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200 results on '"Kim, Jinman"'

1. 3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks

Author

Li, Xiaoshuang, Meng, Mingyuan, Huang, Zimo, Bi, Lei, Delamare, Eduardo, Feng, Dagan, Sheng, Bin, and Kim, Jinman

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Panoramic X-ray (PX) is a prevalent modality in dental practice for its wide availability and low cost. However, as a 2D projection image, PX does not contain 3D anatomical information, and therefore has limited use in dental applications that can benefit from 3D information, e.g., tooth angular misa-lignment detection and classification. Reconstructing 3D structures directly from 2D PX has recently been explored to address limitations with existing methods primarily reliant on Convolutional Neural Networks (CNNs) for direct 2D-to-3D mapping. These methods, however, are unable to correctly infer depth-axis spatial information. In addition, they are limited by the in-trinsic locality of convolution operations, as the convolution kernels only capture the information of immediate neighborhood pixels. In this study, we propose a progressive hybrid Multilayer Perceptron (MLP)-CNN pyra-mid network (3DPX) for 2D-to-3D oral PX reconstruction. We introduce a progressive reconstruction strategy, where 3D images are progressively re-constructed in the 3DPX with guidance imposed on the intermediate recon-struction result at each pyramid level. Further, motivated by the recent ad-vancement of MLPs that show promise in capturing fine-grained long-range dependency, our 3DPX integrates MLPs and CNNs to improve the semantic understanding during reconstruction. Extensive experiments on two large datasets involving 464 studies demonstrate that our 3DPX outperforms state-of-the-art 2D-to-3D oral reconstruction methods, including standalone MLP and transformers, in reconstruction quality, and also im-proves the performance of downstream angular misalignment classification tasks., Comment: accepted by MICCAI 2024

Published
2024

2. GVT2RPM: An Empirical Study for General Video Transformer Adaptation to Remote Physiological Measurement

Author

Wang, Hao, Ahn, Euijoon, and Kim, Jinman

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Remote physiological measurement (RPM) is an essential tool for healthcare monitoring as it enables the measurement of physiological signs, e.g., heart rate, in a remote setting via physical wearables. Recently, with facial videos, we have seen rapid advancements in video-based RPMs. However, adopting facial videos for RPM in the clinical setting largely depends on the accuracy and robustness (work across patient populations). Fortunately, the capability of the state-of-the-art transformer architecture in general (natural) video understanding has resulted in marked improvements and has been translated to facial understanding, including RPM. However, existing RPM methods usually need RPM-specific modules, e.g., temporal difference convolution and handcrafted feature maps. Although these customized modules can increase accuracy, they are not demonstrated for their robustness across datasets. Further, due to their customization of the transformer architecture, they cannot use the advancements made in general video transformers (GVT). In this study, we interrogate the GVT architecture and empirically analyze how the training designs, i.e., data pre-processing and network configurations, affect the model performance applied to RPM. Based on the structure of video transformers, we propose to configure its spatiotemporal hierarchy to align with the dense temporal information needed in RPM for signal feature extraction. We define several practical guidelines and gradually adapt GVTs for RPM without introducing RPM-specific modules. Our experiments demonstrate favorable results to existing RPM-specific module counterparts. We conducted extensive experiments with five datasets using intra-dataset and cross-dataset settings. We highlight that the proposed guidelines GVT2RPM can be generalized to any video transformers and is robust to various datasets., Comment: Code is available at https://github.com/Dylan-H-Wang/facial-ai

