本研究係透過對全球暖化科普出版品的圖像再現分析，研究圖像與公眾溝通暖化議題時的角色、功能與意義，進而討論圖像設計與公眾參與間的關係發展。圖像分析的來源主要以2007年IPCC發表第四次報告後，全球暖化科學知識的發展成熟、確定人為造成暖化及國際社會採取行動等社會氛圍為觀察點，以臺灣出版市場為選材來源，蒐集13本以圖文共構為主的科普文本，取徑符號學並應用家族相似性觀點於圖像再現的分析主軸。 研究者立論與分析思維的處理方式，是以宏觀的知識社會學理論視角，先行探索全球暖化知識的認識論及其在臺灣產製的研究議題，以及科學哲學、風險社會對此知識觀點的探討，再以社會建構論的角色切入，梳理社會建構論者如何分別從規範建構與權力建構的詮釋，分析全球暖化議題與社會間的互動發展關係，並與先前闡述理論形成辯證過程。研究者接著再從媒體再現全球暖化議題的相關研究，進行資料蒐集與分析，討論過去在風險議題下圖像被賦予的角色與功能。最後，研究者據成因面、影響面與解決面為圖像所依附的敘事架構，整理分析出各面向下圖像家族再現的論述主題、議題與意義，將其結果分析與處理後，再與先前的學理對話，形成本研究的發現與結論。 本研究發現成因面圖像多再現科學共識，影響面圖像側重全球暖化帶來的災難事件及不確定性，解決面圖像則再現技術與行為規範的論述。在圖像輔助文本論述的角色界定下，各方面有以下特徵與意義： 首先，文本在描述科學事實與因果關係時，圖像再現會顯示科學文化圈的慣用符碼，如模型、數字、圖表、科學家研究的歷史相片等融入圖像再現，形塑能摒除懷疑、建構嚴謹可信的知識論述。 其次，解釋全球暖化影響時，圖像再現則傾向表現災難正在發生，在各地不斷游移與變動的不確性，來表徵全球暖化風險的系統性與連鎖效應。圖像再現會善用人們認知習慣上的比較手法、恐懼符碼來建構意義：直接影響面上，會以比較法配合描述改變中的自然景觀（融冰、海平面上升），或是各地災難事件的受創場景，以輔助闡述暖化持續進行的現象，並構連宗教文化的神話系統，象徵人類的罪與罰，以激起行動與重生的意念；衍生影響面上，圖像再現則以恐懼相反的修辭策略，如美麗、平和與可愛的動植物圖像及依賴自然生活與生存的人們、文化古都等景觀，搭配控訴暖化破壞的書寫，以建構等待救援受難的他者，圖像在散發出美感外隱藏生命共同體的道德訊息。 最後，文本在議論如何解決風險社會中複雜的系統性利益與需求問題時，再生能源的技術就在其中扮演著關鍵性的工具角色，其中又以太陽能、風能等圖像較常被引用。圖像再現則以車、房為載體，將回收技術、再生能源科技、生態技術的想像與論述置入其中，提供接受科技治理而無須改變現有生活的價值與態度。但是，風車圖像所再現的風場與發電廠，隱藏高技術門檻及科技專家治理的形態，隱藏由政府、產業與科技專家共治的大治理論述；而方舟圖像再現意指科學或科技行動須集思廣益才能事竟其功。在行為規範面，圖像再現節能減碳、綠化環境的行為圖標，輔助個人參與適應暖化行動論述，抗爭與遊行圖像的再現則指涉公眾參與科技民主的一種形式，卻忽略其他多元參與論述的圖像。上述研究發現與學理概念對照與分析後，本研究從公眾參與角度提出以下科普圖像設計的實務建議與創新見解： （一）圖像設計可減少註解式的圖文關係，以故事發揮圖像敘事潛能。 （二）公眾涉入感為理念，設計與公眾生活脈絡相關的圖像與論述。 （三）公眾能動性為理念，邀非專家設計圖像，建立情感與認知連接點。 （四）圖像用以轉化公眾的願景，專家與公眾共構小治理規劃的實踐。 公眾解讀科普與參與傳播，則能豐富媒體素養教育的理論的實踐範疇，圖像則發揮圖像激勵公眾參與的潛能。 / This study examines the roles, the functions, and the significance of images for communication with the public on global warming issues by analyzing image representations of popular science publications on global warming. The study also discusses the relationship development between image design and public participation. According to the study’s findings of popular science images, "cause images" represent scientific consensus, "effect images" correspond to catastrophic events and uncertainties resulting from global warming, and "resolution images" signify the descriptions of technologies and the norms of behaviors. Under the current defined role of images for the supporting text, we observe the following characteristics. First, when describing scientific facts and the cause-and-effect relationships in the content, the representing images of the symbols commonly used by the science culture circle, which include models, numbers, figures/tables, and historical photos, eliminate doubt and allow for the construction of significant and credible knowledgeable narratives. Second, when explaining the impact of global warming, the representing images often lean towards showing the shifting and changing uncertainties caused by disasters that are occurring in order to signify the systems and the chain reactions produced by the risks of global warming. Third and lastly, the study also explores the key roles of sustainable energy technologies for solving the problems of complex systems and the needs of society. The most common images cited are those of solar energy and wind energy. After the comparison and analysis of the abovementioned findings and the concepts of various theories, we provide practical suggestions and innovative insights for a scientific image design from the public participation perspective as follows. (1) Image design can reduce the needs for image annotation, and story-centered images retain great potential for illustration. (2) Public participation shall be considered as the basis for designing public life-correlated images and narratives. (3) Experts should design images based on the concept of public dynamics so as to establish a connection between emotion and recognition. (4) For the transformation of public thought using images, experts and the public should carry out the construction of a small plan together. The public perception of science and public participation in communication can enrich the practical areas of the theories of media literacy education. Images can additionally generate potential benefits through design of public participation.

