Television composes the largest share of media market in the Czech Republic. Large scope for addressing a broad audience is created and investments of companies constitute a major share of income of television stations. These stations with their program focus on various groups of viewers which creates a space for submitters of advertisement to focus their marketing activities on selected target groups of viewers. Considering the fact that television constitutes such massive communication channel, differences among advertisements on individual television stations cease to exist. The objective of my thesis is to find out, if companies still apply theoretical recommendations regarding segmentation, targeting and suitability of selected strategy when submitting advertisements. To find answers to researched question, a data file containing all advertisements broadcasted by television stations in 2011, has been analyzed in this thesis. Research of data has been performed using cluster analysis. Upon results of this thesis, suggestions for improvement of researched condition of companies have been recommended.

This thesis present research work conducted in the field of retina image analysis. More specifically, the work is directed at the application of texture analysis technique for the segmentation of common retinal landmark and for retina image classification. The main challenge in this research is in identifying the suitable texture measurement for retina images. In this research we proposed the used of texture measurement based on Binary Robust Independent Elementary Features (BRIEF). BRIEF measure texture by performing an intensity comparison in a local image patch, thus it is very fast to compute and tolerant to any monotonic increase or decrease of image intensities, which makes the descriptor invariant to illumination. The performance of BRIEF as texture measurement is first shown in an experiment involving texture classification and segmentation using common texture datasets. The result demonstrates good performance from BRIEF in this experiment. BRIEF is next used in two applications of retinal image analysis, namely optic disc segmentation and glaucoma classification. In the former, we proposed the used of pixel classification using BRIEF as textural features and circular template matching to segment the optic disc. In addition, an extension of BRIEF called Rotation Invariant BRIEF (OBRIEF) is later proposed to improve the segmentation result. For glaucoma classification, we described two approaches for glaucoma classification using BRIEF/OBRIEF features. The first is based on determination of cup to disc ratio (CDR) and the second is classification using image features i.e. BRIEF features. Overall, our preliminary results on using BRIEF as texture measurement for retinal image analysis are encouraging and demonstrate that it has the potential to be used in retina image analysis.

L'orientation marché est une notion apparue dans les années 1990. Faisant l'objet d'un fort intérêt de la part de nombreux chercheurs, elle pourrait s'avérer être une réponse efficiente à la complexification des environnements respectifs des entreprises ainsi que celle des consommateurs. Pourtant, méconnu par de nombreux praticiens, ce concept fait l'objet de mauvaises interprétations, lesquelles ne reflètent pas réellement son grand potentiel stratégique. Par ailleurs, la grande majorité des recherches réalisées portent sur l’effet de l'orientation marché sur la performance globale d'une entreprise. Au travers de ce travail, nous avons ainsi souhaité analyser si, dans un contexte spécifique, l'intégration de cette philosophie d'entreprise pouvait posséder ou non un effet direct sur l'implémentation d'un outil stratégique souvent considéré comme étant l'un des fondements du marketing : le triptyque segmentation-ciblage-positionnement. / Market orientation was created during the Nineties. It could be considered as an efficient answer to the complexification of the companies and consumers environments. In this state of mind, it provides a strong interest for numerous researchers. This notion is unknown and badly interpreted by a majority of practitioners, despite its important strategic potential. Moreover, the big majority of researches linked with this topic are dealing with the effect of its integration on the global performance of a company. We wanted to analyze through this academic work if in a specific context, the integration of the market orientation philosophy could have a direct effect on the implementation of a strategic tool: the segmentation-targeting-positioning process. This triptych is indeed often considered as a basis of marketing strategy.

The aim of the thesis is to make segmentation of leisure activities market, to uncover important customer segments and describe the differences in their behavior and feature. Is used the knowledge of social sciences, particularly economics and sociology, with the help of which is described relationship of lifestyle and consumer behavior. The data come from research focused on consumer and media behavior and its relation to population lifestyle. Data were subjected to factor and cluster analysis.

