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Journal articles on the topic 'Personalized images'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Zhao, Haiming, Jufeng Wang, Xiaoyu Ren, Jingyuan Li, Yong-Liang Yang, and Xiaogang Jin. "Personalized food printing for portrait images." Computers & Graphics 70 (February 2018): 188–97. http://dx.doi.org/10.1016/j.cag.2017.07.012.

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Nakashima, Yuto. "Personalized Image Generation Through Swiping." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 374–75. http://dx.doi.org/10.1609/aaaiss.v3i1.31238.

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Generating preferred images from GANs is a challenging task due to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images from users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of StyleGAN, creating meaningful subspaces. Additionally, we use a multi-armed bandit algorithm to decide which dimensions to explore, focusing on the user's preferences. Our experiments show that our method is more efficient in genera

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Chen, Jun, Chaokun Wang, Jianmin Wang, Xiang Ying, and Xuecheng Wang. "Learning the Personalized Intransitive Preferences of Images." IEEE Transactions on Image Processing 26, no. 9 (2017): 4139–53. http://dx.doi.org/10.1109/tip.2017.2709941.

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Wang, Jia. "Innovative Practice of Digital Printing Technology and Computer Image Fusion in Personalized Packaging." Journal of Combinatorial Mathematics and Combinatorial Computing 127a (April 15, 2025): 1285–302. https://doi.org/10.61091/jcmcc127a-075.

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In the context of continuous innovation in science and technology, consumer demand is becoming increasingly diversified, especially in product packaging design, personalization and uniqueness have become a new pursuit. The article proposes a computer image fusion DPformer-GAN model based on Transformer model and GAN, which is used to realize personalized packaging image fusion and generation. The digital image is then converted into a personalized packaging object through digital printing technology, which then realizes the innovative practice of personalized packaging.The DPformer-GAN model r

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Zhu, Hancheng, Yong Zhou, Zhiwen Shao, Wenliang Du, Guangcheng Wang, and Qiaoyue Li. "Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning." Mathematics 10, no. 22 (2022): 4181. http://dx.doi.org/10.3390/math10224181.

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Due to the subjective nature of people’s aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignor

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Hammoudi, Karim, and Mahmoud Melkemi. "Personalized Shares in Visual Cryptography." Journal of Imaging 4, no. 11 (2018): 126. http://dx.doi.org/10.3390/jimaging4110126.

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This article deals with visual cryptography. It consists of hiding a message in two key images (also called shares). The decryption of the message is obtained through human vision by superposition of the shares. In existing methods, the surface of key images is not visually pleasant and is not exploited for communicating textual or pictorial information. Presently, we propose a pictogram-based visual cryptography technique, which generates shares textured with customizable and aesthetic rendering. Moreover, robustness characteristics of this technique to the automated decoding of the secret me

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., Prasanna Wadekar. "PERSONALIZED GEO-TAG RECOMMENDATION FOR COMMUNITY CONTRIBUTED IMAGES." International Journal of Research in Engineering and Technology 04, no. 05 (2015): 234–39. http://dx.doi.org/10.15623/ijret.2015.0405045.

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Nitzan, Yotam, Kfir Aberman, Qiurui He, et al. "MyStyle." ACM Transactions on Graphics 41, no. 6 (2022): 1–10. http://dx.doi.org/10.1145/3550454.3555436.

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We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~ 100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space. We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individ

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Lu, Hanzhang, and Ji Qi. "Personalized Super Resolution with Face Prior." Applied and Computational Engineering 95, no. 1 (2024): 141–50. http://dx.doi.org/10.54254/2755-2721/95/2024ch0052.

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Face Super-Resolution (FSR) is a critical technology in computer vision that aims to reconstruct high-resolution facial images from low-resolution inputs. Despite recent advancements, current FSR methods struggle to accurately reconstruct personalized and detailed features. This paper proposes a novel FSR approach that addresses these challenges through a personalized feature extraction and fusion framework. Our method integrates a U-Net based downsampling mechanism to extract individual- specific features from high-resolution reference images, which are then fused with a pre-trained Generativ

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Cherepkova, Olga, Seyed Ali Amirshahi, and Marius Pedersen. "Individual Contrast Preferences in Natural Images." Journal of Imaging 10, no. 1 (2024): 25. http://dx.doi.org/10.3390/jimaging10010025.

