Virtual Reality Intelligent Hardware最新文献_第3页

Mesh representation matters: investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models 网格表示很重要：研究不同网格特征对深度三维可变形模型的感知和空间保真度的影响

Virtual Reality Intelligent Hardware Pub Date : 2024-10-01 DOI: 10.1016/j.vrih.2024.08.006

Robert KOSK , Richard SOUTHERN , Lihua YOU , Shaojun BIAN , Willem KOKKE , Greg MAGUIRE

{"title":"Mesh representation matters: investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models","authors":"Robert KOSK , Richard SOUTHERN , Lihua YOU , Shaojun BIAN , Willem KOKKE , Greg MAGUIRE","doi":"10.1016/j.vrih.2024.08.006","DOIUrl":"10.1016/j.vrih.2024.08.006","url":null,"abstract":"<div><h3>Background</h3><div>Deep 3D morphable models (deep 3DMMs) play an essential role in computer vision. They are used in facial synthesis, compression, reconstruction and animation, avatar creation, virtual try-on, facial recognition systems and medical imaging. These applications require high spatial and perceptual quality of synthesised meshes. Despite their significance, these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.</div></div><div><h3>Methods</h3><div>We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes. This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with <span><math><mrow><msub><mi>L</mi><mn>1</mn></msub></mrow></math></span> and <span><math><mrow><msub><mi>L</mi><mn>2</mn></msub></mrow></math></span> norm metrics and underperforms on perceptual metrics. In contrast, using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error. The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.</div></div><div><h3>Results</h3><div>The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 383-395"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CURDIS: A template for incremental curve discretization algorithms and its application to conics CURDIS：增量曲线离散化算法模板及其在圆锥曲线中的应用

Virtual Reality Intelligent Hardware Pub Date : 2024-10-01 DOI: 10.1016/j.vrih.2024.06.005

Philippe Latour, Marc Van Droogenbroeck

{"title":"CURDIS: A template for incremental curve discretization algorithms and its application to conics","authors":"Philippe Latour, Marc Van Droogenbroeck","doi":"10.1016/j.vrih.2024.06.005","DOIUrl":"10.1016/j.vrih.2024.06.005","url":null,"abstract":"<div><div>We introduce CURDIS, a template for algorithms to discretize arcs of regular curves by incrementally producing a list of support pixels covering the arc. In this template, algorithms proceed by finding the tangent quadrant at each point of the arc and determining which side the curve exits the pixel according to a tailored criterion. These two elements can be adapted for any type of curve, leading to algorithms dedicated to the shape of specific curves. While the calculation of the tangent quadrant for various curves, such as lines, conics, or cubics, is simple, it is more complex to analyze how pixels are traversed by the curve. In the case of conic arcs, we found a criterion for determining the pixel exit side. This leads us to present a new algorithm, called CURDIS-C, specific to the discretization of conics, for which we provide all the details. Surprisingly, the criterion for conics requires between one and three sign tests and four additions per pixel, making the algorithm efficient for resource-constrained systems and feasible for fixed-point or integer arithmetic implementations. Our algorithm also perfectly handles the pathological cases in which the conic intersects a pixel twice or changes quadrants multiple times within this pixel, achieving this generality at the cost of potentially computing up to two square roots per arc. We illustrate the use of CURDIS for the discretization of different curves, such as ellipses, hyperbolas, and parabolas, even when they degenerate into lines or corners.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 358-382"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MKEAH： Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering MKEAH：基于超平面嵌入的多模态知识提取和积累，用于基于知识的视觉问题解答

Virtual Reality Intelligent Hardware Pub Date : 2024-08-01 DOI: 10.1016/j.vrih.2023.06.002

Heng Zhang , Zhihua Wei , Guanming Liu , Rui Wang , Ruibin Mu , Chuanbao Liu , Aiquan Yuan , Guodong Cao , Ning Hu

{"title":"MKEAH： Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering","authors":"Heng Zhang , Zhihua Wei , Guanming Liu , Rui Wang , Ruibin Mu , Chuanbao Liu , Aiquan Yuan , Guodong Cao , Ning Hu","doi":"10.1016/j.vrih.2023.06.002","DOIUrl":"10.1016/j.vrih.2023.06.002","url":null,"abstract":"<div><h3>Background</h3><p>External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world. Recent entity-relationship embedding approaches are deficient in representing some complex relations, resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.</p></div><div><h3>Methods</h3><p>To this end, we propose MKEAH: Multimodal Knowledge Extraction and Accumulation on Hyperplanes. To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information, two losses are proposed to learn the triplet representations from the complementary views: range loss and orthogonal loss. To interpret the capability of extracting topic-related knowledge, we present the Topic Similarity (TS) between topic and entity-relations.</p></div><div><h3>Results</h3><p>Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering. Our model outperformed state-of-the-art methods by 2.12% and 3.24% on two challenging knowledge-request datasets: OK-VQA and KRVQA, respectively.</p></div><div><h3>Conclusions</h3><p>The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 4","pages":"Pages 280-291"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000268/pdfft?md5=74ea90656cf281de7a0e35aa5b55705b&pid=1-s2.0-S2096579623000268-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-scale context-aware network for continuous sign language recognition 用于连续手语识别的多尺度情境感知网络

Virtual Reality Intelligent Hardware Pub Date : 2024-08-01 DOI: 10.1016/j.vrih.2023.06.011

