Given a set of images from the same viewpoint, in which occlusions are present, background estimation is to output an image with stationary objects in the scene only. Background estimation is an important step in many computer vision problems such as object detection and recognition. With the growing interest in more sophisticated video surveillance systems, the requirement for the accuracy of background estimation increases as well. In this thesis, we present two novel methods whose fundamental objectives are the same, namely, to estimate the background of a set of related images. In order to make our methods more general, we assume that the input images can be taken either from the same viewpoint or from different viewpoints. Both methods combine information from multiple input images by selecting the appropriate pixels to construct the background. Our first method is a scanline energy optimization method, and our second method is based on graph cuts optimization. We apply these two methods to datasets with different feature and the results are encouraging. Furthermore, we use the CUDA (Compute Unified Device Architecture) programming language to make full use of the GPU processing power. GPU stands for Graphics Processing Unit, which employs parallel processing and is more powerful than the CPU. In particular, we implement an efficient graph-based image segmentation algorithm as well as a linear blending method using the CUDA programming language for acceleration, both of which are used in our first method. The speedup of our GPU implementation can be 20 times faster.

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During the last decade, many baseline estimation methods have been proposed, but many of these approaches are either only useful for specific kinds of analytical signals or require the adjustment of many parameters. This complicates the selection of an appropriate approach for each kind of chemical signal and the optimization of multiple parameters itself is not an easy task. In this work, an asymmetric least squares (ALS) approach is used with truncated and augmented Fourier basis functions to provide a universal basis space for baseline approximation for diverse analytical signals. The proposed method does not require extensive parameter adjustment or prior baseline information. The basis set used to model the baselines includes a Fourier series truncated to low frequency sines and cosines (consistent with the number of channels) which is then augmented with lower frequencies. The number of basis functions employed depends mainly on the frequency characteristics of the baseline, which is the only parameter adjustment required for baseline estimation. The weighting factor for the asymmetric least squares in this case is dependent mainly on the level of the noise. The adjustment of these two parameters can be easily performed by visual inspection of results. To estimate and eliminate the baseline from the analytical signals, a novel algorithm, called Truncated Fourier Asymmetric Least Squares (TFALS) was successfully developed and optimized. It does not require baseline representative signals or extensive parameter adjustments. The method is described only with parameters optimization using simulated signals. The results with simulated and experimental data sets having different baseline artefacts show that TFALS is a versatile, effective and easy-to-use baseline removal method.

La problématique générale de cette thèse est de reconstituer la scène de fond à partir d’une séquence d’images en présence de masques d’avant-plan. Nous nous sommes intéressés aux méthodes pour détecter ce qui constitue le fond ainsi que les solutions pour corriger les parties cachées et les distor­sions géométrique et chromatique introduites lors de la photographie.Une série de processus est proposée, dont la mise en œuvre comporte dans l'ordre l’aligne­ment géométrique, le réglage chromatique, la fusion des images et la correction des défauts.Nous nous plaçons dans l’hypothèse où le fond est porté sur une surface plane. L'aligne­ment géométrique est alors réalisé par calcul de l'homographie entre une image quelconque et l’image qui sert de référence, suivi d’une interpolation bilinéaire.Le réglage chromatique vise à retrouver un même contraste dans les différentes images. Nous proposons de modéliser la mise en cor­respondance chromatique entre images par une approximation linéaire dont les para­mètres sont déterminés par les résultats de la mise en correspondance des points de contrôle (SIFT).Ces deux étapes sont suivies par une étape de fusion. Plusieurs techniques sont comparées.La première proposition est d’étendre la définition de la médiane dans l’espace vec­toriel. Elle est robuste lorsqu’il y a plus de la moitié des images qui voient les pixels d’arrière-plan. En outre, nous concevons un algorithme original basé sur la notion de clique. Il permet de détecter le plus grand nuage de pixels dans l'espace RGB. Cette approche est fiable même lorsque les pixels d’arrière-plan sont minoritaires.Lors de la mise en œuvre de ce protocole, on constate que certains résultats de fusion présentent des défauts de type flou dus à l’existence d’erreurs d’alignement géomé­trique. Nous proposons donc un traitement complémentaire. Il est basé sur une compa­raison entre le résultat de fusion et les images alignées après passage d'un filtre gaussien. Sa sortie est un assemblage des morceaux très détaillés d'image alignés qui ressemblent le plus au résultat de fusion associés.La performance de nos méthodes est éva­luée par un ensemble de données contenant de nombreuses images de qualités diffé­rentes. Les expériences confirment la fiabi­lisé et la robustesse de notre conception dans diverses conditions de photographie. / The general topic of this thesis is to reconstruct the background scene from a burst of images in presence of masks. We focus on the background detection methods as well as on solutions to geometric and chromatic distortions introduced during ph-otography. A series of process is proposed, which con­sists of geometric alignment, chromatic adjustment, image fusion and defect correction.We consider the case where the background scene is a flat surface. The geometric align­ment between a reference image and any other images in the sequence, depends on the computation of a homography followed by a bilinear interpolation.The chromatic adjustment aims to attach a similar contrast to the scene in different im­ages. We propose to model the chromatic mapping between images with linear approximations whose parameters are decided by matched pixels of SIFT .These two steps are followed by a discus­sion on image fusion. Several methods have been compared.The first proposition is a generation of typical median filter to the vector range. It is robust when more than half of the images convey the background information. Besides, we design an original algorithm based on the notion of clique. It serves to distinguish the biggest cloud of pixels in RGB space. This approach is highly reliable even when the background pixels are the minority.During the implementation, we notice that some fusion results bear blur-like defects due to the existence of geometric alignment errors. We provide therefore a combination method as a complementary step to ameli-orate the fusion results. It is based on a com-parison between the fusion image and other aligned images after applying a Gaussian filter. The output is a mosaic of patches with clear details issued from the aligned images which are the most similar to their related fusion patches.The performance of our methods is evaluated by a data set containing extensive images of different qualities. Experiments confirm the reliability and robustness of our design under a variety of photography conditions.

