Bibliografías temáticas / Image

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Literatura académica sobre el tema "Image"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Image"

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Silva, Adriano Alves da. "O TEMPO IMAGÉTICO: a condensação do tempo na imagem fotográfica e a eternidade cíclica na imagem fílmica." Revista Observatório 4, no. 1 (2018): 879. http://dx.doi.org/10.20873/uft.2447-4266.2018v4n1p879.

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Os profissionais da comunicação buscam criar e midiatizar imagens dotadas de sentido. O conhecimento de saberes que permitem entender como são percebidas as imagens se colocam como uma importante ferramenta, tanto para a produção, quanto para a interpretação destas. Este estudo tem como premissa discutir, problematizar e realizar ensaio artístico autoral sobre a ideia de tempo-imagem, tanto na imagem estática, quanto fílmica, a luz dos conceitos apresentados por Jacques Aumont no livro “A Imagem”.
 
 PALAVRAS-CHAVE: Narrativas visuais; imagem-tempo; fotografia; cinema.
 
 &
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Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab env
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Vijayalakshmi, A., and V. Girish. "Affordable image analysis using NIH Image/ImageJ." Indian Journal of Cancer 41, no. 1 (2004): 47. http://dx.doi.org/10.4103/0019-509x.12345.

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Arti Verma. "Digital Image Processing of SEM image of Polymer Nanocomposite Thin Film Using Java Based Program Image J." Power System Technology 43, no. 2 (2019): 44–46. https://doi.org/10.52783/pst.1135.

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Various images obtain from TEM; SEM can be analyzed with the help of computer image analysis software. Image processing is used to describe the size, shape, surface topography of micro or nano structure materials. In the present paper the characterization analysis is being reported qualitatively by using digital image processing of SEM/TEM image of some nanomaterials. This novel technique is an effective experimental tool for the detailed structural characterization. For image processing Java based software ImageJ is used in the present study.
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Budige, Usharani, and Srikar Goud Konda. "Text To Image Generation By Using Stable Diffusion Model With Variational Autoencoder Decoder." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 514–19. http://dx.doi.org/10.22214/ijraset.2023.56024.

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Abstract: Imagen is a text-to-image diffusion model with a profound comprehension of language and an unmatched level of photorealism. Imagen relies on the potency of diffusion models for creating high-fidelity images and draws on the strength of massive transformer language models for comprehending text. Our most important finding is that general large language models, like T5, pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: expanding the language model in Imagen improves sample fidelity and image to text alignment much more than expanding the
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Janagam, Raju, and K. Yakub Reddy. "Automatic Image Captions for Lightly Labelled Images." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 452–54. http://dx.doi.org/10.31142/ijtsrd10786.

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Liang, Bo, Xi Chen, Lan Yu, et al. "High-precision Multichannel Solar Image Registration Using Image Intensity." Astrophysical Journal Supplement Series 261, no. 2 (2022): 10. http://dx.doi.org/10.3847/1538-4365/ac7232.

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Abstract Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular
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Niitsu, M., H. Hirohata, H. Yoshioka, I. Anno, N. G. Campeau, and Y. Itai. "Magnetization Transfer Contrast on Gradient Echo MR Imaging of the Temporomandibular Joint." Acta Radiologica 36, no. 3 (1995): 295–99. http://dx.doi.org/10.1177/028418519503600317.

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Thirty-nine temporomandibular joints (TMJ) from 20 patients with suspected internal derangements were imaged by a 1.5 T MR imager. The on-resonance binomial magnetization transfer contrast (MTC) pulse was applied to gradient echo images with a dual receiver coil (9 s/section). With the use of an opening device, a series of sequential images were obtained at increments of mouth opening and closing. The tissue signal intensities with (Ms) and without (Mo) MTC were measured and subjective image analysis was performed. Compared with the standard images, MTC technique provided selective signal supp
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Strauss, Lourens Jochemus, and William ID Rae. "Image quality dependence on image processing software in computed radiography." South African Journal of Radiology 16, no. 2 (2012): 44–48. http://dx.doi.org/10.4102/sajr.v16i2.305.