This Master's Thesis is focusing on the typology of consumers in the apparel industry. The thesis is comparing two different typologies. The first one is based on the data of the project Fashion 2012, the second one on the analysis of data MML-TGI. The thesis is also including a detailed description of the segments and evaluation of the positive and negative aspects of these projects, possible recommendations and proposals for new typologies. Next sections are focusing on the theoretical aspects and characteristics of the Czech market. It is fully possible to use the results in practice. Thanks to them, the companies can obtain a comprehensive overview of the current trends and customers of this market.

[pt] A ocupação informal nas periferias dos centros urbanos brasileiros cresce de modo acelerado. O Sensoriamento Remoto provê técnicas eficientes para medir esta expansão. Em cidades com topografia acidentada, como o Rio de Janeiro, a expansão inicial, preponderantemente horizontal, acaba dando lugar à expansão vertical, à medida que novos pavimentos vão sendo construídos sobre edificações já existentes. Para estimar o crescimento de assentamentos deste tipo requerem-se, portanto, técnicas de medição 3D. Esta pesquisa propõe um método para produção de Modelos Digitais de Superfície (MDS) a partir de pares estereoscópicos de imagens fotogramétricas digitais. O método tem como entrada um MDS inicial calculado a partir de um par de imagens estereoscópicas sem qualquer conhecimento a priori da semântica da cena imageada. O MDS de entrada é então refinado levando-se em conta informação relativa à geometria das edificações identificadas. O método baseia-se no conhecimento a priori de que edificações em assentamentos informais de grandes centros urbanos no Brasil têm em geral altura aproximadamente constante. O processo envolve três passos principais: primeiramente são identificados os topos das edificações em cada par de imagens estereoscópicas. Em seguida, as regiões de topo homólogas são pareadas. O terceiro passo envolve a extração 3D das edificações. Ao final, obtém-se um MDS mais exato do que o inicial, além de uma imagem rotulada com a semântica dos objetos identificados. Os resultados obtidos com base nos experimentos realizados sobre imagens aéreas de uma área teste do município do Rio de Janeiro apresentaram uma melhora significativa de acurácia relativamente ao MDS de entrada. / [en] Informal settlements in many Brazilian urban centers are growing up quickly. Remote Sensing techniques provide a cost-effective mean to measure such an expansion. In cities with rough topography, like the city of Rio de Janeiro, the initial expansion, predominantly horizontal, is gradually being shifted to vertical, as new floors are being built on the existing buildings. To estimate the changing of such type of buildings, 3D measuring techniques are required. This research proposes a method for generating Digital Surface Model (DSM) from digital photogrammetry techniques. The method takes an initial DSM calculated from a pair of stereoscopic images as input. These models have no knowledge of image scenes semantics. The input DSM is refined taking into account the information about the geometry of buildings identified by a process of segmentation and interpretation applied to both images of the stereo pair. The method is based on a priori knowledge that buildings from informal settlements in large urban centers in Brazil generally have their roof tops at approximately constant height (flat roofs). The process involves three main steps: firstly the tops of buildings are identified in each pair of stereoscopic images. Then, the regions corresponding to the top of the buildings are matched. The third step involves the 3D extraction of those buildings. Finally, the method generates a more accurate DSM plus an image label with the semantics of the identified buildings. The results obtained in the experiments on airborne imagery of a test area in the city of Rio de Janeiro showed a significant improvement in the original DSM, as it takes into account the semantics of the 3D reconstructed buildings.