<p dir="ltr">This thesis presents two studies in search for violation of lepton flavor universality as predicted by the Standard Model. The first searches for signs of the violation by studying beyond the Standard Model (BSM) physics models involving same flavor and opposite sign dilepton pair and bottom quarks as final states. This study was done using the dataset collected during years of 2016, 2017 and 2018, with center of mass energy $\sqrt{s} = 13$ TeV and integrated luminosity of 138 $fb^-1$. In the study, scale factors were derived in order to correct deviations between simulation and real life data, specifically for high transverse momentum muons and top\&anti-top quark background. Furthermore, lower limits of energy scale were calculated leading to exclusion of the BSM models with energy scale values lower than that of the calculated value.</p><p dir="ltr">The second study also searches for of the lepton flavor universality violation, but in the specific decay of a tauon into three muons ($\tau \rightarrow 3\mu$). In the study, graph based neural network model (GNN) designed to classify $\tau \rightarrow 3\mu$ events at the CMS detector was converted to high level synthesis (HLS) code, so that the GNN could be coded into a custom hardware such as field programmable gate arrays (FPGA) for deployment. Moreover, techniques such as pruning and quantization were applied in an attempt to make the GNN more light weight, due to strict requirements of FPGA.</p>

This thesis concerns the background reconstruction from several photographs (so called depopulation scene efect). There are presented methods for obtaining the background from video and discussion of their use for photographs. The greatest emphasis is placed on the Gaussian mixture model and effort to improve this algorithm due to static image. The photographs should be taken with a tripod.

Modern day intelligent transportation system (ITS) relies on reliable and accurate estimated traffic parameters. Travel speed, traffic flow, and traffic state classification are the main traffic parameters of interest. These parameters can be estimated through efficient vision-based algorithms and appropriate camera sensor technology. With the advances in camera technologies and increasing computing power, use of monocular vision, stereo vision, and camera sensor fusion technologies have been an active research area in the field of ITS. In this thesis, we investigated stationary monocular and stereo camera technology for traffic parameters estimation. Stationary camera sensors provide large spatial-temporal information of the road section with relatively low installation costs. Two novel scientific contributions for vehicle detection and recognition are proposed. The first one is the use stationary stereo camera technology, and the second contribution is the fusion of monocular and stereo camera technologies. A vision-based ITS consists of several hardware and software components. The overall performance of such a system does not only depend on these single modules but also on their interaction. Therefore, a systematic approach considering all essential modules was chosen instead of focusing on one element of the complete system chain. This leads to detailed investigations of several core algorithms, e.g. background subtraction, histogram based fingerprints, and data fusion methods. From experimental results on standard datasets, we concluded that proposed fusion-based approach, consisting of monocular and stereo camera technologies performs better than each particular technology for vehicle detection and vehicle recognition. Moreover, this research work has a potential to provide a low-cost vision-based solution for online traffic monitoring systems in urban and rural environments.