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Background. Image post-processing gives computed radiography (CR) a considerable advantage over film-screen systems. After digitisation of information from CR plates, data are routinely processed using manufacturer-specific software. Agfa CR readers use MUSICA software, and an upgrade with significantly different image appearance was recently released: MUSICA2.
 Aim. This study quantitatively compares the image quality of images acquired without post-processing (flatfield) with images processed using these two software packages.
 Methods. Four aspects of image quality were evaluated.
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Shete, S. G., and Nagnath G. Ghadge. "Image Processing in MATLAB 9.3." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 925–29. https://doi.org/10.31142/ijtsrd9545.

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In this paper we introduce how to handle different kinds of image formats in MATLAB 9.3 by using Matlab Workspace and its Various Commands. Also we illustrated example of processing the images. Shete S. G. | Ghadge Nagnath G. "Image Processing in MATLAB 9.3" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: https://www.ijtsrd.com/papers/ijtsrd9545.pdf
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Tesis sobre el tema "Image"

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Suri, Sahil. "Automatic image to image registration for multimodal remote sensing images." kostenfrei, 2010. https://mediatum2.ub.tum.de/node?id=967187.

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Mennborg, Alexander. "AI-Driven Image Manipulation : Image Outpainting Applied on Fashion Images." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85148.

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The e-commerce industry frequently has to deal with displaying product images in a website where the images are provided by the selling partners. The images in question can have drastically different aspect ratios and resolutions which makes it harder to present them while maintaining a coherent user experience. Manipulating images by cropping can sometimes result in parts of the foreground (i.e. product or person within the image) to be cut off. Image outpainting is a technique that allows images to be extended past its boundaries and can be used to alter the aspect ratio of images. Together
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Dalkvist, Mikael. "Image Completion Using Local Images." Thesis, Linköpings universitet, Informationskodning, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70940.

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Image completion is a process of removing an area from a photograph and replacing it with suitable data. Earlier methods either search for this relevant data within the image itself, or extends the search to some form of additional data, usually some form of database. Methods that search for suitable data within the image itself has problems when no suitable data can be found in the image. Methods that extend their search has in earlier work either used some form of database with labeled images or a massive database with photos from the Internet. For the labels in a database to be useful they
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Schilling, Lennart. "Generating synthetic brain MR images using a hybrid combination of Noise-to-Image and Image-to-Image GANs." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166034.

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Generative Adversarial Networks (GANs) have attracted much attention because of their ability to learn high-dimensional, realistic data distributions. In the field of medical imaging, they can be used to augment the often small image sets available. In this way, for example, the training of image classification or segmentation models can be improved to support clinical decision making. GANs can be distinguished according to their input. While Noise-to-Image GANs synthesize new images from a random noise vector, Image-To-Image GANs translate a given image into another domain. In this study, it
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Murphy, Brian P. "Image processing techniques for acoustic images." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26585.

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Approved for public release; distribution is unlimited<br>The primary goal of this research is to test the effectiveness of various image processing techniques applied to acoustic images generated in MATLAB. The simulated acoustic images have the same characteristics as those generated by a computer model of a high resolution imaging sonar. Edge Detection and Segmentation are the two image processing techniques discussed in this study. The two methods tested are a modified version of the Kalman filtering and median filtering
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Zeng, Ziming. "Medical image segmentation on multimodality images." Thesis, Aberystwyth University, 2013. http://hdl.handle.net/2160/17cd13c2-067c-451b-8217-70947f89164e.

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Segmentation is a hot issue in the domain of medical image analysis. It has a wide range of applications on medical research. A great many medical image segmentation algorithms have been proposed, and many good segmentation results were obtained. However, due to the noise, density inhomogenity, partial volume effects, and density overlap between normal and abnormal tissues in medical images, the segmentation accuracy and robustness of some state-of-the-art methods still have room for improvement. This thesis aims to deal with the above segmentation problems and improve the segmentation accurac
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Khan, Preoyati. "Cluster Based Image Processing for ImageJ." Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1492164847520322.

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Tummala, Sai Virali, and Veerendra Marni. "Comparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15360.

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Asplund, Raquel. "Evaluation of a cloud-based image analysis and image display system for medical images." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-105984.