Cette thèse explore l’utilisation de représentations hiérarchiques des images issues de la morphologie mathématique, les arbres des coupes, pour la recherche et la classification d’images. Différents types de structures arborescentes sont analysés et une nouvelle classification en deux superclasses est proposée, ainsi qu’une contribution à l’indexation et à la représentation de ces structures par des dendogrammes. Deux contributions à la recherche d’images sont proposées, l’une sur la détection de régions d’intérêt et l’autre sur la description de ces régions. Les régions MSER peuvent être détectées par un algorithme s’appuyant sur une représentation des images par arbres min et max. L’utilisation d’autres structures arborescentes sous-jacentes permet de détecter des régions présentant des propriétés de stabilité différentes. Un nouveau détecteur, basé sur les arbres des formes, est proposé et évalué en recherche d’images. Pour la description des régions, le concept de spectres de formes 2D permettant de décrire globalement une image est étendu afin de proposer un descripteur local, au pouvoir discriminant plus puissant. Ce nouveau descripteur présente de bonnes propriétés à la fois de compacité et d’invariance à la rotation et à la translation. Une attention particulière a été portée à la préservation de l’invariance à l’échelle. Le descripteur est évalué à la fois en classification d’images et en recherche d’images satellitaires. Enfin, une technique de simplification des arbres de coupes est présentée, qui permet à l’utilisateur de réévaluer les mesures du niveau d’agrégation des régions imposé par les arbres des coupes. / This thesis explores component trees, hierarchical structures from Mathematical Morphology, and their application to image retrieval and related tasks. The distinct component trees are analyzed and a novel classification into two superclasses is proposed, as well as a contribution to indexing and representation of the hierarchies using dendrograms. The first contribution to the field of image retrieval is in developing a novel feature detector, built upon the well-established MSER detection. The tree-based implementation of the MSER detector allows for changing the underlying tree in order to produce features of different stability properties. This resulted in the Tree of Shapes based Maximally Stable Region detector, leading to improvements over MSER in retrieval performance. Focusing on feature description, we extend the concept of 2D pattern spectra and adapt their global variant to more powerful, local schemes. Computed on the components of Min/Max-tree, they are histograms holding the information on distribution of image region attributes. The rotation and translation invariance is preserved from the global descriptor, while special attention is given to achieving scale invariance. We report comparable results to SIFT in image classification, as well as outperforming Morphology-based descriptors in satellite image retrieval, with a descriptor shorter than SIFT. Finally, a preprocessing or simplification technique for component trees is also presented, allowing the user to reevaluate the measures of region level of aggregation imposed on a component tree. The thesis is concluded by outlining the future perspectives based on the content of the thesis.