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This paper is an investigation in the field of personalized image quality assessment with the focus of studying individual contrast preferences for natural images. To achieve this objective, we conducted an in-lab experiment with 22 observers who assessed 499 natural images and collected their contrast level preferences. We used a three-alternative forced choice comparison approach coupled with a modified adaptive staircase algorithm to dynamically adjust the contrast for each new triplet. Through cluster analysis, we clustered observers into three groups based on their preferred contrast rang

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Moroto, Yuya, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama. "Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention." Sensors 20, no. 8 (2020): 2170. http://dx.doi.org/10.3390/s20082170.

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A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand,

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Huang, Qihan, Siming Fu, Jinlong Liu, Hao Jiang, Yipeng Yu, and Jie Song. "Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 3707–14. https://doi.org/10.1609/aaai.v39i4.32386.

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Personalized text-to-image generation methods can generate customized images based on the reference images, which have garnered wide research interest. Recent methods propose a finetuning-free approach with a decoupled cross-attention mechanism to generate personalized images requiring no test-time finetuning. However, when multiple reference images are provided, the current decoupled cross-attention mechanism encounters the object confusion problem and fails to map each reference image to its corresponding object, thereby seriously limiting its scope of application. To address the object conf

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JIN, SHOU-NING, BING-FEI GU, BEI-BEI ZHANG, YUAN-PING XIA, and HUA-ZHOU HE. "Pants design and pattern generation based on body images." Industria Textila 74, no. 01 (2023): 74–80. http://dx.doi.org/10.35530/it.074.01.20223.

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The personalized pattern generation method based on 2D body-measuring technology has considerable application potential in clothing e-commerce, remote clothing customization, clothing production, and other aspects. By inputting the front and side body images, this study proposed a new method of generating personalized patterns automatically. The silhouettes could be extracted from the body images to estimate body sizes and design style. The basic rules between the patterns and the body sizes were analysed, and the rules of the general pattern generation were established through a knowledge-bas

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Yazykeev, A. N. "Symbols of the past and images of the future in strategies of tourist development of territories." Abyss (Studies in Philosophy, Political science and Social anthropology), no. 4(30) (2024): 78–88. https://doi.org/10.33979/2587-7534-2024-4-78-88.

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The article considers the role of personalized images in the branding of territories. On the example of sociological studies conducted in different cities and regions, the significance of historical personalities and architectural objects in the symbolic field of cities is demonstrated, as well as the possibilities of their use in the strategies of tourist development of territories. The work demonstrates how the images of the past, transforming can form the basis for the vision of the future. Difficulties may arise when using personalized images as significant symbols are considered separatel

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Müller, H., A. Foncubierta-Rodriguez, and A. Depeursinge. "Sensors, Medical Images and Signal Processing: Ubiquitous Personalized Health Monitoring." Yearbook of Medical Informatics 21, no. 01 (2012): 100–103. http://dx.doi.org/10.1055/s-0038-1639438.

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SummaryTo summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2011.Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2012 through a manual initial selection and a peer review process to find the best paper in this domain published in 2011.Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. An increased number of systems with embedded signal processing where

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Cheng, Jiaxiang, Pan Xie, Xin Xia, et al. "ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2438–46. https://doi.org/10.1609/aaai.v39i3.32245.

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Recent advancement in text-to-image models and corresponding personalized technologies enables individuals to generate high-quality and imaginative images. However, they often suffer from limitations when generating images with resolutions outside of their trained domain. To overcome this limitation, we present the resolution adapter \textbf{(ResAdapter)}, a domain-consistent adapter designed for diffusion models to generate images with unrestricted resolutions and aspect ratios. Unlike other multi-resolution generation methods that process images of static resolution with complex post-process

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Jagadish, P., B. Nethravathi, Sandhyarani, C. Chirag, Bhagyashre, and C. S. Gokhul. "FaceForge: Personalized Facial Enhancement with Class-guided Noise Reduction." Journal of Research and Review: Machine Learning 1, no. 2 (2025): 20–28. https://doi.org/10.5281/zenodo.14874554.