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Karlsson, Simon, and Per Welander. "Generative Adversarial Networks for Image-to-Image Translation on Street View and MR Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148475.

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Generative Adversarial Networks (GANs) is a deep learning method that has been developed for synthesizing data. One application for which it can be used for is image-to-image translations. This could prove to be valuable when training deep neural networks for image classification tasks. Two areas where deep learning methods are used are automotive vision systems and medical imaging. Automotive vision systems are expected to handle a broad range of scenarios which demand training data with a high diversity. The scenarios in the medical field are fewer but the problem is instead that it is diffi
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Libros sobre el tema "Image"

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1967-, Saraiva Alberto, Monterosso Jean-Luc, Oi Futuro (Rio de Janeiro, Brazil), Maison européenne de la photographie (Paris, France), and São Paulo (Brazil : State). Pinacoteca do Estado, eds. Áspera imagem =: Âpre image = Rough image. Aeroplano Editora, 2006.

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(Firm), Atelier central, ed. Atelier Central: Imagem = image. Uzina Books, 2014.

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Robin, Jean-François. Image par image. Climats, 1996.

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Lander, Daniel. Image par image. Cherche midi, 1989.

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1944-, Tschumi Bernard, Bernard Tschumi Architects, and Galerie d'architecture (Paris France), eds. Virtuæl: Expo 2004 image = images. Bernard Tschumi Architects, 2003.

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Lorenz, Alma. Imago Sloveniae: Image of Slovenia. Imago Sloveniae, 1999.

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aut, Tschumi Bernard, ed. Virtuael: Expo 2004 image = images. Bernard Tschumi Architectes, 2002.

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Alma, Lorenz, ed. Imago Sloveniae =: Image of Slovenia. Imago Sloveniae, 1999.

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Jones, Philippe. Image verbale, image visible. Le Taillis pré, 2013.

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McDowell, Josh. His image, my image. T. Nelson, 1993.

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Capítulos de libros sobre el tema "Image"

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Wolff, Robert S., and Larry Yaeger. "Images and Image Processing." In Visualization of Natural Phenomena. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0646-7_1.

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Corke, Peter. "Images and Image Processing." In Springer Tracts in Advanced Robotics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54413-7_12.

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Corke, Peter, Witold Jachimczyk, and Remo Pillat. "Images and Image Processing." In Springer Tracts in Advanced Robotics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-07262-8_11.

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Corke, Peter. "Images and Image Processing." In Springer Tracts in Advanced Robotics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-06469-2_11.

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Elad, Michael. "Image Compression – Facial Images." In Sparse and Redundant Representations. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7011-4_13.

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Corke, Peter. "Images and Image Processing." In Robotic Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79175-9_4.

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Koeva, Svetla. "Multilingual Image Corpus." In European Language Grid. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_22.

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AbstractThe ELG pilot project Multilingual Image Corpus (MIC 21) provides a large image dataset with annotated objects and multilingual descriptions in 25 languages. Our main contributions are: the provision of a large collection of highquality, copyright-free images; the formulation of an ontology of visual objects based on WordNet noun hierarchies; precise manual correction of automatic image segmentation and annotation of object classes; and association of objects and images with extended multilingual descriptions. The dataset is designed for image classification, object detection and seman
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Cree, Michael J., and Herbert F. Jelinek. "Image Analysis of Retinal Images." In Medical Image Processing. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_11.

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Donchyts, Gennadii. "Exploring Image Collections." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_13.

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AbstractThis chapter teaches how to explore image collections, including their spatiotemporal extent, resolution, and values stored in images and image properties. You will learn how to map and inspect image collections using maps, charts, and interactive tools and how to compute different statistics of values stored in image collections using reducers.
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Bader, Nadia, Sina Hartmann, Notburga Karl, and Raphael Spielmann. "»Kein Bock mehr auf Zoom!« oder: Vom Spiel mit den Regeln als Bildungschance." In Image. transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839465493-006.

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Actas de conferencias sobre el tema "Image"

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Blochet, Baptiste, and Marc Guillon. "Single-shot phase and polarimetric microscopy." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/3d.2024.jf2a.2.

