Pub Date : 2025-01-25DOI: 10.1016/j.cag.2025.104169
Jie Lin , Chuan-Kai Yang , Chiun-How Kao
Sports visualization analysis is an important area within visualization studies. However, there is a lack of tools tailored for NBA writers among existing systems. Creating these tools would improve understanding of the game’s complex dynamics, particularly player interactions.
We propose a visualization system to improve understanding of complex NBA game data. Featuring multiple modules, it allows users to analyze the game from various perspectives. This paper highlights the system’s use of storylines to examine player interactions, enhancing the extraction of valuable insights. The study shows that our design enhances personalized in-game data analysis, improving the understanding and aiding in identifying critical moments.
{"title":"Visualizing NBA information via storylines","authors":"Jie Lin , Chuan-Kai Yang , Chiun-How Kao","doi":"10.1016/j.cag.2025.104169","DOIUrl":"10.1016/j.cag.2025.104169","url":null,"abstract":"<div><div>Sports visualization analysis is an important area within visualization studies. However, there is a lack of tools tailored for NBA writers among existing systems. Creating these tools would improve understanding of the game’s complex dynamics, particularly player interactions.</div><div>We propose a visualization system to improve understanding of complex NBA game data. Featuring multiple modules, it allows users to analyze the game from various perspectives. This paper highlights the system’s use of storylines to examine player interactions, enhancing the extraction of valuable insights. The study shows that our design enhances personalized in-game data analysis, improving the understanding and aiding in identifying critical moments.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104169"},"PeriodicalIF":2.5,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a proof-of-concept fabrication method to transform edible cookies into information displays. The proposed method encodes the surface of cookie dough so that the displayed information changes with the viewpoint. We use a computational design method where small holes are bored into cookie dough at specific angles to create shapes that are only visible from a given perspective. This method allows for selective switching of information depending on the viewpoint position. We investigate the effects of baking time, hole depth, azimuth angle changes on the presented image, and select the appropriate hole spacing based on the number of presented images. Finally, we demonstrate the results and use cases of visualizing information on cookies.
{"title":"ShadoCookies: Creating user viewpoint-dependent information displays on edible cookies","authors":"Takumi Yamamoto , Takashi Amesaka , Anusha Withana , Yuta Sugiura","doi":"10.1016/j.cag.2024.104158","DOIUrl":"10.1016/j.cag.2024.104158","url":null,"abstract":"<div><div>In this paper, we propose a proof-of-concept fabrication method to transform edible cookies into information displays. The proposed method encodes the surface of cookie dough so that the displayed information changes with the viewpoint. We use a computational design method where small holes are bored into cookie dough at specific angles to create shapes that are only visible from a given perspective. This method allows for selective switching of information depending on the viewpoint position. We investigate the effects of baking time, hole depth, azimuth angle changes on the presented image, and select the appropriate hole spacing based on the number of presented images. Finally, we demonstrate the results and use cases of visualizing information on cookies.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104158"},"PeriodicalIF":2.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-21DOI: 10.1016/j.cag.2025.104167
Peigang Liu, Chenkang Wang, Yecong Wan, Penghui Lei
Restoring high-quality clean images from corrupted observations, commonly referred to as image restoration, has been a longstanding challenge in the computer vision community. Existing methods often struggle to recover fine-grained contextual details due to the lack of semantic awareness of the degraded images. To overcome this limitation, we propose a novel prompt-guided semantic-aware image restoration network, termed PSAIR, which can adaptively incorporate and exploit semantic priors of degraded images and reconstruct photographically fine-grained details. Specifically, we exploit the robust degradation filtering and semantic perception capabilities of the segmentation anything model and utilize it to provide non-destructive semantic priors to aid the network’s semantic perception of the degraded images. To absorb the semantic prior, we propose a semantic fusion module that adaptively utilizes the segmentation map to modulate the features of the degraded image thereby facilitating the network to better perceive different semantic regions. Furthermore, considering that the segmentation map does not provide semantic categories, to better facilitate the network’s customized restoration of different semantics we propose a prompt-guided module which dynamically guides the restoration of different semantics via learnable visual prompts. Comprehensive experiments demonstrate that our PSAIR can restore finer contextual details and thus outperforms existing state-of-the-art methods by a large margin in terms of quantitative and qualitative evaluation.
