Etude bibliographique sur le recalage d'images de visage et sur le recalage d'images et travail en collaboration avec Son VuS, pour définir la précision nécessaire du recalage en fonction des exigences des méthodes de reconnaissance de visages. / Face alignment is an important step in a typical automatic face recognition system.This thesis addresses the alignment of faces for face recognition applicationin video surveillance context. The main challenging factors of this research includethe low quality of images (e.g., low resolution, motion blur, and noise), uncontrolledillumination conditions, pose variations, expression changes, and occlusions. In orderto deal with these problems, we propose several face alignment methods using differentstrategies. The _rst part of our work is a three-stage method for facial pointlocalization which can be used for correcting mis-alignment errors. While existingalgorithms mostly rely on a priori knowledge of facial structure and on a trainingphase, our approach works in an online mode without requirements of pre-de_nedconstraints on feature distributions. The proposed method works well on images underexpression and lighting variations. The key contributions of this thesis are aboutjoint image alignment algorithms where a set of images is simultaneously alignedwithout a biased template selection. We respectively propose two unsupervised jointalignment algorithms : \Lucas-Kanade entropy congealing" (LKC) and \gradient correlationcongealing" (GCC). In LKC, an image ensemble is aligned by minimizing asum-of-entropy function de_ned over all images. GCC uses gradient correlation coef-_cient as similarity measure. The proposed algorithms perform well on images underdi_erent conditions. To further improve the robustness to mis-alignments and thecomputational speed, we apply a multi-resolution framework to joint face alignmentalgorithms. Moreover, our work is not limited in the face alignment stage. Since facealignment and face acquisition are interrelated, we develop an adaptive appearanceface tracking method with alignment feedbacks. This closed-loop framework showsits robustness to large variations in target's state, and it signi_cantly decreases themis-alignment errors in tracked faces.

L’alignement d’images paramétrique a de nombreuses applications pour la réalité augmentée, la compression vidéo ou encore le suivi d’objets. Dans cette thèse, nous nous intéressons notamment aux techniques de recalage d’images (template matching) reposant sur l’optimisation locale d’une fonctionnelle d’erreur. Ces approches ont conduit ces dernières années à de nombreux algorithmes efficaces pour le suivi d’objets. Cependant, les performances de ces algorithmes ont été peu étudiées lorsque les images sont dégradées par un bruit important comme c’est le cas, par exemple, pour des captures réalisées dans des conditions de faible luminosité. Dans cette thèse, nous proposons un nouveau formalisme, appelé formalisme bidirectionnel, qui unifie plusieurs approches de l’état de l’art. Ce formalisme est utilisé dans un premier temps pour porter un éclairage nouveau sur un grand nombre d’approches de la littérature et en particulier sur l’algorithme ESM (Efficient Second-order Minimization). Nous proposons ensuite une étude théorique approfondie de l’influence du bruit sur le processus d’alignement. Cette étude conduit à la définition de deux nouvelles familles d’algorithmes, les approches ACL (Asymmetric Composition on Lie Groups) et BCL (Bidirectional Composition on Lie Groups) qui permettent d’améliorer les performances en présence de niveaux de bruit asymétriques (Rapport Signal sur Bruit différent dans les images). L’ensemble des approches introduites sont validées sur des données synthétiques et sur des données réelles capturées dans des conditions de faible luminosité. / Parametric image alignment is a fundamental task of many vision applications such as object tracking, image mosaicking, video compression and augmented reality. To recover the motion parameters, direct image alignment works by optimizing a pixel-based difference measure between a moving image and a fixed-image called template. In the last decade, many efficient algorithms have been proposed for parametric object tracking. However, those approaches have not been evaluated for aligning images of low SNR (Signal to Noise ratio) such as images captured in low-light conditions. In this thesis, we propose a new formulation of image alignment called Bidirectional Framework for unifying existing state of the art algorithms. First, this framework allows us to produce new insights on existing approaches and in particular on the ESM (Efficient Second-order Minimization) algorithm. Subsequently, we provide a theoretical analysis of image noise on the alignment process. This yields the definition of two new approaches : the ACL (Asymmetric Composition on Lie Groups) algorithm and the BCL (Bidirectional Composition on Lie Groups) algorithm, which outperform existing approaches in presence of images of different SNR. Finally, experiments on synthetic and real images captured under low-light conditions allow to evaluate the new and existing approaches under various noise conditions.