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Cottis, Robert A., and Stuart B. Lyon. "Modeling of Image Formation and Image Enhancement for Scanning Electrochemical Methods." In CORROSION 2005. NACE International, 2005. https://doi.org/10.5006/c2005-05056.

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Abstract This paper examines the way in which the image is formed during a SRET measurement, and considers some of the potential sources of error in the measurement. Methods for the enhancement of images to increase the resolution are discussed, and the impact of the various error types on image enhancement processes is considered. It is shown that ideal images can be significantly improved by the conventional image method of Fourier Transform Deconvolution, but that typical errors present in the measurement are liable to seriously limit the capacity for enhancement.
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Işıl, Çağatay, Tianyi Gan, F. Onuralp Ardic, et al. "Image denoising using diffractive optical processors." In CLEO: Fundamental Science. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_fs.2024.ftu3g.2.

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We report an image denoising analog processor composed of passive diffractive layers engineered through deep learning to filter out various types of noise from input images, instantly projecting denoised images at the output field-of-view.
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Hoffmann, Rolf. "A High Quality Image Stitching Process for Industrial Image Processing and Quality Assurance." In OCM 2021 - 5th International Conference on Optical Characterization of Materials. KIT Scientific Publishin, 2021. http://dx.doi.org/10.58895/ksp/1000128686-18.

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The size of the recording area of a camera is limited. The resolution of a camera image is also limited. To capture larger areas, a wide angle lens can be used, for example. However, the image resolution per unit area decreases. The decreased image resolution can be compensated by image sensors with a higher number of pixels. However, the use of a high pixel number of image sensors is limited to the current state of the art and availability of real image sensors. Furthermore the use of a wide angle lens has the disadvantage of a stronger distortion of the image scene. Also the viewing directio
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Cho, Soojin, and Byunghyun Kim. "Image-driven Bridge Inspection Framework using Deep Learning and Image Registration." In IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures. International Association for Bridge and Structural Engineering (IABSE), 2020. http://dx.doi.org/10.2749/seoul.2020.269.

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&lt;p&gt;This paper proposes an image-driven bridge inspection framework using automated damage detection using deep learning technique and image registration. A state-of-the-art deep learning model, Cascade Mask R-CNN (Mask and Region-based Convolutional Neural Networks) is trained for detection of cracks, which is a representative damage type of bridges, from the images taken from a bridge. The model is trained with more than a thousand training images containing cracks as well as crack-like objects (hard negative samples). The images taken from a test bridge are input to a deep learning mod
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Doan, Tien Tai, Guillaume Ghyselinck, and Blaise Hanczar. "Informative Multimodal Unsupervised Image-to-Image Translation." In 9th International Conference of Security, Privacy and Trust Management (SPTM 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110503.

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We propose a new method of multimodal image translation, called InfoMUNIT, which is an extension of the state-of-the-art method MUNIT. Our method allows controlling the style of the generated images and improves their quality and diversity. It learns to maximize the mutual information between a subset of style code and the distribution of the output images. Experiments show that our model cannot only translate one image from the source domain to multiple images in the target domain but also explore and manipulate features of the outputs without annotation. Furthermore, it achieves a superior d
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Yang, S., Y. Wang, and C. Shrivastava. "Sedimentary Analysis Via Automatic Image Segmentation and Clustering with the LWD OBM Resistivity Image: A Case Study from Gulf of Mexico." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214908-ms.

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Abstract Microfacies analysis is the first step for depositional environment interpretation and sand body prediction. Textural details from borehole images are building blocks for facies analysis, representing different paleo sediimentation conditions. Associated workflows have been applied on high resolution borehole images by geologists and log analysts manually. Automation via machine learning solutions provides an opportunity to improve the working efficiency and accuracy. Such an approach has given satisfactory results with post-drilling wireline images. In this paper, the improved workfl
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Zhao, Zixiang, Shuang Xu, Chunxia Zhang, Junmin Liu, Jiangshe Zhang, and Pengfei Li. "DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/135.