Orientador: Alexandre Xavier Falcão / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-27T03:30:14Z (GMT). No. of bitstreams: 1 PhellanAro_Renzo_M.pdf: 4734368 bytes, checksum: 27f258762d497b786df234144140e47a (MD5) Previous issue date: 2014 / Resumo: A segmentação de imagens médicas consiste de duas tarefas fortemente acopladas: reconhecimento e delineamento. O reconhecimento indica a localização aproximada de um objeto, enquanto o delineamento define com precisão sua extensão espacial na imagem. O reconhecimento também verifica a corretude do delineamento do objeto. Os seres humanos são superiores aos computadores na tarefa de reconhecimento, enquanto o contrário acontece no delineamento. A segmentação manual, por exemplo, é geralmente passível de erro, tediosa, demorada e sujeita à variabilidade. Portanto, os métodos de segmentação interativa mais eficaces limitam a intervenção humana ao reconhecimento. No caso das imagens médicas, os objetos podem ser as estruturas anatômicas do corpo humano, como órgãos, sistemas e tumores. Sua segmentação é uma fase fundamental para obter medidas, como seus tamanhos e distâncias, para poder realizar sua análise quantitativa. A visualização de suas formas em 3D também é importante para sua análise qualitativa. Ambas análises podem ajudar os especialistas a estudar os fenómenos anatômicos e fisiológicos do corpo humano, diferenciar situações normais e anormais, diagnosticar doenças, estabelecer tratamentos, monitorar a evolução dos tumores e planejar procedimentos cirúrgicos. No entanto, um desafio crucial para a segmentação automática é obter um modelo matemático que possa substituir os seres humanos, capaz de reconhecer as estruturas anatômicas com base em suas características de textura e forma. Esta dissertação estuda duas aproximações importantes para este problema: os Modelos Estatísticos da Forma do Objeto (SOSMs) e os Modelos Nebulosos da Forma do Objeto (FOSMs). Os SOSMs são popularmente conhecidos como métodos de segmentação baseados em atlas e têm sido utilizados amplamente e com suceso em muitas aplicações. Porém, eles precisam do registro deformável das imagens --- um processo demorado que mapeia as imagens em um mesmo sistema de coordenadas (referência), que limita seu uso em estudos com grandes conjuntos de imagens. Os FOSMs são modelos mais recentes que podem ser significativamente mais eficientes que os SOSMs, mas precisam de métodos mais eficazes de reconhecimento e delineamento. Esta dissertação compara pela primeira vez os prós e contras dos SOSMs e FOSMs, utilizando conjuntos de imagens médicas de diferentes modalidades e estruturas anatômicas / Abstract: Image segmentation consists of two tightly coupled tasks: recognition and delineation. Recognition indicates the whereabouts of a desired object, while delineation precisely defines its spatial extent in the image. Recognition also verifies the correctness of the object's delineation. Humans are superior to computers in recognition and the other way around is valid for delineation. Manual segmentation, for instance, is usually considered error-prone, tedious, time-consuming, and subject to inter-observer variability. Therefore, the most effective interactive segmentation methods reduce human intervention to the recognition tasks. In medical images, objects may be body anatomical structures, such as organs, organ systems, and tumors. Their segmentation is a fundamental step to extract measures, such as sizes and distances for quantitative analysis. The visualization of their 3D shapes is also important for qualitative analysis. Both can help experts to study anatomical and physiological phenomena of the human body, differentiate between normal and abnormal, diagnose a disease, establish a treatment, monitor the evolution of a tumor, and plan a surgical procedure. However, a crucial challenge in automated segmentation is to obtain a surrogate mathematical model for humans, able to recognize the anatomy of such structures based on their texture and shape properties. This dissertation investigates two important approaches for this problem: the Statistical Object Shape Models (SOSMs) and the Fuzzy Object Shape Models (FOSMs). SOSMs are popularly known as atlas-based segmentation methods and have been extensively and successfully used in many applications. However, they require deformable image registration --- a time-consuming operation to map images into a common (reference) coordinate system, which limits their use in studies with large image datasets. FOSMs are more recent and can be significantly more efficient than SOSMs, but they require more effective recognition and delineation methods. This dissertation compares for the first time the pros and cons of SOSMs and FOSMs, using image datasets from distinct medical imaging modalities and anatomical structures of the human body / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