{"title":"Prompting semantic priors for image restoration","authors":"Peigang Liu, Chenkang Wang, Yecong Wan, Penghui Lei","doi":"10.1016/j.cag.2025.104167","DOIUrl":"10.1016/j.cag.2025.104167","url":null,"abstract":"<div><div>Restoring high-quality clean images from corrupted observations, commonly referred to as image restoration, has been a longstanding challenge in the computer vision community. Existing methods often struggle to recover fine-grained contextual details due to the lack of semantic awareness of the degraded images. To overcome this limitation, we propose a novel prompt-guided semantic-aware image restoration network, termed PSAIR, which can adaptively incorporate and exploit semantic priors of degraded images and reconstruct photographically fine-grained details. Specifically, we exploit the robust degradation filtering and semantic perception capabilities of the segmentation anything model and utilize it to provide non-destructive semantic priors to aid the network’s semantic perception of the degraded images. To absorb the semantic prior, we propose a semantic fusion module that adaptively utilizes the segmentation map to modulate the features of the degraded image thereby facilitating the network to better perceive different semantic regions. Furthermore, considering that the segmentation map does not provide semantic categories, to better facilitate the network’s customized restoration of different semantics we propose a prompt-guided module which dynamically guides the restoration of different semantics via learnable visual prompts. Comprehensive experiments demonstrate that our PSAIR can restore finer contextual details and thus outperforms existing state-of-the-art methods by a large margin in terms of quantitative and qualitative evaluation.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104167"},"PeriodicalIF":2.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inspired by Neural Radiance Field’s (NeRF) groundbreaking success in novel view synthesis, current methods mostly employ variants of various deep neural network architectures, and use the combination of multi-scale feature maps with the target viewpoint to synthesize novel views. However, these methods only consider spatial domain features, inevitably leading to the loss of some details and edge information. To address these issues, this paper innovatively proposes the FreqSpace-NeRF (FS-NeRF), aiming to significantly enhance the rendering fidelity and generalization performance of NeRF in complex scenes by integrating the unique advantages of spectral domain and spatial domain deep neural networks, and combining contrastive learning driven data augmentation techniques. Specifically, the core contribution of this method lies in designing a dual-stream network architecture: on one hand, capturing global frequency features through Fourier transformation; on the other hand, finely refining local details using well-established spatial domain convolutional neural networks. Moreover, to ensure the model can more acutely distinguish subtle differences between different views, we propose two loss functions: Frequency-Space Contrastive Entropy Loss (FSCE Loss) and Adaptive Spectral Contrastive Loss (ASC Loss). This innovation aims to more effectively guide the data flow and focuses on minimizing the frequency discrepancies between different views. By comprehensively utilizing the fusion of spectral and spatial domain features along with contrastive learning, FS-NeRF achieves significant performance improvements in scene reconstruction tasks. Extensive qualitative and quantitative evaluations confirm that our method surpasses current state-of-the-art (SOTA) models in this field.
{"title":"FreqSpace-NeRF: A fourier-enhanced Neural Radiance Fields method via dual-domain contrastive learning for novel view synthesis","authors":"Xiaosheng Yu , Xiaolei Tian , Jubo Chen , Ying Wang","doi":"10.1016/j.cag.2025.104171","DOIUrl":"10.1016/j.cag.2025.104171","url":null,"abstract":"<div><div>Inspired by Neural Radiance Field’s (NeRF) groundbreaking success in novel view synthesis, current methods mostly employ variants of various deep neural network architectures, and use the combination of multi-scale feature maps with the target viewpoint to synthesize novel views. However, these methods only consider spatial domain features, inevitably leading to the loss of some details and edge information. To address these issues, this paper innovatively proposes the FreqSpace-NeRF (FS-NeRF), aiming to significantly enhance the rendering fidelity and generalization performance of NeRF in complex scenes by integrating the unique advantages of spectral domain and spatial domain deep neural networks, and combining contrastive learning driven data augmentation techniques. Specifically, the core contribution of this method lies in designing a dual-stream network architecture: on one hand, capturing global frequency features through Fourier transformation; on the other hand, finely refining local details using well-established spatial domain convolutional neural networks. Moreover, to ensure the model can more acutely distinguish subtle differences between different views, we propose two loss functions: Frequency-Space Contrastive Entropy Loss (FSCE Loss) and Adaptive Spectral Contrastive Loss (ASC Loss). This innovation aims to more effectively guide the data flow and focuses on minimizing the frequency discrepancies between different views. By comprehensively utilizing the fusion of spectral and spatial domain features along with contrastive learning, FS-NeRF achieves significant performance improvements in scene reconstruction tasks. Extensive qualitative and quantitative evaluations confirm that our method surpasses current state-of-the-art (SOTA) models in this field.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104171"},"PeriodicalIF":2.5,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-18DOI: 10.1016/j.cag.2025.104170