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<em>Personalizing digital faces is a fundamental task in various fields such as virtual try-on, digital avatars, and face image editing. However, achieving realistic and editable face personalization remains a challenge due to the inherent noise and variability of face images. This study presents FaceForge, a new approach to editable face personalization using class-driven noise suppression regulation. FaceForge uses a diffusion-based generative model trained with class-specific information to enable high-quality facial manipulation while preserving identity and expression details. The propose

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Tian, Rui, Xiao Li, Wenxi Li, Guojihong Li, Keer Chen, and Hanwei Dai. "Using Pix2Pix conditional generative adversarial networks to generate personalized poster content: Style transfer and detail enhancement." Journal of Computational Methods in Sciences and Engineering 25, no. 2 (2024): 1938–50. https://doi.org/10.1177/14727978241307472.

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In the process of generating personalized poster content, there are often problems such as inconsistency, blurring, and distortion between the generated image and the original image content, exaggerated image styles that do not meet the personalized needs of users. In response to these issues, this article aims to use Pix2Pix Conditional Generative Adversarial Network (Pix2Pix-CGAN) to solve problems such as image blur, distortion, and content inconsistency in personalized poster generation, in order to improve the quality of generated images and meet users’ personalized needs. This article co

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Roy, Krushabh. "Fashion.AI- Personalized Outfit Recommendation." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5812–17. https://doi.org/10.22214/ijraset.2025.71114.

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In the last few years, AI has slowly started changing how the fashion industry works, especially when it comes to giving people outfit ideas that feel more personal. For this project, we made a fashion assistant that helps suggest clothes by breaking the job into smaller parts. It uses things like image search, color picking, and some smart tools to figure things out. When someone uploads a picture, the system checks what type of clothing is in it using a deep learning method called Faster RCNN (basically, it helps the system "see" the clothes). Then, it tries to figure out the main color of t

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Wan, Yitian, Luwei Xiao, Xingjiao Wu, Jing Yang, and Liang He. "Imaginique Expressions: Tailoring Personalized Short-Text-to-Image Generation Through Aesthetic Assessment and Human Insights." Symmetry 16, no. 12 (2024): 1608. https://doi.org/10.3390/sym16121608.

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The text-to-image task, a critical branch of computer vision and image processing, has witnessed remarkable advancements fueled by the abundance of realistic data and rapid AI innovation. However, existing research often overlooks scenarios involving sparse textual input and fails to incorporate human personalized preferences into the generative process. To address these gaps, we propose a novel AI methodology: personalized short-text-to-image generation through aesthetic assessment and human insights. Our approach introduces a symmetry between personalized aesthetic preferences and the genera

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Cao, Yuanhao. "Rapid Restoration of Medical Images Relying on Bayesian Personalized Sorting Algorithm." Mobile Information Systems 2022 (June 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/9157150.

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Aiming at the real-time problem of medical image depth information restoration, which leads to the incomplete image data information collected in the process of medical image data acquisition, this study proposes a fast medical image restoration method based on the Bayesian personalized sorting algorithm (BPSA), which is used to segment the low- and high-frequency sub-band images in the initial image, The optimal low-frequency sub-band coefficient is solved by combining the nonnegative matrix decomposition method, and the high-frequency direction sub-band coefficient is solved according to the

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Wang, Yangang, Ruting Rao, and Changqing Zou. "Personalized Hand Modeling from Multiple Postures with Multi‐View Color Images." Computer Graphics Forum 39, no. 7 (2020): 339–50. http://dx.doi.org/10.1111/cgf.14149.

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Booth, Brian G., Eva Hoefnagels, Toon Huysmans, Jan Sijbers, and Noel L. W. Keijsers. "PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping." PLoS One 15, no. 2 (2020): e0229685. https://doi.org/10.1371/journal.pone.0229685.