The diploma thesis deals with the registration of images taken with the multimodal holographic microscope (MHM). The summary covers the fluorescent and holographic microscopy, and the multimodal holographic microscope combining both these microscopy types. Every pair of the images needs to be aligned in order to gain new information by combining both image types. The thesis contains an algorithm that registers images by phase correlation as well as a process created in MATLAB in accordance with the algorithm. The most important procedure parameters’ influence on the registration success is described and the results are annotated.

No description available.

Μια από τις συνεχώς εξελισσόμενες περιοχές της επιστήμης των υπολογιστών είναι η Υπολογιστική Όραση, σκοπός της οποίας είναι η δημιουργία έξυπνων συστημάτων για την ανάκτηση πληροφοριών από πραγματικές εικόνες. Πολλές σύγχρονες εφαρμογές της υπολογιστικής όρασης βασίζονται στην αντιστοίχιση εικόνων. Την πλειοψηφία των αλγορίθμων αντιστοίχισης συνθέτουν παραμετρικές τεχνικές, σύμφωνα με τις οποίες υιοθετείται ένα παραμετρικό μοντέλο, το οποίο εφαρμοζόμενο στη μια εικόνα δύναται να παρέχει μια προσέγγιση της άλλης. Στο πλαίσιο της διατριβής μελετάται εκτενώς το πρόβλημα της Στερεοσκοπικής Αντιστοίχισης και το γενικό πρόβλημα της Ευθυγράμμισης Εικόνων. Για την αντιμετώπιση του πρώτου προβλήματος προτείνεται ένας τοπικός αλγόριθμος διαφορικής αντιστοίχισης που κάνει χρήση μιας νέας συνάρτησης κόστους, του Τροποποιημένου Συντελεστή Συσχέτισης (ECC), η οποία ενσωματώνει το παραμετρικό μοντέλο μετατόπισης στον κλασικό συντελεστή συσχέτισης. Η ενσωμάτωση αυτή καθιστά τη νέα συνάρτηση κατάλληλη για εκτιμήσεις ανομοιότητας με ακρίβεια μικρότερη από αυτήν του εικονοστοιχείου. Αν και η συνάρτηση αυτή είναι μη γραμμική ως προς την παράμετρο μετατόπισης, το πρόβλημα μεγιστοποίησης έχει κλειστού τύπου λύση με αποτέλεσμα τη μειωμένη πολυπλοκότητα της διαδικασίας της αντιστοίχισης με ακρίβεια υπο-εικονοστοιχείου. Ο προτεινόμενος αλγόριθμος παρέχει ακριβή αποτελέσματα ακόμα και κάτω από μη γραμμικές φωτομετρικές παραμορφώσεις, ενώ η απόδοσή του υπερτερεί έναντι γνωστών στη διεθνή βιβλιογραφία τεχνικών αντιστοίχισης ενώ φαίνεται να είναι απαλλαγμένος από το φαινόμενο pixel locking. Στην περίπτωση του προβλήματος της ευθυγράμμισης εικόνων, η προτεινόμενη συνάρτηση γενικεύεται με αποτέλεσμα τη δυνατότητα χρήσης οποιουδήποτε δισδιάστατου μετασχηματισμού. Η μεγιστοποίησή της, η οποία αποτελεί ένα μη γραμμικό πρόβλημα, επιτυγχάνεται μέσω της επίλυσης μιας ακολουθίας υπο-προβλημάτων βελτιστοποίησης. Σε κάθε επανάληψη επιβάλλεται η μεγιστοποίηση μιας μη γραμμικής συνάρτησης του διανύσματος διορθώσεων των παραμέτρων, η οποία αποδεικνύεται ότι καταλήγει στη λύση ενός γραμμικού συστήματος. Δύο εκδόσεις του σχήματος αυτού προτείνονται: ο αλγόριθμος Forwards Additive ECC (FA-ECC) και o αποδοτικός υπολογιστικά αλγόριθμος Inverse Compositional ECC (IC-ECC). Τα προτεινόμενα σχήματα συγκρίνονται με τα αντίστοιχα (FA-LK και SIC) του αλγόριθμου Lucas-Kanade, ο οποίος αποτελεί σημείο αναφοράς στη σχετική βιβλιογραφία, μέσα από μια σειρά πειραμάτων. Ο αλγόριθμος FA-ECC παρουσιάζει όμοια πολυπλοκότητα με τον ευρέως χρησιμοποιούμενο αλγόριθμο FA-LΚ και παρέχει πιο ακριβή αποτελέσματα ενώ συγκλίνει με αισθητά μεγαλύτερη πιθανότητα και ταχύτητα. Παράλληλα, παρουσιάζεται πιο εύρωστος σε περιπτώσεις παρουσίας προσθετικού θορύβου, φωτομετρικών παραμορφώσεων και υπερ-μοντελοποίησης της γεωμετρικής παραμόρφωσης των εικόνων. Ο αλγόριθμος IC-ECC κάνει χρήση της αντίστροφης λογικής, η οποία στηρίζεται στην αλλαγή των ρόλων των εικόνων αντιστοίχισης και συνδυάζει τον κανόνα ενημέρωσης των παραμέτρων μέσω της σύνθεσης των μετασχηματισμών. Τα δύο αυτά χαρακτηριστικά έχουν ως αποτέλεσμα