Mohammed Y. Abbass, H. Kasban, Zeinab F. Elsharkawy
Deep learning approaches have notable results in the area of computer vision applications. Our paper presents improved LYT-Net, a Lightweight YUV Transformer-based models, as an innovative method to improve low-light scenes. Unlike traditional Retinex-based methods, the proposed framework utilizes the chrominance (U and V) and luminance (Y) channels in YUV color-space, mitigating the complexity between color details and light in scenes. LYT-Net provides a thorough contextual realization of the image while keeping architecture burdens low. In order to tackle the issue of weak feature generation of traditional Channel-wise Denoiser (CWD) Block, improved CWD is proposed using Triplet Attention network. Triplet Attention network is exploited to capture both dynamics and static features. Qualitative and quantitative experiments demonstrate that the proposed technique effectively addresses images with varying exposure levels and outperforms state-of-the-art techniques. Furthermore, the proposed technique shows faster computational performance compared to other Retinex-based techniques, promoting it as a suitable option for real-time computer vision topics.
The source code is available at https://github.com/Mohammed-Abbass/YUV-Attention
{"title":"Low-light image enhancement via improved lightweight YUV attention network","authors":"Mohammed Y. Abbass, H. Kasban, Zeinab F. Elsharkawy","doi":"10.1016/j.cag.2025.104170","DOIUrl":"10.1016/j.cag.2025.104170","url":null,"abstract":"<div><div>Deep learning approaches have notable results in the area of computer vision applications. Our paper presents improved LYT-Net, a Lightweight YUV Transformer-based models, as an innovative method to improve low-light scenes. Unlike traditional Retinex-based methods, the proposed framework utilizes the chrominance (U and V) and luminance (Y) channels in YUV color-space, mitigating the complexity between color details and light in scenes. LYT-Net provides a thorough contextual realization of the image while keeping architecture burdens low. In order to tackle the issue of weak feature generation of traditional Channel-wise Denoiser (CWD) Block, improved CWD is proposed using Triplet Attention network. Triplet Attention network is exploited to capture both dynamics and static features. Qualitative and quantitative experiments demonstrate that the proposed technique effectively addresses images with varying exposure levels and outperforms state-of-the-art techniques. Furthermore, the proposed technique shows faster computational performance compared to other Retinex-based techniques, promoting it as a suitable option for real-time computer vision topics.</div><div>The source code is available at <span><span>https://github.com/Mohammed-Abbass/YUV-Attention</span><svg><path></path></svg></span></div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104170"},"PeriodicalIF":2.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-18DOI: 10.1016/j.cag.2025.104166
Mingdong Zhang, Li Chen, Junhai Yong
In recent years, storyline visualization has garnered considerable attention from the visualization research community. However, previous studies have given little focus to representing the locations of scene and addressing visual clutter issues, especially with larger datasets. In response to this gap, we propose an innovative visual analysis method named Stratiline (short for stratified storyline), which emphasizes multiperspective story data exploration and overview+detail analysis for large-scale datasets. Stratiline introduces a novel framework for calculating the significance of locations, actors, and scenes, providing a mechanism that incorporates user adjustments into the calculation framework to enable multiperspective exploration. Based on this calculation framework, Stratiline offers multiple coordinated views that collaboratively present different perspectives of the story while facilitating rich interactions. Specifically, Stratiline includes time-range drill-down features for overview+detail analysis, while the Storyline View allows for detailed analysis, and the Scene View provides an overview of the entire narrative to help maintain the mental map. The effectiveness of Stratiline is validated through comparative analyses against contemporary storyline designs. Carefully designed case studies illustrate Stratiline’s capability for multiperspective story exploration and large-scale dataset analysis. Quantitative evaluations affirm the stability of our sorting algorithms, which are crucial for time-range drill-down analysis.