The aim of the diploma thesis "Media image of selected components of the Integrated Rescue System during a state of emergency in the Czech Republic in the daily newspapers Lidové noviny, Právo and Blesk" is to show the main features of reporting on the components of the Integrated Rescue System (IRS) during the state of emergency, which was announced in connection with the so-called coronavirus pandemic for the Czech Republic on March 12, 2020 and lasted continuously until until 17 May 2020. Two basic components of the IRS were selected for the analysis - the Medical Rescue Service of the Czech Republic and the Fire and Rescue Service of the Czech Republic, and one of the other components - the Prison Service of the Czech Republic. The depiction of these components was analyzed in the daily newspapers Právo and Lidové noviny, and the daily newspaper Blesk was chosen as the representative of the tabloid press. A qualitative analysis of the collected text showed what topics the daily newspapers dealt with in connection with these IRS units and what role they assigned to them in the "story of the pandemic". The thesis shows in what types of reports the monitored IRS units occurred, what stories were offered to the audience in connection with these components and what myths depicted these stories....

This work deals with the creation of several interactive web applets for education of vector graphics. Described is the image signal, his discretization, vector and bitmap types of image record. Further they describe selected vector curves, their properties, algorithms for their construction and usage. The principles of rasterization of basic vector objects are explained. Using the Figma tool, graphical user interfaces for each applet are designed. These applets are implemented using HTML and Javascript. The implemented applets are placed on web pages that are used in computer graphics education.

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Yes / Faces convey much information. Interestingly we humans have a remarkable ability of identifying, extracting, and interpreting this information. Recently automatic facial ageing (AFA) has gained popularity due to its numerous applications which include search for missing people, biometrics, and multimedia. The problem of AFA is faced with various challenges, including incomplete training datasets, unrestrained environments, ethnic and gender variations to mention but a few. This work presents a new approach to automatic facial ageing which involves the development of a person specific facial ageing system. A color based Active Appearance Model (AAM) is used to extract facial features. Then, regression is used to model an age estimator. Age synthesis is achieved by computing a solution that minimises the distance from the original face with the use of constrained regression. The model is tested on a challenging database of single image per person. Initial results suggest that plausible images can be rerendered at different ages, automatically using the AAM representation. Using the constrained regressor we are guaranteed to get estimated ages that are exact for an individual at a given age.

