolation super-resolution (SR) method. A gradient-based algorithm estimates the horizontal and vertical shifts for each frame. Then, the uniformly spaced sampling points of the HR image are produced by means of Kriging interpolation. Wiener filtering is employed to deal with the restoration problem. The novelty of the proposed nonuniform interpolation approach to SR image reconstruction lies in the employment of Kriging interpolation technique. Comparisons with the original image demonstrate the superiority of our method to a conventional nonuniform interpolation one of SR image reconstruction.
- ItemOpen AccessSuper-resolution image reconstruction techniques: Trade-offs between the data-fidelity and regularization terms
Τμήμα Φυσικής (Δημοσ. Π.Π. σε περιοδικά)Stochastic regularized methods are quite advantageous in Super-Resolution (SR) image reconstruction problems. In the particular techniques the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. The present work examines the effect of each one of these terms on the SR reconstruction result with respect to the presence or absence of noise in the Low-Resolution (LR) frames. Experimentation is carried out with the widely employed L2, L1, Huber and Lorentzian estimators for the data-fidelity term. The Tikhonov and Bilateral (B) Total Variation (TV) techniques are employed for the regularization term. The extracted conclusions can, in practice, help to select an effective SR method for a given sequence of LR frames. Thus, in case that the potential methods present common data-fidelity or regularization term, and frames are noiseless, the method which employs the most robust regularization or data-fidelity term should be used. Otherwise, experimental conclusions regarding performance ranking vary with the presence of noise in frames, the noise model as well as the difference in robustness of efficiency between the rival terms. Estimators employed for the data-fidelity term or regularizations stand for the rival terms.
- ItemOpen AccessSuper-resolution reconstruction of thermal infrared images
Τμήμα Φυσικής (Δημοσ. Π.Π. σε συνέδρια)In this paper a high-resolution (HR) thermal infrared image is reconstructed from a sequence of subpixel shifted, aliased low-resolution (LR) frames, by means of a stochastic regularized super-resolution (SR) method. The Huber (H) cost function is employed to measure the difference between the projected estimate of the HR image and each LR frame. The bilateral Total Variation (TV) regularization is incorporated as a priori knowledge about the solution. The proposed HTV super-resolution approach that employs the Huber norm in combination with the bilateral TV regularization exhibits superior performance to former SR method. Thus the effect of outliers is significantly reduced and the high-frequency edge structures of the reconstructed HR thermal infrared image are preserved. The proposed technique is also tested on frames that are corrupted by Gaussian noise and proves superior when compared to existing regularized SR method.