{"title":"Stratiline: A visualization system based on stratified storyline","authors":"Mingdong Zhang, Li Chen, Junhai Yong","doi":"10.1016/j.cag.2025.104166","DOIUrl":"10.1016/j.cag.2025.104166","url":null,"abstract":"<div><div>In recent years, storyline visualization has garnered considerable attention from the visualization research community. However, previous studies have given little focus to representing the locations of scene and addressing visual clutter issues, especially with larger datasets. In response to this gap, we propose an innovative visual analysis method named Stratiline (short for stratified storyline), which emphasizes multiperspective story data exploration and overview+detail analysis for large-scale datasets. Stratiline introduces a novel framework for calculating the significance of locations, actors, and scenes, providing a mechanism that incorporates user adjustments into the calculation framework to enable multiperspective exploration. Based on this calculation framework, Stratiline offers multiple coordinated views that collaboratively present different perspectives of the story while facilitating rich interactions. Specifically, Stratiline includes time-range drill-down features for overview+detail analysis, while the Storyline View allows for detailed analysis, and the Scene View provides an overview of the entire narrative to help maintain the mental map. The effectiveness of Stratiline is validated through comparative analyses against contemporary storyline designs. Carefully designed case studies illustrate Stratiline’s capability for multiperspective story exploration and large-scale dataset analysis. Quantitative evaluations affirm the stability of our sorting algorithms, which are crucial for time-range drill-down analysis.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104166"},"PeriodicalIF":2.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-17DOI: 10.1016/j.cag.2025.104172
Vítor J. Sá , Anabela Marto , Paula Alexandra Silva , Alan Chalmers
{"title":"Foreword to the special section on Recent Advances in Graphics and Interaction (RAGI 2023)","authors":"Vítor J. Sá , Anabela Marto , Paula Alexandra Silva , Alan Chalmers","doi":"10.1016/j.cag.2025.104172","DOIUrl":"10.1016/j.cag.2025.104172","url":null,"abstract":"","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104172"},"PeriodicalIF":2.5,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-17DOI: 10.1016/j.cag.2025.104165
Bernardo Marques, Paulo Dias, Kangsoo Kim, Heejin Jeong
{"title":"Foreword to the special section on Recent Advances in Industrial eXtended Reality (XR)","authors":"Bernardo Marques, Paulo Dias, Kangsoo Kim, Heejin Jeong","doi":"10.1016/j.cag.2025.104165","DOIUrl":"10.1016/j.cag.2025.104165","url":null,"abstract":"","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104165"},"PeriodicalIF":2.5,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.cag.2024.104127
Tao Ku , Sam Galanakis , Bas Boom , Remco C. Veltkamp , Darshan Bangera , Shankar Gangisetty , Nikolaos Stagakis , Gerasimos Arvanitis , Konstantinos Moustakas
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy).
This article has been retracted at the request of the author and Editor-in-Chief.
The authors identified an error in the original paper with the software that was made publicly available on GitHub, where accidentally the testing was carried out using the training set, instead of the correct test set, and therefore the published test results are invalid.
In addition, other minor inaccuracies in the paper were also identified.
The authors intend to correct the errors and resubmit the paper.
{"title":"Retraction notice to “SHREC 2021: 3D point cloud change detection for street scenes”","authors":"Tao Ku , Sam Galanakis , Bas Boom , Remco C. Veltkamp , Darshan Bangera , Shankar Gangisetty , Nikolaos Stagakis , Gerasimos Arvanitis , Konstantinos Moustakas","doi":"10.1016/j.cag.2024.104127","DOIUrl":"10.1016/j.cag.2024.104127","url":null,"abstract":"<div><div>This article has been retracted: please see Elsevier Policy on Article Withdrawal (<span><span>https://www.elsevier.com/locate/withdrawalpolicy</span><svg><path></path></svg></span>).</div><div>This article has been retracted at the request of the author and Editor-in-Chief.</div><div>The authors identified an error in the original paper with the software that was made publicly available on GitHub, where accidentally the testing was carried out using the training set, instead of the correct test set, and therefore the published test results are invalid.</div><div>In addition, other minor inaccuracies in the paper were also identified.</div><div>The authors intend to correct the errors and resubmit the paper.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"125 ","pages":"Article 104127"},"PeriodicalIF":2.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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