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Quantitative analyses of plantar pressure images typically occur at the group level and under the assumption that individuals within each group display homogeneous pressure patterns. When this assumption does not hold, a personalized analysis technique is required. Yet, existing personalized plantar pressure analysis techniques work at the image level, leading to results that can be unintuitive and difficult to interpret. To address these limitations, we introduce PAPPI: the Personalized Analysis of Plantar Pressure Images. PAPPI is built around the statistical modelling of the relationship be

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Blanco-Mallo, Eva, Beatriz Remeseiro, Verónica Bolón-Canedo, and Amparo Alonso-Betanzos. "On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems." Proceedings 54, no. 1 (2020): 11. http://dx.doi.org/10.3390/proceedings2020054011.

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Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users from all over the world seek and share their opinions based on all types of products. Specifically, millions of images tagged with users’ tastes are available on the web. Therefore, the application of deep learning techniques to solve these types of tasks has become a key issue, and there is a growing interest in the use of images to solve them, particularly through feature extraction. T

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Huang, Xiaoyang, Yanjun Wang, Yang Liu, et al. "AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 944–52. http://dx.doi.org/10.1609/aaai.v37i1.25174.

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Spatial audio, which focuses on immersive 3D sound rendering, is widely applied in the acoustic industry. One of the key problems of current spatial audio rendering methods is the lack of personalization based on different anatomies of individuals, which is essential to produce accurate sound source positions. In this work, we address this problem from an interdisciplinary perspective. The rendering of spatial audio is strongly correlated with the 3D shape of human bodies, particularly ears. To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with sing

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ARYA, ALI, and BABAK HAMIDZADEH. "PERSONALIZED FACE ANIMATION IN SHOWFACE SYSTEM." International Journal of Image and Graphics 03, no. 02 (2003): 345–63. http://dx.doi.org/10.1142/s0219467803001044.

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The problem of Personalized Face Animation is addressed in this paper by proposing the ShowFace streaming structure. This structure is based on most widely accepted industry standards in multimedia presentation like MPEG-4 and SMIL, extending them by defining image transformations required for certain facial movements, and also by providing a higher level Face Modeling Language (FML) for modeling and control purposes. It defines a comprehensive framework for face animation consisting of components for parsing the input script, generating and splitting the audio and video "behaviors", creating

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Pant, Sakshi. "Deep Learning for Personalized Healthcare Recommendations." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 470–75. http://dx.doi.org/10.22214/ijraset.2024.65093.

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Personalized healthcare refers to an evolving paradigm of providing appropriate medical treatments based on the particularities of the individual patient, where evidence-based management is enhanced with the use of technologies. Deep learning (DL) is placed within the umbrella of efficient systems known as artificial intelligence (AI), it assists in performing data processing with more accuracy, and making suggestions based on the unique health information of the health record e.g. EHRs, images and genetic data among others. This article gives an overview of deep learning techniques in develop

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Vijayarajan, R., P. Gnanasivam, and R. Avudaiammal. "Bio-Key Based AES for Personalized Image Cryptography." Computer Journal 62, no. 11 (2019): 1695–705. http://dx.doi.org/10.1093/comjnl/bxz030.

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Abstract In the era of multimedia technologies and rapidly growing multimedia contents, secured information transfer needs to be ensured through robust cryptography techniques. This paper presents a bio-key-generation scheme in the local environment to transfer multimedia content over vulnerable networks. To encrypt the images, one user has to ensure his/her authentication and further needs to provide a secret key for bio-key generation. Secret key aids to fetch biometric features for key formation and key expansion. Thus, the user receives an expanded key from the template database to perform

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Li, Jinpeng. "Analyze the Impact, Reasons, and Optimization Strategies of the Image of AI Anchors on Consumer Purchase Intention in Live Streaming E-commerce." Advances in Economics, Management and Political Sciences 102, no. 1 (2024): 117–24. http://dx.doi.org/10.54254/2754-1169/102/2024ed0104.