Published
2024

3. Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration

Author

Meng, Mingyuan, Feng, Dagan, Bi, Lei, and Kim, Jinman

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Deformable image registration is a fundamental step for medical image analysis. Recently, transformers have been used for registration and outperformed Convolutional Neural Networks (CNNs). Transformers can capture long-range dependence among image features, which have been shown beneficial for registration. However, due to the high computation/memory loads of self-attention, transformers are typically used at downsampled feature resolutions and cannot capture fine-grained long-range dependence at the full image resolution. This limits deformable registration as it necessitates precise dense correspondence between each image pixel. Multi-layer Perceptrons (MLPs) without self-attention are efficient in computation/memory usage, enabling the feasibility of capturing fine-grained long-range dependence at full resolution. Nevertheless, MLPs have not been extensively explored for image registration and are lacking the consideration of inductive bias crucial for medical registration tasks. In this study, we propose the first correlation-aware MLP-based registration network (CorrMLP) for deformable medical image registration. Our CorrMLP introduces a correlation-aware multi-window MLP block in a novel coarse-to-fine registration architecture, which captures fine-grained multi-range dependence to perform correlation-aware coarse-to-fine registration. Extensive experiments with seven public medical datasets show that our CorrMLP outperforms state-of-the-art deformable registration methods., Comment: Accepted at CVPR2024 as Oral Presentation && Best Paper Candidate

Published
2024

4. Dual-modal Dynamic Traceback Learning for Medical Report Generation

Author

Ye, Shuchang, Meng, Mingyuan, Li, Mingjian, Feng, Dagan, and Kim, Jinman

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

With increasing reliance on medical imaging in clinical practices, automated report generation from medical images is in great demand. Existing report generation methods typically adopt an encoder-decoder deep learning framework to build a uni-directional image-to-report mapping. However, such a framework ignores the bi-directional mutual associations between images and reports, thus incurring difficulties in associating the intrinsic medical meanings between them. Recent generative representation learning methods have demonstrated the benefits of dual-modal learning from both image and text modalities. However, these methods exhibit two major drawbacks for medical report generation: 1) they tend to capture morphological information and have difficulties in capturing subtle pathological semantic information, and 2) they predict masked text rely on both unmasked images and text, inevitably degrading performance when inference is based solely on images. In this study, we propose a new report generation framework with dual-modal dynamic traceback learning (DTrace) to overcome the two identified drawbacks and enable dual-modal learning for medical report generation. To achieve this, our DTrace introduces a traceback mechanism to control the semantic validity of generated content via self-assessment. Further, our DTrace introduces a dynamic learning strategy to adapt to various proportions of image and text input, enabling report generation without reliance on textual input during inference. Extensive experiments on two well-benchmarked datasets (IU-Xray and MIMIC-CXR) show that our DTrace outperforms state-of-the-art medical report generation methods.

Published
2024

5. Enhancing medical vision-language contrastive learning via inter-matching relation modelling

Author

Li, Mingjian, Meng, Mingyuan, Fulham, Michael, Feng, David Dagan, Bi, Lei, and Kim, Jinman

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using four public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, supervised classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities., Comment: 11 pages, 5 figures. Under review

Published
2024

8. HRDC challenge: a public benchmark for hypertension and hypertensive retinopathy classification from fundus images

Author

Qian, Bo, Wang, Xiangning, Guan, Zhouyu, Yang, Dawei, Ran, Anran, Li, Tingyao, Wang, Zheyuan, Wen, Yang, Shu, Xinming, Xie, Jinyang, Liu, Shichang, Xing, Guanyu, Silva-Rodríguez, Julio, Kobbi, Riadh, Li, Ping, Chen, Tingli, Bi, Lei, Kim, Jinman, Jia, Weiping, Li, Huating, Qin, Jing, Zhang, Ping, Cheng, Ching-Yu, Heng, Pheng-Ann, Wong, Tien Yin, Cheung, Carol Y., Tham, Yih-Chung, Thalmann, Nadia Magnenat, and Sheng, Bin

Published
2024
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11. Challenges and Constraints in Deformation-Based Medical Mesh Representation

Author

Jin, Ge, Jung, Younhyun, Kim, Jinman, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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13. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

