Fractal Image Compression Using Modified Operator (IFS)

Image data Compression based on fractal theory is fundamentally dierent from conventional compression methods, its idea is to generate a contraction operator whose fixed point approximates the original image in a complete metric space of images. The specication of such operator can be stored as the fractal code for the original image. The contraction mapping principle implies that the iteration of the stored operator starting from arbitrary initial image will recover its xed point which is an approximation for the original image. This Contraction mapping is usually constructed using the partitioned IFS(PIFS) technique which relies on the assertion that parts of the image resemble other parts of the same image. It then, nds the fractal code for each part by searching for another larger similar part. This high costly search makes fractal image compression dicult to be implemented in practice, even it has the advantages of a high compression ratio, a low loss ratio, and the resolution independence of the compression rate. In this paper, we investigate fractal image compression(FIC) using Iterated Function Systems(IFS). After reviewing the standard scheme, we state a mathematical formulation for the practical aspect. We then propose a modied IFS that relies on the fact  that, there are very smooth parts in certain images. From the view point of mathematics, we present the modied operator, proving its properties that make it not only a fractal operator but also more eective than the standard one. The experimental results are presented and the performance of the proposed algorithm is discussed

HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING

Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked

Natural Parameterization

The objective of this project has been to develop an approach for imitating physical objects with an underlying stochastic variation. The key assumption is that a set of “natural parameters” can be extracted by a new subdivision algorithm so they reflect what is called the object’s “geometric DNA”. A case study on one hundred wheat grain crosssections (Triticum aestivum) showed that it was possible to extract thirty-six such parameters and to reuse them for Monte Carlo simulation of “new” stochastic phantoms which possessthe same stochastic behavior as the “original” cross-sections

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Multiple cue integration for robust tracking in dynamic environments: application to video relighting

L'anàlisi de moviment i seguiment d'objectes ha estat un dels pricipals focus d'atenció en la comunitat de visió per computador durant les dues darreres dècades. L'interès per aquesta àrea de recerca resideix en el seu ample ventall d'aplicabilitat, que s'extén des de tasques de navegació de vehicles autònoms i robots, fins a aplications en la indústria de l'entreteniment i realitat virtual.Tot i que s'han aconseguit resultats espectaculars en problemes específics, el seguiment d'objectes continua essent un problema obert, ja que els mètodes disponibles són propensos a ser sensibles a diversos factors i condicions no estacionàries de l'entorn, com ara moviments impredictibles de l'objecte a seguir, canvis suaus o abruptes de la il·luminació, proximitat d'objectes similars o fons confusos. Enfront aquests factors de confusió la integració de múltiples característiques ha demostrat que permet millorar la robustesa dels algoritmes de seguiment. En els darrers anys, degut a la creixent capacitat de càlcul dels ordinadors, hi ha hagut un significatiu increment en el disseny de complexes sistemes de seguiment que consideren simultàniament múltiples característiques de l'objecte. No obstant, la majoria d'aquests algoritmes estan basats enheurístiques i regles ad-hoc formulades per aplications específiques, fent-ne impossible l'extrapolació a noves condicions de l'entorn.En aquesta tesi proposem un marc probabilístic general per integrar el nombre de característiques de l'objecte que siguin necessàries, permetent que interactuin mútuament per tal d'estimar-ne el seu estat amb precisió, i per tant, estimar amb precisió la posició de l'objecte que s'està seguint. Aquest marc, s'utilitza posteriorment per dissenyar un algoritme de seguiment, que es valida en diverses seqüències de vídeo que contenen canvis abruptes de posició i il·luminació, camuflament de l'objecte i deformacions no rígides. Entre les característiques que s'han utilitzat per representar l'objecte, cal destacar la paramatrització robusta del color en un espai de color dependent de l'objecte, que permet distingir-lo del fons més clarament que altres espais de color típicament ulitzats al llarg de la literatura.En la darrera part de la tesi dissenyem una tècnica per re-il·luminar tant escenes estàtiques com en moviment, de les que s'en desconeix la geometria. La re-il·luminació es realitza amb un mètode 'basat en imatges', on la generació de les images de l'escena sota noves condicions d'il·luminació s'aconsegueix a partir de combinacions lineals d'un conjunt d'imatges de referència pre-capturades, i que han estat generades il·luminant l'escena amb patrons de llum coneguts. Com que la posició i intensitat de les fonts d'il.luminació que formen aquests patrons de llum es pot controlar, és natural preguntar-nos: quina és la manera més òptima d'il·luminar una escena per tal de reduir el nombre d'imatges de referència? Demostrem que la millor manera d'il·luminar l'escena (és a dir, la que minimitza el nombre d'imatges de referència) no és utilitzant una seqüència de fonts d'il·luminació puntuals, com es fa generalment, sinó a través d'una seqüència de patrons de llum d'una base d'il·luminació depenent de l'objecte. És important destacar que quan es re-il·luminen seqüències de vídeo, les imatges successives s'han d'alinear respecte a un sistema de coordenades comú. Com que cada imatge ha estat generada per un patró de llum diferent il·uminant l'escena, es produiran canvis d'il·luminació bruscos entre imatges de referència consecutives. Sota aquestes circumstàncies, el mètode de seguiment proposat en aquesta tesi juga un paper fonamental. Finalment, presentem diversos resultats on re-il·luminem seqüències de vídeo reals d'objectes i cares d'actors en moviment. En cada cas, tot i que s'adquireix un únic vídeo, som capaços de re-il·luminar una i altra vegada, controlant la direcció de la llum, la seva intensitat, i el color.Motion analysis and object tracking has been one of the principal focus of attention over the past two decades within the computer vision community. The interest of this research area lies in its wide range of applicability, extending from autonomous vehicle and robot navigation tasks, to entertainment and virtual reality applications.Even though impressive results have been obtained in specific problems, object tracking is still an open problem, since available methods are prone to be sensitive to several artifacts and non-stationary environment conditions, such as unpredictable target movements, gradual or abrupt changes of illumination, proximity of similar objects or cluttered backgrounds. Multiple cue integration has been proved to enhance the robustness of the tracking algorithms in front of such disturbances. In recent years, due to the increasing power of the computers, there has been a significant interest in building complex tracking systems which simultaneously consider multiple cues. However, most of these algorithms are based on heuristics and ad-hoc rules formulated for specific applications, making impossible to extrapolate them to new environment conditions.In this dissertation we propose a general probabilistic framework to integrate as many object features as necessary, permitting them to mutually interact in order to obtain a precise estimation of its state, and thus, a precise estimate of the target position. This framework is utilized to design a tracking algorithm, which is validated on several video sequences involving abrupt position and illumination changes, target camouflaging and non-rigid deformations. Among the utilized features to represent the target, it is important to point out the use of a robust parameterization of the target color in an object dependent colorspace which allows to distinguish the object from the background more clearly than other colorspaces commonly used in the literature.In the last part of the dissertation, we design an approach for relighting static and moving scenes with unknown geometry. The relighting is performed through an -image-based' methodology, where the rendering under new lighting conditions is achieved by linear combinations of a set of pre-acquired reference images of the scene illuminated by known light patterns. Since the placement and brightness of the light sources composing such light patterns can be controlled, it is natural to ask: what is the optimal way to illuminate the scene to reduce the number of reference images that are needed? We show that the best way to light the scene (i.e., the way that minimizes the number of reference images) is not using a sequence of single, compact light sources as is most commonly done, but rather to use a sequence of lighting patterns as given by an object-dependent lighting basis. It is important to note that when relighting video sequences, consecutive images need to be aligned with respect to a common coordinate frame. However, since each frame is generated by a different light pattern illuminating the scene, abrupt illumination changes between consecutive reference images are produced. Under these circumstances, the tracking framework designed in this dissertation plays a central role. Finally, we present several relighting results on real video sequences of moving objects, moving faces, and scenes containing both. In each case, although a single video clip was captured, we are able to relight again and again, controlling the lighting direction, extent, and color.Postprint (published version

