475 research outputs found

    Scalable Exploration of Complex Objects and Environments Beyond Plain Visual Replication​

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    Digital multimedia content and presentation means are rapidly increasing their sophistication and are now capable of describing detailed representations of the physical world. 3D exploration experiences allow people to appreciate, understand and interact with intrinsically virtual objects. Communicating information on objects requires the ability to explore them under different angles, as well as to mix highly photorealistic or illustrative presentations of the object themselves with additional data that provides additional insights on these objects, typically represented in the form of annotations. Effectively providing these capabilities requires the solution of important problems in visualization and user interaction. In this thesis, I studied these problems in the cultural heritage-computing-domain, focusing on the very common and important special case of mostly planar, but visually, geometrically, and semantically rich objects. These could be generally roughly flat objects with a standard frontal viewing direction (e.g., paintings, inscriptions, bas-reliefs), as well as visualizations of fully 3D objects from a particular point of views (e.g., canonical views of buildings or statues). Selecting a precise application domain and a specific presentation mode allowed me to concentrate on the well defined use-case of the exploration of annotated relightable stratigraphic models (in particular, for local and remote museum presentation). My main results and contributions to the state of the art have been a novel technique for interactively controlling visualization lenses while automatically maintaining good focus-and-context parameters, a novel approach for avoiding clutter in an annotated model and for guiding users towards interesting areas, and a method for structuring audio-visual object annotations into a graph and for using that graph to improve guidance and support storytelling and automated tours. We demonstrated the effectiveness and potential of our techniques by performing interactive exploration sessions on various screen sizes and types ranging from desktop devices to large-screen displays for a walk-up-and-use museum installation. KEYWORDS - Computer Graphics, Human-Computer Interaction, Interactive Lenses, Focus-and-Context, Annotated Models, Cultural Heritage Computing

    Lightweight MobileNet Model for Image Tempering Detection

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    In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images

    Human Movement Recognition using Deep Learning on Visualized CSI Wi-Fi data

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    Wireless signal transmission is an intricate process, significantly influenced by the environment within which it operates. Notably, the mobility of various elements within this environment, such as the parts of a human body, distinctly modifies the manner in which these signals are reflected. These alterations subsequently cause changes in Channel State Information (CSI) data captured by Wi-Fi routers. Intriguingly, specific human behaviors can be detected through a meticulous examination of the data streams from CSI. These behaviors, representing diverse activities, can be identified by processing the data streams and juxtaposing them against predefined models. The recognition of these activities hinges on discerning patterns within the CSI data, reflecting the relationship between human movement and the variation in channel state information. A variety of techniques have been developed to explore and understand these patterns, with machine learning emerging as the most popular and effective tool. Machine learning techniques are harnessed to develop sophisticated models capable of correlating variations in channel state information with specific human movements. These correlations enable the prediction and identification of human activities based on changes in CSI data. This research focuses on further exploring this intriguing intersection of human activity, wireless signal processing, and machine learning. It aims to provide a deeper understanding of these correlations and develop more effective models for human activity recognition. More specifically, with this work we attempt to to explore new way of using the CSI data in Deep Learning tasks. That is by using the visualized amplitude of signals and correlate them to certain activities

    Critical Heritage Studies and the Futures of Europe

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    Cultural and natural heritage are central to ‘Europe’ and ‘the European project’. They were bound up in the emergence of nation-states in the eighteenth and nineteenth centuries, where they were used to justify differences over which border conflicts were fought. Later, the idea of a ‘common European heritage’ provided a rationale for the development of the European Union. Now, the emergence of ‘new’ populist nationalisms shows how the imagined past continues to play a role in cultural and social governance, while a series of interlinked social and ecological crises are changing the ways that heritage operates. New discourses and ontologies are emerging to reconfigure heritage for the circumstances of the present and the uncertainties of the future. Taking the current role of heritage in Europe as its starting point, Critical Heritage Studies and the Futures of Europe presents a number of case studies that explore key themes in this transformation. Contributors draw on a range of disciplinary perspectives to consider, variously, the role of heritage and museums in the migration and climate ‘emergencies’; approaches to urban heritage conservation and practices of curating cities; digital and digitised heritage; the use of heritage as a therapeutic resource; and critical approaches to heritage and its management. Taken together, the chapters explore the multiple ontologies through which cultural and natural heritage have actively intervened in redrawing the futures of Europe and the world

    Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

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    This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work

    Super-Resolution Textured Digital Surface Map (DSM) Formation by Selecting the Texture From Multiple Perspective Texel Images Taken by a Low-Cost Small Unmanned Aerial Vehicle (UAV)

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    Textured Digital Surface Model (TDSM) is a three-dimensional terrain map with texture overlaid on it. Utah State University has developed a texel camera which can capture a 3D image called a texel image. A TDSM can be constructed by combining these multiple texel images, which is much cheaper than the traditional method. The overall goal is to create a TDSM for a larger area that is cheaper and equally accurate as the TDSM created using a high-cost system. The images obtained from such an inexpensive camera have a lot of errors. To create scientifically accurate TDSM, the error presented in the image must be corrected. An automatic process to create TDSM is presented that can handle a large number of input texel images. The advantage of using such a large set of input images is that they can cover a large area on the ground, making the algorithm suitable for large-scale applications. This is done by processing images and correcting them in a windowing manner. Furthermore, the appearance of the final 3D terrain map is improved by selecting the texture from many candidate images. This ensures that the best texture is selected. The selection criteria are discussed. Lastly, a method to increase the resolution of the final image is discussed. The methods described in this dissertation improve the current technique of creating TDSM, and the results are shown and analyzed

    Change and continuity at the Roman coastal fort at Oudenburg from the late 2nd until the early 5th century AD. Volume II: The material culture of the south-west corner site

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    In de latere Romeinse periode vormde de Noordzee- en Kanaalregio het decor voor aanvallen van over zee, politieke crisissen, hervormingen van het leger, Germaanse invallen en veranderende verdedigingsstrategieën van het Romeinse Rijk. Woelige tijden dus, waarover weinig literaire bronnen bestaan. De kustforten zijn van onschatbare waarde om de gebeurtenissen van deze periode te begrijpen, maar onderzoek daarvan was schaars. De opgravingen van het Oudenburgse castellum zorgden dan ook voor een belangrijke ommezwaai in onze kennis over die gebeurtenissen, want ze bieden een unieke inkijk in het enige gekende Romeinse stenen fort in Vlaanderen. De opgravingen legden een opmerkelijk goed bewaarde chronologie bloot van vijf opeenvolgende forten, van de late 2de tot de vroege 5de eeuw na Chr. Het is de eerste keer in een kustfort dat de evolutie van midden- tot laat-Romeins fort zo precies kan gedateerd en geïllustreerd worden. Politieke, economische en sociale ontwikkelingen zijn duidelijk te herkennen, dankzij de uitgebreide studie van de stratigrafie en de enorme hoeveelheid aan vondsten. De materiaalstudies, uitgevoerd door specialisten die gebruik maken van verschillende analytische methodes, vormen referenties voor regionaal militair onderzoek en studies van de latere Romeinse periode in de noordwestelijke provincies. De studie van het kustfort van Oudenburg helpt het onderzoek naar verandering en continuïteit en identiteit met betrekking tot het dagelijks leven van de soldaten en de interactie met de ruimere regio. Het is duidelijk dat dit castellum nauw verbonden was met de Britse forten, de Germaanse invloed er geleidelijk aan toenam en het leven in het fort evolueerde naar dat van een gemeenschap van militaire families

    Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions

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    Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, regular convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model's ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach.Comment: 18 pages, 5 figure

    Images on the Move

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    In contemporary society, digital images have become increasingly mobile. They are networked, shared on social media, and circulated across small and portable screens. Accordingly, the discourses of spreadability and circulation have come to supersede the focus on production, indexicality, and manipulability, which had dominated early conceptions of digital photography and film. However, the mobility of images is neither technologically nor conceptually limited to the realm of the digital. The edited volume re-examines the historical, aesthetical, and theoretical relevance of image mobility. The contributors provide a materialist account of images on the move - ranging from wired photography to postcards to streaming media
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