Author

Saha, Anindo, Bosma, Joeran S., Twilt, Jasper J., van Ginneken, Bram, Noordman, Constant R., Slootweg, Ivan, Roest, Christian, Fransen, Stefan J., Sunoqrot, Mohammed R.S., Bathen, Tone F., Rouw, Dennis, Immerzeel, Jos, Geerdink, Jeroen, van Run, Chris, Groeneveld, Miriam, Meakin, James, Karagöz, Ahmet, Bône, Alexandre, Routier, Alexandre, Marcoux, Arnaud, Abi-Nader, Clément, Li, Cynthia Xinran, Feng, Dagan, Alis, Deniz, Karaarslan, Ercan, Ahn, Euijoon, Nicolas, François, Sonn, Geoffrey A., Bhattacharya, Indrani, Kim, Jinman, Shi, Jun, Jahanandish, Hassan, An, Hong, Kan, Hongyu, Oksuz, Ilkay, Qiao, Liang, Rohé, Marc-Michel, Yergin, Mert, Khadra, Mohamed, Şeker, Mustafa E., Kartal, Mustafa S., Debs, Noëlie, Fan, Richard E., Saunders, Sara, Soerensen, Simon J.C., Moroianu, Stefania, Vesal, Sulaiman, Yuan, Yuan, Malakoti-Fard, Afsoun, Mačiūnien, Agnė, Kawashima, Akira, de Sousa Machadov, Ana M.M. de M.G., Moreira, Ana Sofia L., Ponsiglione, Andrea, Rappaport, Annelies, Stanzione, Arnaldo, Ciuvasovas, Arturas, Turkbey, Baris, de Keyzer, Bart, Pedersen, Bodil G., Eijlers, Bram, Chen, Christine, Riccardo, Ciabattoni, Courrech Staal, Ewout F.W., Jäderling, Fredrik, Langkilde, Fredrik, Aringhieri, Giacomo, Brembilla, Giorgio, Son, Hannah, Vanderlelij, Hans, Raat, Henricus P.J., Pikūnienė, Ingrida, Macova, Iva, Schoots, Ivo, Caglic, Iztok, Zawaideh, Jeries P., Wallström, Jonas, Bittencourt, Leonardo K., Khurram, Misbah, Choi, Moon H., Takahashi, Naoki, Tan, Nelly, Franco, Paolo N., Gutierrez, Patricia A., Thimansson, Per Erik, Hanus, Pieter, Puech, Philippe, Rau, Philipp R., de Visschere, Pieter, Guillaume, Ramette, Cuocolo, Renato, Falcão, Ricardo O., van Stiphout, Rogier S.A., Girometti, Rossano, Briediene, Ruta, Grigienė, Rūta, Gitau, Samuel, Withey, Samuel, Ghai, Sangeet, Penzkofer, Tobias, Barrett, Tristan, Tammisetti, Varaha S., Løgager, Vibeke B., Černý, Vladimír, Venderink, Wulphert, Law, Yan M., Lee, Young J., Bjartell, Anders, Padhani, Anwar R., Bonekamp, David, Villeirs, Geert, Salomon, Georg, Giannarini, Gianluca, Kalpathy-Cramer, Jayashree, Barentsz, Jelle, Maier-Hein, Klaus H., Rusu, Mirabela, Obuchowski, Nancy A., Rouvière, Olivier, van den Bergh, Roderick, Panebianco, Valeria, Kasivisvanathan, Veeru, Yakar, Derya, Elschot, Mattijs, Veltman, Jeroen, Fütterer, Jurgen J., de Rooij, Maarten, Huisman, Henkjan, Bosma, Joeran S, Twilt, Jasper J, Padhani, Anwar R, Maier-Hein, Klaus H, Obuchowski, Nancy A, and Fütterer, Jurgen J

Published
2024
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19. Optimal Limestone Content on Hydration Properties of Ordinary Portland Cement with 5% Ground Granulated Blast-Furnace Slag.

Author

Kang, Ingyu, Shin, Sangchul, and Kim, Jinman

Subjects
LIMESTONE, PORTLAND cement, HEAT of hydration, SLAG, COMPRESSIVE strength, X-ray diffraction, HYDRATION, DILUTION
Abstract

In this study, the effect of limestone content on the mechanical performance and the heat of hydration of ordinary Portland cement (OPC) was investigated. Changes in the phase assemblage were analyzed through XRD and thermodynamic modeling. The purpose of the study was to identify the optimal limestone content in OPC. As a result of the experiment, all samples were found to have equal fluidity. Increasing the limestone content accelerated the hydration of the cement before approximately 13 h and shortened the setting time due to the acceleration of the initial hydration reaction. The compressive strength of the cement mortar showed a dilution effect, with lower compressive strength compared to the reference sample at an early age, but it gradually recovered at a later age. This is because, as shown in the XRD and thermodynamic modeling results, the carboaluminate phases formed due to the chemical effect of limestone contributed to the development of compressive strength. As a result, within the scope of this study, it is believed that maintaining the limestone content in OPC within 10% is optimal to minimize quality degradation. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Co-Learning Multimodality PET-CT Features via a Cascaded CNN-Transformer Network