The analysis presented in this thesis focuses on two methods developed to estimate, from data, the multi-jet and the V+jets background processes for top quark pair production occuring during proton-proton at LHC. Top quark paires are reconstructed using the CMS detector, exploiting the semi-leptonic decay channel. Both methods have been developed and studied using Monte-Carlo simulated data. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Στην παρούσα διδακτορική διατριβή προτείνονται μεθοδολογίες υπολογιστικής νοημοσύνης για την επεξεργασία και ανάλυση δεδομένων γονιδιακής έκφρασης μικροσυστοιχιών cDNA. Πιο συγκεκριμένα, στο πρώτο σκέλος αναπτύσσονται δύο νέες προσεγγίσεις για την εύρωστη εκτίμηση και διόρθωση του θορύβου υποβάθρου: η διόρθωση υποβάθρου βάσει εκατοστημορίων και η διόρθωση υποβάθρου βάσει παλινδρόμησης loess. Οι προσεγγίσεις αυτές καινοτομούν κυρίως στο ότι χρησιμοποιούν μία εύρωστη εκτίμηση του θορύβου υποβάθρου, γεγονός που τις καθιστά ιδανικές σε περιπτώσεις, όπου τα δεδομένα είναι θορυβώδη. Επιπροσθέτως, αναπτύσσεται ένα νέο, γενικής χρήσεως, πλαίσιο για τη συστηματική αξιολόγηση του βαθμού επίδρασης των μεθόδων διόρθωσης υποβάθρου. Μέσω του πλαισίου αυτού, οι δύο προτεινόμενες προσεγγίσεις, καθώς και άλλες ευρέως χρησιμοποιούμενες μέθοδοι, αξιολογούνται βάσει εφαρμογής τους σε διαφορετικά σύνολα δεδομένων αυτο-υβριδοποίησης, με τις πρώτες να εμφανίζουν ιδιαιτέρως καλή απόδοση. Το πλαίσιο αυτό καινοτομεί στο ότι ενσωματώνει νέα κριτήρια και τρόπους γραφικής απεικόνισης. Τόσο οι προτεινόμενες μέθοδοι εκτίμησης και διόρθωσης θορύβου υποβάθρου, όσο και το πλαίσιο συστηματικής αξιολόγησής τους, συνιστούν μία νέα, ενδελεχή μελέτη που προσανατολίζει στην εφαρμογή ή απόρριψη μίας συγκεκριμένης προσέγγισης, συνεισφέροντας εν τέλει στην κατάκτηση καλλίτερης ποιότητας δεδομένων μικροσυστοιχιών. Επίσης, στο δεύτερο σκέλος της διατριβής αναπτύσσεται ένα νέο, ολοκληρωμένο και γενικής χρήσεως πλαίσιο ανάλυσης δεδομένων μικροσυστοιχιών ούτως, ώστε να διερευνηθεί το ζήτημα εάν στην T-λευχαιμική κυτταρική σειρά CCRF-CEM επικρατούν εγγενείς ή επίκτητοι μηχανισμοί αντοχής στην πρεδνιζολόνη. Συγκεκριμένα, καταλλήλως επιλεχθέντα δεδομένα μικροσυστοιχιών cDNA – που διευκολύνουν την εξέταση τόσο της εξαρτώμενης από τη συγκέντρωση δράσης, όσο και της δυναμικής της ανταπόκρισης στην πρεδνιζολόνη (πρώιμη και όψιμη δράση) – γίνονται αντικείμενο επεξεργασίας και ενδελεχούς ανάλυσης, και βάσει συγκεκριμένων, προ-διατυπωμένων συλλογισμών, προσεγγίζεται το εν λόγω ερώτημα. Το πλαίσιο αυτό είναι καινοτόμο, εφόσον, πέραν του ότι ενσωματώνει μία πρωτότυπη ακολουθία μεθόδων, προσεγγίζει συστηματικά το πρόβλημα της εγγενούς ή επίκτητης αντοχής, συνεισφέροντας, έτσι, στην ευρύτερη προσπάθεια διερεύνησης των επακριβών μηχανισμών αντοχής των λευχαιμικών κυττάρων στα γλυκοκορτικοειδή. Τα αποτελέσματα από την εφαρμογή του στα δεδομένα της εν λόγω κυτταρικής σειράς συνηγορούν υπέρ της ύπαρξης μίας σύνθετης ανταπόκρισης του υπό μελέτη συστήματος στα γλυκοκορτικοειδή, η οποία όμως τείνει περισσότερο προς έναν εγγενή μηχανισμό αντοχής. / In the present Ph.D. thesis, computational intelligence methods for processing and analyzing cDNA microarray gene expression data are designed and developed. More specifically, in the first part of this thesis, the problem of background estimation and correction of two-channel microarray data is addressed and two novel algorithms are proposed, namely the percentiles-based and the loess-based background correction methods. Both approaches are based on the multiplicative model of background, while utilizing robust background noise estimators, thus making them ideal for noisy datasets. Furthermore, a new, generic framework for the systematic evaluation of the impact of the background estimating methodologies is suggested, whereupon the aforementioned methods as well as other approaches are evaluated by application to various publicly available self-self hybridization datasets. As suggested by this thorough, comparative evaluation our algorithms perform very well regarding noise reduction. The evaluation framework, which is based mainly on different and widely used statistical measures, incorporates new criteria and visualization methods. Moreover, it represents a novel, detailed contribution to the examination of the impact of background correction methods to the final interpretation of microarray experiments, conferring explicit guidance on the pros and cons of them and when they should be applied. Additionally, in the second part of this thesis, a new, generic, computational microarray data analysis framework is described, in order to examine the hypothesis of whether the resistant T-cell leukemia cell line CCRF-CEM posses an intrinsic or exert an acquired mechanism of resistance and to investigate the molecular imprint of this, upon prednisolone treatment. More analytically, using the above explained computational analysis workflow, microarray data that enable the examination of both the dose effect of prednisolone exposure and the dynamics (early and late) of the molecular response of the cells at the transcriptomic layer, are systematically analyzed based on specific, predefined formulations. The analysis of the results supports a complex mechanism of action for the cells which seems to favor though more the intrinsic mechanism of resistance.