La planification à des niveaux d’abstraction plus élevés est essentielle lorsqu’il s’agit de résoudre des tâches à long horizon avec des complexités hiérarchiques. Pour planifier avec succès à un niveau d’abstraction donné, un agent doit comprendre le fonctionnement de l’environnement à ce niveau particulier. Cette compréhension peut être implicite en termes de politiques, de fonctions de valeur et de modèles, ou elle peut être définie explicitement. Dans ce travail, nous introduisons les concepts comme un moyen de représenter et d’accumuler explicitement des informations sur l’environnement. Les concepts sont définis en termes de transition d’état et des conditions requises pour que cette transition ait lieu. La simplicité de cette définition offre flexibilité et contrôle sur le processus d’apprentissage. Étant donné que les concepts sont de nature hautement interprétable, il est facile d’encoder les connaissances antérieures et d’intervenir au cours du processus d’apprentissage si nécessaire. Cette définition facilite également le transfert de concepts entre différents domaines. Les concepts, à un niveau d’abstraction donné, sont intimement liés aux compétences, ou actions temporellement abstraites. Toutes les transitions d’état suffisamment importantes pour être représentées par un concept se produisent après l’exécution réussie d’une compétence. En exploitant cette relation, nous introduisons un cadre qui facilite l’apprentissage tout au long de la vie et le raffinement des concepts à différents niveaux d’abstraction. Le cadre comporte trois volets: Le sytème 1 segmente un flux d’expérience (par exemple une démonstration) en une séquence de compétences. Cette segmentation peut se faire à différents niveaux d’abstraction. Le sytème 2 analyse ces segments pour affiner et mettre à niveau son ensemble de concepts, lorsqu’applicable. Le sytème 3 utilise les concepts disponibles pour générer un graphe de dépendance de sous-tâches. Ce graphe peut être utilisé pour planifier à différents niveaux d’abstraction. Nous démontrons l’applicabilité de ce cadre dans l’environnement hiérarchique 2D CRAFT. Nous effectuons des expériences pour explorer comment les concepts peuvent être appris de différents flux d’expérience et comment la qualité de la base de concepts affecte l’optimalité du plan général. Dans les tâches avec des dépendances de sous-tâches complexes, où la plupart des algorithmes ne parviennent pas à se généraliser ou prennent un temps impraticable à converger, nous démontrons que les concepts peuvent être utilisés pour simplifier considérablement la planification. Ce cadre peut également être utilisé pour comprendre l’intention d’une démonstration donnée en termes de concepts. Cela permet à l’agent de répliquer facilement la démonstration dans différents environnements. Nous montrons que cette méthode d’imitation est beaucoup plus robuste aux changements de configuration de l’environnement que les méthodes traditionnelles. Dans notre formulation du problème, nous faisons deux hypothèses: 1) que nous avons accès à un ensemble de compétences suffisamment exhaustif, et 2) que notre agent a accès à des environnements de pratique, qui peuvent être utilisés pour affiner les concepts en cas de besoin. L’objectif de ce travail est d’explorer l’aspect pratique des concepts d’apprentissage comme moyen d’améliorer la compréhension de l’environnement. Dans l’ensemble, nous démontrons que les concepts d’apprentissage / Planning at higher levels of abstraction is critical when it comes to solving long horizon tasks with hierarchical complexities. To plan successfully at a given level of abstraction, an agent must have an understanding of how the environment functions at that particular level. This understanding may be implicit in terms of policies, value functions, and world models, or it can be defined explicitly. In this work, we introduce concepts as a means to explicitly represent and accumulate information about the environment. Concepts are defined in terms of a state transition and the conditions required for that transition to take place. The simplicity of this definition offers flexibility and control over the learning process. Since concepts are highly interpretable in nature, it is easy to encode prior knowledge and intervene during the learning process if necessary. This definition also makes it relatively straightforward to transfer concepts across different domains wherever applicable. Concepts, at a given level of abstraction, are intricately linked to skills, or temporally abstracted actions. All the state transitions significant enough to be represented by a concept occur only after the successful execution of a skill. Exploiting this relationship, we introduce a framework that aids in lifelong learning and refining of concepts across different levels of abstraction. The framework has three components: - System 1 segments a stream of experience (e.g. a demonstration) into a sequence of skills. This segmentation can be done at different levels of abstraction. - System 2 analyses these segments to refine and upgrade its set of concepts, whenever applicable. - System 3 utilises the available concepts to generate a sub-task dependency graph. This graph can be used for planning at different levels of abstraction We demonstrate the applicability of this framework in the 2D hierarchical environment CRAFT. We perform experiments to explore how concepts can be learned from different streams of experience, and how the quality of the concept base affects the optimality of the overall plan. In tasks with complex sub-task dependencies, where most algorithms fail to generalise or take an impractical amount of time to converge, we demonstrate that concepts can be used to significantly simplify planning. This framework can also be used to understand the intention of a given demonstration in terms of concepts. This makes it easy for the agent to replicate a demonstration in different environments. We show that this method of imitation is much more robust to changes in the environment configurations than traditional methods. In our problem formulation, we make two assumptions: 1) that we have access to a sufficiently exhaustive set of skills, and 2) that our agent has access to practice environments, which can be used to refine concepts when needed. The objective behind this work is to explore the practicality of learning concepts as a means to improve one’s understanding about the environment. Overall, we demonstrate that learning concepts can be a light-weight yet efficient way to increase the capability of a system.