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The importance of live streaming e-commerce in online shopping is increasing with the continuous development of live streaming technology. Artificial intelligence (AI), as a disruptive force, has introduced virtual anchors in the live streaming field, replacing live anchors for product introduction and interaction. The virtual anchor image, with its anthropomorphic appearance, interacts with the audience in real-time and is gradually being applied in various fields such as e-commerce, advertising, and social platforms. This study reviews how the image of AI anchors in live streaming e-commerce

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Rescigno, Martina, Matteo Spezialetti, and Silvia Rossi. "Personalized models for facial emotion recognition through transfer learning." Multimedia Tools and Applications 79, no. 47-48 (2020): 35811–28. http://dx.doi.org/10.1007/s11042-020-09405-4.

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AbstractEmotions represent a key aspect of human life and behavior. In recent years, automatic recognition of emotions has become an important component in the fields of affective computing and human-machine interaction. Among many physiological and kinematic signals that could be used to recognize emotions, acquiring facial expression images is one of the most natural and inexpensive approaches. The creation of a generalized, inter-subject, model for emotion recognition from facial expression is still a challenge, due to anatomical, cultural and environmental differences. On the other hand, u

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Nanmaran, R., S. Srimathi, G. Yamuna, et al. "Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine." Computational and Mathematical Methods in Medicine 2022 (February 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/7137524.

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Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused images will be more informative than individual input images, thus more suitable for classification problems. Artificial intelligence (AI) algorithms play a significant role in improving patient’s treatment in the health care industry and thus improving personalized medicine. This research work analyses the role of image fusion in an improved brain tu

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Kosutic, Tomislav, Miran Mosmondor, Ivan Andrisek, Mario Weber, Maja Matijasevic, and Igor Pandzic. "Personalized Avatars for Mobile Entertainment." Mobile Information Systems 2, no. 2-3 (2006): 95–110. http://dx.doi.org/10.1155/2006/139614.

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With evolution in computer and mobile networking technologies comes the challenge of offering novel and complex multimedia applications and end-user services in heterogeneous environments for both developers and service providers. This paper describes one novel service, called LiveMail that explores the potential of existing face animation technologies for innovative and attractive services intended for the mobile market. This prototype service allows mobile subscribers to communicate using personalized 3D face models created from images taken by their phone cameras. The user can take a snapsh

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Jian, Meng, Jingjing Guo, Xin Fu, Lifang Wu, and Ting Jia. "Cross-Modal Manifold Propagation for Image Recommendation." Applied Sciences 12, no. 6 (2022): 3180. http://dx.doi.org/10.3390/app12063180.

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The growing complex user intention gap and information overload are obstacles for users to access the desired content. User interactions and the involved content indicate rich evidence of users’ interests. It is required to investigate interaction characters over user interest and information distribution, and this alleviates information overload for personalized recommendation. Therefore, this work explores user interests with interactions and visual information from users’ historical records for image recommendation. This paper introduces cross-modal manifold propagation (CMP) for personaliz

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Zhao, Manlin, Zhichao Sheng, and Yong Fang. "Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements." Applied Sciences 12, no. 16 (2022): 8155. http://dx.doi.org/10.3390/app12168155.

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In this paper, we propose a global personalized head-related transfer function (HRTF) method based on anthropometric measurements and ear images. The model consists of two sub-networks. The first is the VGG-Ear Model, which extracts features from the ear images. The second sub-network uses anthropometric measurements, ear features, and frequency information to predict the spherical harmonic (SH) coefficients. Finally, the personalized HRTF is obtained through inverse spherical harmonic transform (SHT) reconstruction. With only one training, the HRTF in all directions can be obtained, which gre

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Hong, Taekeun, Jin-A. Choi, Kiho Lim, and Pankoo Kim. "Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks." Sensors 21, no. 1 (2020): 199. http://dx.doi.org/10.3390/s21010199.

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The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by

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Kim, Mujung, Jisang Yoo, and Soonchul Kwon. "Personalized Text-to-Image Model Enhancement Strategies: SOD Preprocessing and CNN Local Feature Integration." Electronics 12, no. 22 (2023): 4707. http://dx.doi.org/10.3390/electronics12224707.