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Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel auto-encoder (AE) based fusion network. The core idea is that the encoder decomposes an image into background and detail feature maps with low- and high-frequency information, respectively, and that the decoder recovers the original image. To this end, the loss function makes the background/detail feature maps of source images similar/dissimilar. In the test phase, background and detail feature maps are respectively
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VARGHESE, SUBIN, REBECCA WANG, and VEDHUS HOSKERE. "IMAGE TO IMAGE TRANSLATION OF STRUCTURAL DAMAGE USING GENERATIVE ADVERSARIAL NETWORKS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36307.

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In the aftermath of earthquakes, structures can become unsafe and hazardous for humans to safely reside. Automated methods that detect structural damage can be invaluable for rapid inspections and faster recovery times. Deep neural networks (DNNs) have proven to be an effective means to classify damaged areas in images of structures but have limited generalizability due to the lack of large and diverse annotated datasets (e.g., variations in building properties like size, shape, color). Given a dataset of paired images of damaged and undamaged structures supervised deep learning methods could
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Varnasuthan, S., W. T. M. Fernando, D. S. Dahanayaka, A. B. N. Dassanayake, M. A. D. M. G. Wickrama, and I. M. T. N. Illankoon. "Image analysis approach to determine the porosity of rocks." In International Symposium on Earth Resources Management & Environment - ISERME 2023. Department of Earth Resources Engineering, 2023. http://dx.doi.org/10.31705/iserme.2023.7.

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Accurate characterisation of rock porosity is essential for assessing its strength and durability. This study explores both conventional and image analysis methods for determining rock porosity of two types of rocks, Bibai sandstone, a hard clastic rock and limestone, a soft rock. Conventional methods for determining rock porosity involve physical measurements and laboratory analysis, while image analysis methods utilize advanced imaging techniques such as CT scans or SEM to assess porosity based on visual information extracted from rock images. While various image analysis approaches exist to
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Informes sobre el tema "Image"

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Lingner, Stefan, Karl Heger, and Claas Faber. MAMS Image Broker. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_3_2022.

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The GEOMAR Helmhotz Centre for Ocean Research Kiel is operating a Media Asset Management System (MAMS) which manages large amounts image- and video data. Similar systems are operated by the Helmholtz Centre HEREON and by the Alfred Wegener Institute (AWI). Although the MAMS system provides access to data and metadata using an API, it is not possible to directly request image data without prior knowledge of the internal MAMS data structure. The image broker is a web service which brokers between a client (e.g. web-browser) and the MAMS. It allows users to request images by metadata values (e.g.
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Wendelberger, James G. Image Analysis of Stringer Area: Three Images. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1467243.

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Thayer, Colette, and Laura Skufca. Media Image Landscape: Age Representation in Online Images. AARP Research, 2019. http://dx.doi.org/10.26419/res.00339.001.

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Doerry, Armin, and Douglas Bickel. Synthetic Aperture Radar Image Geolocation Using Fiducial Images. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1890785.

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Rosenfeld, Azriel. Parallel Image Processing and Image Understanding. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada183223.

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Rosenfeld, A. Parallel Image Processing and Image Understanding. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada159029.

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Wendelberger, James G., Kimberly Ann Kaufeld, Elizabeth J. Kelly, Juan Duque, and John M. Berg. Automated Image Analysis for Screening LCM Images – Examples from 09DE2 Images. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1422981.

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Boopalan, Santhana. Aerial Wildlife Image Repository. Mississippi State University, 2023. http://dx.doi.org/10.54718/wvgf3020.

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The availability of an ever-improving repository of datasets allows machine learning algorithms to have a robust training set of images, which in turn allows for accurate detection and classification of wildlife. This repository (AWIR---Aerial Wildlife Image Repository) would be a step in creating a collaborative rich dataset both in terms of taxa of animals and in terms of the sensors used to observe (visible, infrared, Lidar etc.). Initially, priority would be given to wildlife species hazardous to aircrafts, and common wildlife damage-associated species. AWIR dataset is accompanied by a cla
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Caldeira, J., W. L. K. Wu, and B. Nord. DeepCMB: Saliency for Image-to-Image Regression. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1637621.

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Delgrande Jordan, Marina, Valentine Schmidhauser, and Nora Balsiger. Statut pondéral, image du corps (image 3). Addiction Suisse, 2023. http://dx.doi.org/10.58758/hbsc.ch.image.03.

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