La représentation d'image est au cœur de beaucoup d'algorithmes de vision par ordinateur. Elle intervient notamment dans des tâches de reconnaissance de catégories visuelles comme la classification ou la détection d'objets. Dans ce contexte, la représentation "sac de mot visuel" (Bag of Visual Words ou BoVW en anglais) est l'une des méthodes de référence. Dans cette thèse, nous nous appuyons sur ce modèle pour proposer des représentations d'images discriminantes. Dans la première partie, nous présentons une nouvelle approche simple et efficace pour prendre en compte des informations spatiales dans le modèle BoVW. Son principe est de considérer l'orientation et la longueur de segments formés par des paires de descripteurs similaires. Une notion de "softsimilarité" est introduite pour définir ces relations intra et inter mots visuels. Nous montrons expérimentalement que notre méthode ajoute une information discriminante importante au modèle BoVW et que cette information est complémentaire aux méthodes de l'état de l'art. Ensuite, nous nous focalisons sur la description de l'information couleur. Contrairement aux approches traditionnelles qui s'appuient sur des descriptions invariantes aux changements d'éclairage, nous proposons un descripteur basé sur le pouvoir discriminant. Nos expérimentations permettent de conclure que ce descripteur apprend automatiquement un certain degré d'invariance photométrique tout en surclassant les descripteurs basés sur cette invariance photométrique. De plus, combiné avec un descripteur de forme, le descripteur proposé donne des résultats excellents sur quatre jeux de données particulièrement difficiles. Enfin, nous nous intéressons à la représentation de la couleur à partir de la réflectance multispectrale des surfaces observées, information difficile à extraire sans instruments sophistiqués. Ainsi, nous proposons d'utiliser l'écran et la caméra d'un appareil portable pour capturer des images éclairées par les couleurs primaires de l'écran. Trois éclairages et trois réponses de caméra produisent neuf valeurs pour estimer la réflectance. Les résultats montrent que la précision de la reconstruction spectrale est meilleure que celle estimée avec un seul éclairage. Nous concluons que ce type d'acquisition est possible avec des appareils grand public tels que les tablettes, téléphones ou ordinateurs portables / Image representation is in the heart of many computer vision algorithms. Different computer vision tasks (e.g. classification, detection) require discriminative image representations to recognize visual categories. In a nutshell, the bag-of-visual-words image representation is the most successful approach for object and scene recognition. In this thesis, we mainly revolve around this model and search for discriminative image representations. In the first part, we present a novel approach to incorporate spatial information in the BoVW method. In this framework, we present a simple and efficient way to infuse spatial information by taking advantage of the orientation and length of the segments formed by pairs of similar descriptors. We introduce the notion of soft-similarity to compute intra and inter visual word spatial relationships. We show experimentally that, our method adds important discriminative information to the BoVW method and complementary to the state-of-the-art method. Next, we focus on color description in general. Differing from traditional approaches of invariant description to account for photometric changes, we propose discriminative color descriptor. We demonstrate that such a color description automatically learns a certain degree of photometric invariance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape descriptor, the proposed color descriptor obtain excellent results on four challenging data sets.Finally, we focus on the most accurate color representation i.e. multispectral reflectance which is an intrinsic property of a surface. Even with the modern era technological advancement, it is difficult to extract reflectance information without sophisticated instruments. To this end, we propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results show that the accuracy of the spectral reconstruction improves significantly over the spectral reconstruction based on a single illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops

Image representation is in the heart of many computer vision algorithms. Different computer vision tasks (e.g. classification, detection) require discriminative image representations to recognize visual categories. In a nutshell, the bag-of-visual-words image representation is the most successful approach for object and scene recognition. In this thesis, we mainly revolve around this model and search for discriminative image representations. In the first part, we present a novel approach to incorporate spatial information in the BoVW method. In this framework, we present a simple and efficient way to infuse spatial information by taking advantage of the orientation and length of the segments formed by pairs of similar descriptors. We introduce the notion of soft-similarity to compute intra and inter visual word spatial relationships. We show experimentally that, our method adds important discriminative information to the BoVW method and complementary to the state-of-the-art method. Next, we focus on color description in general. Differing from traditional approaches of invariant description to account for photometric changes, we propose discriminative color descriptor. We demonstrate that such a color description automatically learns a certain degree of photometric invariance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape descriptor, the proposed color descriptor obtain excellent results on four challenging data sets.Finally, we focus on the most accurate color representation i.e. multispectral reflectance which is an intrinsic property of a surface. Even with the modern era technological advancement, it is difficult to extract reflectance information without sophisticated instruments. To this end, we propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results show that the accuracy of the spectral reconstruction improves significantly over the spectral reconstruction based on a single illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops

Le problème de représentation d’image est au cœur du domaine de vision. Le choix de représentation d’une image change en fonction de la tâche que nous voulons étudier. Un problème de recherche d’image dans des grandes bases de données exige une représentation globale compressée, alors qu’un problème de segmentation sémantique nécessite une carte de partitionnement de ses pixels. Les techniques d’apprentissage statistique sont l’outil principal pour la construction de ces représentations. Dans ce manuscrit, nous abordons l’apprentissage des représentations visuels dans trois problèmes différents : la recherche d’image, la correspondance sémantique et classification d’image. Premièrement, nous étudions la représentation vectorielle de Fisher et sa dépendance sur le modèle de mélange Gaussien employé. Nous introduisons l’utilisation de plusieurs modèles de mélange Gaussien pour différents types d’arrière-plans, e.g., différentes catégories de scènes, et analyser la performance de ces représentations pour objet classification et l’impact de la catégorie de scène en tant que variable latente. Notre seconde approche propose une extension de la représentation l’exemple SVM pipeline. Nous montrons d’abord que, en remplaçant la fonction de perte de la SVM par la perte carrée, on obtient des résultats similaires à une fraction de le coût de calcul. Nous appelons ce modèle la « square-loss exemplar machine », ou SLEM en anglais. Nous introduisons une variante de SLEM à noyaux qui bénéficie des même avantages computationnelles mais affiche des performances améliorées. Nous présentons des expériences qui établissent la performance et l’efficacité de nos méthodes en utilisant une grande variété de représentations de base et de jeux de données de recherche d’images. Enfin, nous proposons un réseau neuronal profond pour le problème de l’établissement sémantique correspondance. Nous utilisons des boîtes d’objets en tant qu’éléments de correspondance pour construire une architecture qui apprend simultanément l’apparence et la cohérence géométrique. Nous proposons de nouveaux scores géométriques de cohérence adaptés à l’architecture du réseau de neurones. Notre modèle est entrainé sur des paires d’images obtenues à partir des points-clés d’un jeu de données de référence et évaluées sur plusieurs ensembles de données, surpassant les architectures d’apprentissage en profondeur récentes et méthodes antérieures basées sur des caractéristiques artisanales. Nous terminons la thèse en soulignant nos contributions et en suggérant d’éventuelles directions de recherche futures. / The problem of image representation is at the heart of computer vision. The choice of feature extracted of an image changes according to the task we want to study. Large image retrieval databases demand a compressed global vector representing each image, whereas a semantic segmentation problem requires a clustering map of its pixels. The techniques of machine learning are the main tool used for the construction of these representations. In this manuscript, we address the learning of visual features for three distinct problems: Image retrieval, semantic correspondence and image classification. First, we study the dependency of a Fisher vector representation on the Gaussian mixture model used as its codewords. We introduce the use of multiple Gaussian mixture models for different backgrounds, e.g. different scene categories, and analyze the performance of these representations for object classification and the impact of scene category as a latent variable. Our second approach proposes an extension to the exemplar SVM feature encoding pipeline. We first show that, by replacing the hinge loss by the square loss in the ESVM cost function, similar results in image retrieval can be obtained at a fraction of the computational cost. We call this model square-loss exemplar machine, or SLEM. Secondly, we introduce a kernelized SLEM variant which benefits from the same computational advantages but displays improved performance. We present experiments that establish the performance and efficiency of our methods using a large array of base feature representations and standard image retrieval datasets. Finally, we propose a deep neural network for the problem of establishing semantic correspondence. We employ object proposal boxes as elements for matching and construct an architecture that simultaneously learns the appearance representation and geometric consistency. We propose new geometrical consistency scores tailored to the neural network’s architecture. Our model is trained on image pairs obtained from keypoints of a benchmark dataset and evaluated on several standard datasets, outperforming both recent deep learning architectures and previous methods based on hand-crafted features. We conclude the thesis by highlighting our contributions and suggesting possible future research directions.

This work deal with determination topohraphic parameters of a randomly rough surface by the help of method of shearing interferometry. It is a optical method for determination surface roughness. The basic idea is based of on deformation interference strips which are made by interference of the same mutually translated monochrome luminous wavefronts. The wavefront is created after transit or reflection monochrome lights from the surface of a studied sample. The wavefronts creates picture with deformed interference strips , which carries information about character of the surface. This information can be profited by algorithms of image processing from the picture . The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.