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Recent advancements in text-to-image models have been substantial, generating new images based on personalized datasets. However, even within a single category, such as furniture, where the structures vary and the patterns are not uniform, the ability of the generated images to preserve the detailed information of the input images remains unsatisfactory. This study introduces a novel method to enhance the quality of the results produced by text-image models. The method utilizes mask preprocessing with an image pyramid-based salient object detection model, incorporates visual information into i

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Karabay, Aknur, Arman Bolatov, Huseyin Atakan Varol, and Mei-Yen Chan. "A Central Asian Food Dataset for Personalized Dietary Interventions." Nutrients 15, no. 7 (2023): 1728. http://dx.doi.org/10.3390/nu15071728.

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Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to c

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Diao, Su, Danyi Huang, and Gaozhe Jiang. "The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging." Frontiers in Science and Engineering 4, no. 8 (2024): 92–100. http://dx.doi.org/10.54691/fbpgw554.

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This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology M

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Jianping Fan, D. A. Keim, Yuli Gao, Hangzai Luo, and Zongmin Li. "JustClick: Personalized Image Recommendation via Exploratory Search From Large-Scale Flickr Images." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 2 (2009): 273–88. http://dx.doi.org/10.1109/tcsvt.2008.2009258.

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Jazizadeh, Farrokh, and Wooyoung Jung. "Personalized thermal comfort inference using RGB video images for distributed HVAC control." Applied Energy 220 (June 2018): 829–41. http://dx.doi.org/10.1016/j.apenergy.2018.02.049.

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Potesil, Vaclav, Timor Kadir, Günther Platsch, and Michael Brady. "Personalized Graphical Models for Anatomical Landmark Localization in Whole-Body Medical Images." International Journal of Computer Vision 111, no. 1 (2014): 29–49. http://dx.doi.org/10.1007/s11263-014-0731-7.

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Sarojadevi, H., Vineet Bhat, Rohan Palekar, and K. Neha. "Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit." Asian Journal of Computer Science and Technology 12, no. 1 (2023): 21–24. http://dx.doi.org/10.51983/ajcst-2023.12.1.3569.

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The methods used to predict how highly a person will evaluate a product or a group of people are known as recommendation systems. Books, movies, restaurants, and other products can be among the things commonly recommended. Objects where people differ in their preferences matter a lot. For predicting the preferences 2 methods are used, one a content based approach that considers the characteristic of an item, and the other is a collaborative method that evaluates choices by taking into account previous user behaviour. In this paper, a system for recommending fashion items is proposed, one that

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Wu, Zhihao, Yushi Cheng, Tianyang Sun, Xiaoyu Ji, and Wenyuan Xu. "MYOPIA: Protecting Face Privacy from Malicious Personalized Text-to-Image Synthesis via Unlearnable Examples." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 905–13. https://doi.org/10.1609/aaai.v39i1.32075.

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Personalized text-to-image synthesis models, such as DreamBooth, have demonstrated significant potential in creating lifelike images tailored to a specific individual by fine-tuning from a limited set of face images and simple prompts. However, if misused, these model could pose a serious risk of privacy infringement by generating harmful images containing violent or pornographic content. To tackle this issue, this paper introduces MYOPIA, a method that renders facial images unlearnable by incorporating error-minimizing perturbations. These meticulously designed perturbations enables the model

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Sanjana, R., V. Sri Harshitha, K. Navyasree, G. Srija, M. Bharathi, and T. Aditya Sai Srinivas. "Glam Guidance: Empowering Your Fashion Choices with Image Insights." Journal of Image Processing and Image Restoration 2, no. 1 (2025): 1–5. https://doi.org/10.48001/joipir.2025.211-5.