Author

Bi, Lei, Fu, Xiaohang, Liu, Qiufang, Song, Shaoli, Dagan Feng, David, Fulham, Michael, and Kim, Jinman

Abstract

Background: Automated segmentation of multimodality positron emission tomography—computed tomography (PET-CT) data is a major challenge in the development of computer-aided diagnosis systems (CADs). In this context, convolutional neural network (CNN)-based methods are considered as the state-of-the-art. These CNN-based methods, however, have difficulty in co-learning the complementary PET-CT image features and in learning the global context when focusing solely on local patterns. Methods: We propose a cascaded CNN-transformer network (CCNN-TN) tailored for PET-CT image segmentation. We employed a transformer network (TN) because of its ability to establish global context via self-attention and embedding image patches. We extended the TN definition by cascading multiple TNs and CNNs to learn the global and local contexts. We also introduced a hyper fusion branch that iteratively fuses the separately extracted complementary image features. We evaluated our approach, when compared to current state-of-the-art CNN methods, on three datasets: two nonsmall cell lung cancer (NSCLC) and one soft tissue sarcoma (STS). Results: Our CCNN-TN method achieved a dice similarity coefficient (DSC) score of 72.25% (NSCLC), 67.11% (NSCLC), and 66.36% (STS) for segmentation of tumors. Compared to other methods the DSC was higher for our CCNN-TN by 4.5%, 1.31%, and 3.44%. Conclusion: Our experimental results demonstrate that CCNN-TN, when compared to the existing methods, achieved more generalizable results across different datasets and has consistent performance across various image fusion strategies and network backbones.

Published
2024
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24. Panoramic imaging errors in machine learning model development: a systematic review.

Author

Delamare, Eduardo, Fu, Xingyue, Huang, Zimo, and Kim, Jinman

Subjects
MACHINE learning, DEEP learning, PANORAMIC radiography, IMAGE intensifiers, ARTIFICIAL intelligence
Abstract

Objectives To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models. Methods This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature. Eligibility criteria PAN studies that used ML models and mentioned image quality concerns. Results Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias. Conclusions This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks. [ABSTRACT FROM AUTHOR]

Published
2024
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25. BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.

Author

Fu, Xiaohang, Lin, Yingxin, Lin, David M., Mechtersheimer, Daniel, Wang, Chuhan, Ameen, Farhan, Ghazanfar, Shila, Patrick, Ellis, Kim, Jinman, and Yang, Jean Y. H.

Subjects
TRANSCRIPTOMES, DEEP learning, SUPERVISED learning, GENE expression, GENE mapping, CELL morphology
Abstract

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery. Subcellular in situ spatial transcriptomics offers the promise to address biological problems that were previously inaccessible but requires accurate cell segmentation to uncover insights. Here, authors present BIDCell, a biologically informed, deep learning-based cell segmentation framework. [ABSTRACT FROM AUTHOR]

Published
2024
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26. K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering

Author

Naseem, Usman, Khushi, Matloob, Dunn, Adam G., and Kim, Jinman

Abstract

Pathology imaging is routinely used to detect the underlying effects and causes of diseases or injuries. Pathology visual question answering (PathVQA) aims to enable computers to answer questions about clinical visual findings from pathology images. Prior work on PathVQA has focused on directly analyzing the image content using conventional pretrained encoders without utilizing relevant external information when the image content is inadequate. In this article, we present a knowledge-driven PathVQA (K-PathVQA), which uses a medical knowledge graph (KG) from a complementary external structured knowledge base to infer answers for the PathVQA task. K-PathVQA improves the question representation with external medical knowledge and then aggregates vision, language, and knowledge embeddings to learn a joint knowledge-image-question representation. Our experiments using a publicly available PathVQA dataset showed that our K-PathVQA outperformed the best baseline method with an increase of 4.15% in accuracy for the overall task, an increase of 4.40% in open-ended question type and an absolute increase of 1.03% in closed-ended question types. Ablation testing shows the impact of each of the contributions. Generalizability of the method is demonstrated with a separate medical VQA dataset.