L'iris est considérée comme une des modalités les plus robustes et les plus performantes en biométrie à cause de ses faibles taux d'erreurs. Ces performances ont été observées dans des situations contrôlées, qui imposent des contraintes lors de l'acquisition pour l'obtention d'images de bonne qualité. Relâcher ces contraintes, au moins partiellement, implique des dégradations de la qualité des images acquises et par conséquent une réduction des performances de ces systèmes. Une des principales solutions proposées dans la littérature pour remédier à ces limites est d'améliorer l'étape de segmentation de l'iris. L'objectif principal de ce travail de thèse a été de proposer des méthodes originales pour la segmentation des images dégradées de l'iris. Les chaînes de Markov ont été déjà proposées dans la littérature pour résoudre des problèmes de segmentation d'images. Dans ce cadre, une étude de faisabilité d'une segmentation non supervisée des images dégradées d'iris en régions par les chaînes de Markov a été réalisée, en vue d'une future application en temps réel. Différentes transformations de l'image et différentes méthodes de segmentation grossière pour l'initialisation des paramètres ont été étudiées et comparées. Les modélisations optimales ont été introduites dans un système de reconnaissance de l'iris (avec des images en niveaux de gris) afin de produire une comparaison avec les méthodes existantes. Finalement une extension de la modélisation basée sur les chaînes de Markov cachées, pour une segmentation non supervisée des images d'iris acquises en visible, a été mise en place / Iris is considered as one of the most robust and efficient modalities in biometrics because of its low error rates. These performances were observed in controlled situations, which impose constraints during the acquisition in order to have good quality images. The renouncement of these constraints, at least partially, implies degradations in the quality of the acquired images and it is therefore a degradation of these systems’ performances. One of the main proposed solutions in the literature to take into account these limits is to propose a robust approach for iris segmentation. The main objective of this thesis is to propose original methods for the segmentation of degraded images of the iris. Markov chains have been well solicited to solve image segmentation problems. In this context, a feasibility study of unsupervised segmentation into regions of degraded iris images by Markov chains was performed. Different image transformations and different segmentation methods for parameters initialization have been studied and compared. Optimal modeling has been inserted in iris recognition system (with grayscale images) to produce a comparison with the existing methods. Finally, an extension of the modeling based on the hidden Markov chains has been developed in order to realize an unsupervised segmentation of the iris images acquired in visible light

Wound assessment using a smartphone image has recently emerged as a novel way to provide actionable feedback to patients and caregivers. Wound segmentation is an important step in image-based wound assessment, after which the wound area can be analyzed. Semantic segmentation algorithms for wounds assume favorable lighting conditions. However, smartphone wound imaging in natural environments can encounter adverse lighting that can cause several errors during semantic segmentation of wound images, which in turn affects the wound analysis. In this work, we study and characterize the effects of adverse lighting on the accuracy of semantic segmentation of wound images. Our findings inform a deep learning-based approach to mitigate the adverse effects. We make three main contributions in this work. First, we create the first large-scale Illumination Varying Dataset (IVDS) of 55440 images of a wound moulage captured under systematically varying illumination conditions and with different camera types and settings. Second, we characterize the effects of changing light intensity on U-Net’s wound semantic segmentation accuracy and show the luminance of images to be highly correlated with the wound segmentation performance. Especially, we show low-light conditions to deteriorate segmentation performance highly. Third, we improve the wound Dice scores of U-Net for low-light images to up to four times the baseline values using a deep learning mitigation method based on the Retinex theory. Our method works well in typical illumination levels observed in homes/clinics as well for a wide gamut of lighting like very dark conditions (20 Lux), medium-intensity lighting (750 - 1500 Lux), and even very bright lighting (6000 Lux).