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The paper introduces a Fashion Recommendation System that utilizes computer vision and machine learning to analyze fashion items' visual attributes such as color, texture, and style, offering similar or complementary product recommendations to users. It guides readers through the process of building such a system using Python. Leveraging a pre-trained Convolutional Neural Network (CNN), the system extracts feature from fashion images, enabling calculation of similarities between images for recommendation purposes. Employing cosine similarity, it provides personalized recommendations based on u

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Geboers, Marloes Annette, and Chad Thomas Van De Wiele. "Regimes of visibility and the affective affordances of Twitter." International Journal of Cultural Studies 23, no. 5 (2020): 745–65. http://dx.doi.org/10.1177/1367877920923676.

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Online social networks produce a visuality that reflects the attention economy governing this space. What is seen becomes elevated into prominence by networked publics that ‘perform’ affective expressions within platform affordances. We mapped Twitter images of refugees in two language spaces – English and Arabic. Using automated analysis and qualitative visual analysis, we found similar images circulating both spaces. However, photographs generating higher retweet counts were distinct. This highlights the impact of affective affordances of Twitter – in this case retweeting – on regimes of vis

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Zhou, Jin, Qing Zhang, Jian-Hao Fan, Wei Sun, and Wei-Shi Zheng. "Joint regression and learning from pairwise rankings for personalized image aesthetic assessment." Computational Visual Media 7, no. 2 (2021): 241–52. http://dx.doi.org/10.1007/s41095-021-0207-y.

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AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and lear

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Danilov, Alexander A., Timur M. Gamilov, Fuyou Liang, et al. "Myocardial perfusion segmentation and partitioning methods in personalized models of coronary blood flow." Russian Journal of Numerical Analysis and Mathematical Modelling 38, no. 5 (2023): 293–302. http://dx.doi.org/10.1515/rnam-2023-0022.

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Abstract In this work we present methods and algorithms for construction of a personalized model of coronary haemodynamics based on computed tomography images. This model provides estimations of fractional flow reserve, coronary flow reserve, and instantaneous wave-free ratio taking into account transmural perfusion ratio indices obtained from perfusion images. The presented pipeline consists of the following steps: aorta segmentation, left ventricle wall segmentation, coronary arteries segmentation, construction of 1D network of vessels, partitioning of left ventricle wall, and personalizatio

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Vatu, Mihaela, Daniela Vintilă, Dragoş Laurenţiu Popa, Veronica Mercuţ, Sanda Mihaela Popescu, and Georgiana Vintila. "Simulations Using Finite Element Method Made on a Personalized Dental System." Advanced Engineering Forum 34 (October 2019): 175–82. http://dx.doi.org/10.4028/www.scientific.net/aef.34.175.

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The human skull and the maxillary bones have a very complicated architecture, determined by the outer walls, by the internal bone structures and their joining. In this paper CAD parametric software has been used to define complex virtual models. First, the mandible and jaw were defined using CT images. These images were imported into a CAD software using specific techniques and methods. These models have been finalized in SolidWorks where the virtual model of the studied system has been generated. Then, the virtual models were exported to a software for FEA simulation and prepared for every de

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Chen, Xi, Qinjin Li, Fuxia Zou, Dan Wang, Tao Li, and Mengyao Hu. "Precision Marketing Strategy for Ecotourism Based on Data Mining and User Images." Mobile Information Systems 2022 (June 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/1104633.

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With the continuous development of ecotourism industry, it has become a common concern in ecotourism industry to utilize the available data to realize the change of ecotourism industry and promote the product development and business expansion of ecotourism industry. To this end, we propose a personalized ecotourism route recommendation (PTIR) algorithm based on the popularity of points of interest (POI) and users’ interest preferences. First, we analyze the real historical ecotourism footprints of users, propose a time-based user interest preference, and design a method to calculate the best

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Mahesh, Sahana. "OCR and NLP based Personalized Allergen Notifying System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41634.

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This project focuses on developing a personalized allergen notification system to help individuals identify potential allergens in food products based on their dietary preferences and known allergens. A key feature of this system is Optical Character Recognition (OCR), which enables users to scan ingredient lists directly from product packaging. The OCR technology extracts text from images of ingredients, which is then processed using natural language processing (NLP) and machine learning algorithms to compare the extracted text against the user's allergen profile. The system provides real-tim

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