Published
2024
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27. PET Synthesis via Self-Supervised Adaptive Residual Estimation Generative Adversarial Network

Author

Xue, Yuxin, Bi, Lei, Peng, Yige, Fulham, Michael, Feng, David Dagan, and Kim, Jinman

Abstract

Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from “low-dose” counterparts have been reported to be “state-of-the-art” for low-to-high-image recovery methods. However, these methods are prone to exhibiting discrepancies in texture and structure between synthesized and real images. Furthermore, the distribution shift between low-dose PET and standard PET has not been fully investigated. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN). We introduce 1) an adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input and 2) a self-supervised pretraining strategy to enhance the feature representation of the coarse generator. Our experiments with a public benchmark dataset of total-body PET images show that SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.

Published
2024
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28. SparseVoxNet: 3-D Object Recognition With Sparsely Aggregation of 3-D Dense Blocks

Author

Karambakhsh, Ahmad, Sheng, Bin, Li, Ping, Li, Huating, Kim, Jinman, Jung, Younhyun, and Chen, C. L. Philip

Abstract

Automatic recognition of 3-D objects in a 3-D model by convolutional neural network (CNN) methods has been successfully applied to various tasks, e.g., robotics and augmented reality. Three-dimensional object recognition is mainly performed by analyzing the object using multi-view images, depth images, graphs, or volumetric data. In some cases, using volumetric data provides the most promising results. However, existing recognition techniques on volumetric data have many drawbacks, such as losing object details on converting points to voxels and the large size of the input volume data that leads to substantial 3-D CNNs. Using point clouds could also provide very promising results; however, point-cloud-based methods typically need sparse data entry and time-consuming training stages. Thus, using volumetric could be a more efficient and flexible recognizer for our special case in the School of Medicine, Shanghai Jiao Tong University. In this article, we propose a novel solution to 3-D object recognition from volumetric data using a combination of three compact CNN models, low-cost SparseNet, and feature representation technique. We achieve an optimized network by estimating extra geometrical information comprising the surface normal and curvature into two separated neural networks. These two models provide supplementary information to each voxel data that consequently improve the results. The primary network model takes advantage of all the predicted features and uses these features in Random Forest (RF) for recognition purposes. Our method outperforms other methods in training speed in our experiments and provides an accurate result as good as the state-of-the-art.

Published
2024
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29. High-dimensional data visualization by interactive construction of low-dimensional parallel coordinate plots

Author

Itoh, Takayuki, Kumar, Ashnil, Klein, Karsten, and Kim, Jinman

Abstract

Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces. They are especially useful for the representation of correlations among the dimensions, which identify relationships and interdependencies between variables. However, within these high-dimensional spaces, PCPs face difficulties in displaying the correlation between combinations of dimensions and generally require additional display space as the number of dimensions increases. In this paper, we present a new technique for high-dimensional data visualization in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space. In our technique, we first construct a graph visualization of sets of well-correlated dimensions. Users observe this graph and are able to interactively select the dimensions by sampling from its cliques, thereby dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs. Our interactive sampling overcomes the shortcomings of the PCPs by enabling the visualization of the most meaningful dimensions (i.e., the most relevant information) from high-dimensional spaces. We demonstrate the effectiveness of our technique through two case studies, where we show that the proposed interactive low-dimensional space constructions were pivotal for visualizing the high-dimensional data and discovering new patterns.