τη δραστική μείωση του υπολογιστικού κόστους, ακόμα και σε σχέση με τον SIC αλγόριθμο, με τον οποίο βέβαια παρουσιάζει παρόμοια συμπεριφορά. Αν και ο αλγόριθμος FA-ECC γενικά υπερτερεί έναντι των τριών άλλων αλγορίθμων, η επιλογή μεταξύ των δύο προτεινόμενων σχημάτων εξαρτάται από το λόγο μεταξύ ακρίβειας αντιστοίχισης και υπολογιστικού κόστους. / Computer Vision has been recently one of the most active research areas in computer society. Many modern computer vision applications require the solution of the well known image registration problem which consist in finding correspondences between projections of the same scene. The majority of registration algorithms adopt a specific parametric transformation model, which is applied to one image, thus providing an approach of the other one. Towards the solution of the Stereo Correspondence problem, where the goal is the construction of the disparity map, a local differential algorithm is proposed which involves a new similarity criterion, the Enhanced Correlation Coefficient (ECC). This criterion is invariant to linear photometric distortions and results from the incorporation of a single parameter model into the classical correlation coefficient, defining thus a continuous objective function. Although the objective function is non-linear in translation parameter, its maximization results in a closed form solution, saving thus much computational burden. The proposed algorithm provides accurate results even under non-linear photometric distortions and its performance is superior to well known conventional stereo correspondence techniques. In addition, the proposed technique seems not to suffer from pixel locking effect and outperforms even stereo techniques, dedicated to the cancellation of this effect. For the image alignment problem, the maximization of a generalized version of ECC function that incorporates any 2D warp transformation is proposed. Although this function is a highly non-linear function of the warp parameters, an efficient iterative scheme for its maximization is developed. In each iteration of the new scheme, an efficient approximation of the nonlinear objective function is used leading to a closed form solution of low computational complexity. Two different iterative schemes are proposed; the Forwards Additive ECC (FA-ECC) and the Inverse Compositional ECC (IC-ECC) algorithm. Τhe proposed iterative schemes are compared with the corresponding schemes (FA-LK and SIC) of the leading Lucas-Kanade algorithm, through a series of experiments. FA-ECC algorithm makes use of the known additive parameter update rule and its computational cost is similar to the one required by the most widely used FA-LK algorithm. The proposed iterative scheme exhibits increased learning ability, since it converges faster with higher probability. This superiority is retained even in presence of additive noise and photometric distortion, as well as in cases of over-modelling the geometric distortion of the images. On the other hand, IC-ECC algorithm makes use of inverse logic by swapping the role of images and adopts the transformation composition update rule. As a consequence of these two options, the complexity per iteration is drastically reduced and the resulting algorithm constitutes the most computationally efficient scheme than three other above mentioned algorithms. However, empirical learning curves and probability of convergence scores indicate that the proposed algorithm has a similar performance to the one exhibited by SIC. Though FA-ECC seems to be clearly more robust in real situation conditions among all the above mentioned alignment algorithms, the choice between two proposed schemes necessitates a trade-off between accuracy and speed.