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

Learnt quasi-transitive similarity for retrieval from large collections of faces

We are interested in identity-based retrieval of face sets from large unlabelled collections acquired in uncontrolled environments. Given a baseline algorithm for measuring the similarity of two face sets, the meta-algorithm introduced in this paper seeks to leverage the structure of the data corpus to make the best use of the available baseline. In particular, we show how partial transitivity of inter-personal similarity can be exploited to improve the retrieval of particularly challenging sets which poorly match the query under the baseline measure. We: (i) describe the use of proxy sets as a means of computing the similarity between two sets, (ii) introduce transitivity meta-features based on the similarity of salient modes of appearance variation between sets, (iii) show how quasi-transitivity can be learnt from such features without any labelling or manual intervention, and (iv) demonstrate the effectiveness of the proposed methodology through experiments on the notoriously challenging YouTube database.Postprin

Intelligent Agents for Active Malware Analysis

The main contribution of this thesis is to give a novel perspective on Active Malware Analysis modeled as a decision making process between intelligent agents. We propose solutions aimed at extracting the behaviors of malware agents with advanced Artificial Intelligence techniques. In particular, we devise novel action selection strategies for the analyzer agents that allow to analyze malware by selecting sequences of triggering actions aimed at maximizing the information acquired. The goal is to create informative models representing the behaviors of the malware agents observed while interacting with them during the analysis process. Such models can then be used to effectively compare a malware against others and to correctly identify the malware famil

Multi-games and bayesian nash equilibriums

A growing awareness of the prominent role the environment plays in multi-agent systems has led to gradual acceptance of its importance by the multi-agent system community in general. Within this line of research, we propose a new class of games, called Multi-Games. A Multi-game is one in which a given number of players play a fixed finite number of basic games simultaneously. The basic games in a multi-game can be regarded as different environments for the players, and, in particular, we submit that multi-games can be used to model investment in multiple national and continental markets within a global economy. Furthermore, when the players' weights for different games in the multi-game are classed as private information or as types with given conditional probability distributions, we obtain a particular class of Bayesian games. The main contribution of this thesis is to illustrate how, for the class of so-called completely pure regular multi-games with finite sets of types, the Nash equilibria of the basic games can be used to compute a Bayesian Nash equilibrium in multi-games, with complexity independent of the number of types. Following the presentation of the main results, the thesis presents two algorithms that allow us to establish whether we have a Bayesian Nash equilibrium which can be determined with lower computational complexity.Open Acces