Published
2024
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30. Determine the Camera Eigenmatrix from Large Parallax Images

Author

Du, Zhenlong, Feng, Yu, Li, Xiaoli, Chen, Dong, Zhao, Xiangjun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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31. Action Recognition via Fine-Tuned CLIP Model and Temporal Transformer

Author

Yang, Xiaoyu, Fu, Yuzhuo, Liu, Ting, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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32. 3DP Code-Based Compression and AR Visualization for Cardiovascular Palpation Training

Author

Chen, Zhendong, Peng, Bo, Gong, Kaifeng, Hao, Yinan, Xie, Xiaohua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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33. A New Camera Calibration Algorithm Based on Adaptive Image Selection

Author

Jian, Huang, Shan, Zeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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34. Multi-sensory Consistency Experience: A 6-DOF Simulation System Based on Video Automatically Generated Motion Effects

Author

Luan, Hongqiu, Wang, Yu, Huang, Li, Wang, Lutong, Lv, Gaorong, Gai, Wei, Luan, Xiaona, Yang, Chenglei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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35. Virtual Reality for the Preservation and Promotion of Historical Real Tennis

Author

Gaugne, Ronan, Saint-Auret, Sony, Duc-Martin, Pierre, Gouranton, Valérie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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36. Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy

Author

Silva-Rodriguez, Julio, Chelbi, Jihed, Kabir, Waziha, Chakor, Hadi, Dolz, Jose, Ayed, Ismail Ben, Kobbi, Riadh, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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37. The ST-GRNN Cooperative Training Model Based on Complex Network for Air Quality Prediction

Author

Chen, Shijie, Wang, Song, Liu, Yipan, Ma, Dongliang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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38. Mobile AR-Based Robot Motion Control from Sparse Finger Joints

Author

Wu, Di, Chen, Shengzhe, Wang, Meiheng, Cheng, Zhanglin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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39. SLf-UNet: Improved UNet for Brain MRI Segmentation by Combining Spatial and Low-Frequency Domain Features

Author

Ding, Hui, Lu, Jiacheng, Cai, Junwei, Zhang, Yawei, Shang, Yuanyuan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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40. Hybrid Prior-Based Diminished Reality for Indoor Panoramic Images

Author

Liu, Jiashu, Zhang, Qiudan, Shen, Xuelin, Wu, Wenhui, Wang, Xu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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41. Research on Deep Learning-Based Lightweight Object Grasping Algorithm for Robots

Author

Zhao, Yancheng, Wei, Tianxu, Du, Baoshuai, Zhao, Jingbo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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42. The Role of the Field Dependence-Independence Construct on the Curvature Gain of Redirected Walking Technology in Virtual Reality

Author

Jing, Rui, Lv, Gaorong, Luan, Hongqiu, Gai, Wei, Song, Shengzun, Yang, Chenglei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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43. VRGestures: Controller and Hand Gesture Datasets for Virtual Reality

Author

Papadopoulos, Georgios, Doumanoglou, Alexandros, Zarpalas, Dimitrios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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44. CAGviz: A Visual Analysis Method to Explore Cyber Asset Graphs of Cybercrime Gangs

Author

Liu, Yinuo, Li, Yifan, Zhao, Binhao, Zhou, Tianyi, Chen, Shengtao, Dong, Xiaoju, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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45. Visual Analytics of CO Emissions from Individuals’ Daily Travel Based on Large-Scale Taxi Trajectories

Author

Ma, Dongliang, Wang, Song, Liu, Liang, Hu, Hao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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46. Visual Analytics of Air Pollution Transmission Among Urban Agglomerations

Author

Chen, Shijie, Wang, Song, Liu, Yipan, Ma, Dongliang, Hu, Hao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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47. Human Joint Localization Method for Virtual Reality Based on Multi-device Data Fusion

Author

Chang, Zihan, Di, Xiaofei, Che, Xiaoping, Liu, Haiming, Su, Jingxi, Qu, Chenxin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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48. GVPM: Garment Simulation from Video Based on Priori Movements

Author

Miao, Jiazhe, Peng, Tao, Fang, Fei, Hu, Xinrong, Zhu, Ping, Yu, Feng, Jiang, Minghua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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49. A Novel Approach to Curved Layer Slicing and Path Planning for Multi-degree-of-Freedom 3D Printing

Author

Zeng, Yuqin, Chen, Zhen, Zhang, Weihao, Wang, Jiapeng, Fan, Shuqian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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50. Visual Analysis of Machine Tool Operation Mode Correlation Based on Parameter Category Coding

Author

Long, Jinxin, Peng, Lijuan, Li, Xuejun, Ma, Kaiming, Qiu, Feng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
Full Text
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