With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This thesis studies flood assessment and land cover mapping of satellite images, and proposes nature inspired algorithms that can be easily implemented in realistic scenarios. Disaster monitoring using space technology is one of the key areas of research with vast potential; particularly flood based disasters are more challenging. Every year floods occur in many regions of the world and cause great losses. In order to monitor and assess such situations, decision-makers need accurate near real-time knowledge of the field situation. How to provide actual information to decision-makers for effective flood monitoring and mitigation is an important task, from the point of view of public welfare. Over-estimation of the flooded area leads to over-compensation to people, while under-estimation results in production loss and negative impacts on the population. Hence it is essential to assess the flood damage accurately, both in qualitative and quantitative terms. In such situations, land cover maps play a very critical role. Updating land cover maps is a time consuming and costlier operation when it is performed using traditional or manual methods. Hence, there is a need to find solutions for such problem through automation. Design of automatic systems dedicated to satellite image processing which involves change detection to discriminate areas of land cover change between imaging dates. The system integrates the spectral and spatial information with the techniques of image registration and pattern classification using nature inspired techniques. In the literature, various works have been carried out for solving the problem of image registration and pattern classification using conventional methods. Many researchers have proved, for different situations, that nature inspired techniques are promising in comparison with that of conventional methods. The main advantage of nature inspired technique over any other conventional methods is its stochastic nature, which converges to optimal solution for any dynamic variation in a given satellite image. Results are given in such terms as to delineate change in multi-date imagery using change-versus-no-change information to guide multi-date data analysis. The main objective of this study is to analyze spatio-temporal satellite data to bring out significant changes in the land cover map through automated image processing methods. In this study, for satellite image analysis of flood assessment and land cover mapping, the study areas and images considered are: Multi-temporal MODerate-resolution Imaging Spectroradiometer (MODIS) image around Krishna river basin in Andhra Pradesh India; Linear Imaging Self Scanning Sensor III (LISS III)and Synthetic Aperture Radar(SAR)image around Kosi river basin in Bihar, India; Landsat7thematicmapperimage from the southern part of India; Quick-Bird image of the central Bangalore, India; Hyperion image around Meerut city, Uttar Pradesh, India; and Indian pines hyperspectral image. In order to develop a flood assessment framework for this study, a database was created from remotely sensed images (optical and/or Synthetic Aperture Radar data), covering a period of time. The nature inspired techniques are used to find solutions to problems of image registration and pattern classification of a multi-sensor and multi-temporal satellite image. Results obtained are used to localize and estimate accurately the flood extent and also to identify the type of the inundated area based on land cover mapping. The nature inspired techniques used for satellite image processing are Artificial Neural Network (ANN), Genetic Algorithm (GA),Particle Swarm Optimization (PSO), Firefly Algorithm(FA),Glowworm Swarm Optimization(GSO)and Artificial Immune System (AIS). From the obtained results, we evaluate the performance of the methods used for image registration and pattern classification to compare the accuracy of satellite image processing using nature inspired techniques. In summary, the main contributions of this thesis include (a) analysis of flood assessment and land cover mapping using satellite images and (b) efficient image registration and pattern classification using nature inspired algorithms, which are more popular than conventional optimization methods because of their simplicity, parallelism and convergence of the population towards the optimal solution in a given search space.