Senhua XUE, Liqing GAO, Liang WAN, Wei FENG

{"title":"Multi-scale context-aware network for continuous sign language recognition","authors":"Senhua XUE, Liqing GAO, Liang WAN, Wei FENG","doi":"10.1016/j.vrih.2023.06.011","DOIUrl":"10.1016/j.vrih.2023.06.011","url":null,"abstract":"<div><p>The hands and face are the most important parts for expressing sign language morphemes in sign language videos. However, we find that existing Continuous Sign Language Recognition (CSLR) methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information. In addition, the signs have different lengths, whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling, which disturbs the perception of complete signs. In this study, we propose a Multi-Scale Context-Aware network (MSCA-Net) to solve the aforementioned problems. Our MSCA-Net contains two main modules: <strong>(</strong>1) Multi-Scale Motion Attention (MSMA), which uses the differences among frames to perceive information of the hands and face in multiple spatial scales, replacing the heavy feature extractors; and <strong>(</strong>2) Multi-Scale Temporal Modeling (MSTM), which explores crucial temporal information in the sign language video from different temporal scales. We conduct extensive experiments using three widely used sign language datasets, i.e., RWTH-PHOENIX-Weather-2014, RWTH-PHOENIX-Weather-2014T, and CSL-Daily. The proposed MSCA-Net achieve state-of-the-art performance, demonstrating the effectiveness of our approach.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 4","pages":"Pages 323-337"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000414/pdfft?md5=d9cac344d105f6ddc495c1cb1e50a67a&pid=1-s2.0-S2096579623000414-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust blind image watermarking based on interest points 基于兴趣点的鲁棒盲图像水印技术

Virtual Reality Intelligent Hardware Pub Date : 2024-08-01 DOI: 10.1016/j.vrih.2023.06.012

Zizhuo WANG, Kun HU, Chaoyangfan HUANG, Zixuan HU, Shuo YANG, Xingjun WANG

{"title":"Robust blind image watermarking based on interest points","authors":"Zizhuo WANG, Kun HU, Chaoyangfan HUANG, Zixuan HU, Shuo YANG, Xingjun WANG","doi":"10.1016/j.vrih.2023.06.012","DOIUrl":"10.1016/j.vrih.2023.06.012","url":null,"abstract":"<div><p>Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability. However, image watermarking algorithms are weak against hybrid attacks, especially geometric at-tacks, such as cropping attacks, rotation attacks, etc. We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further. First, to extract more sparse and stable interest points, we use the Superpoint algorithm for generation and design two steps to perform the screening procedure. We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability. The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework. Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks. We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks. Through extensive experimental results, we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image. Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 4","pages":"Pages 308-322"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000426/pdfft?md5=0d46d851b07db92670b0c63431ec427e&pid=1-s2.0-S2096579623000426-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

S2ANet: Combining local spectral and spatial point grouping for point cloud processing S2ANet：结合局部光谱和空间点分组进行点云处理

Virtual Reality Intelligent Hardware Pub Date : 2024-08-01 DOI: 10.1016/j.vrih.2023.06.005

Yujie LIU, Xiaorui SUN, Wenbin SHAO, Yafu YUAN

{"title":"S2ANet: Combining local spectral and spatial point grouping for point cloud processing","authors":"Yujie LIU, Xiaorui SUN, Wenbin SHAO, Yafu YUAN","doi":"10.1016/j.vrih.2023.06.005","DOIUrl":"10.1016/j.vrih.2023.06.005","url":null,"abstract":"<div><h3>Background</h3><p>Despite the recent progress in 3D point cloud processing using deep convolutional neural networks, the inability to extract local features remains a challenging problem. In addition, existing methods consider only the spatial domain in the feature extraction process.</p></div><div><h3>Methods</h3><p>In this paper, we propose a spectral and spatial aggregation convolutional network (S<sup>2</sup>ANet), which combines spectral and spatial features for point cloud processing. First, we calculate the local frequency of the point cloud in the spectral domain. Then, we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency. We simultaneously extract the local features in the spatial domain to supplement the final features.</p></div><div><h3>Results</h3><p>S<sup>2</sup>ANet was applied in several point cloud analysis tasks; it achieved state-of-the-art classification accuracies of 93.8%, 88.0%, and 83.1% on the ModelNet40, ShapeNetCore, and ScanObjectNN datasets, respectively. For indoor scene segmentation, training and testing were performed on the S3DIS dataset, and the mean intersection over union was 62.4%.</p></div><div><h3>Conclusions</h3><p>The proposed S<sup>2</sup>ANet can effectively capture the local geometric information of point clouds, thereby improving accuracy on various tasks.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 4","pages":"Pages 267-279"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000360/pdfft?md5=718a7d943dc6468abf44b38521bcc2cb&pid=1-s2.0-S2096579623000360-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating animatable 3D cartoon faces from single portraits 从单个肖像生成可动画化的 3D 卡通人脸

Virtual Reality Intelligent Hardware Pub Date : 2024-08-01 DOI: 10.1016/j.vrih.2023.06.010

Chuanyu PAN , Guowei YANG , Taijiang MU , Yu-Kun LAI

{"title":"Generating animatable 3D cartoon faces from single portraits","authors":"Chuanyu PAN , Guowei YANG , Taijiang MU , Yu-Kun LAI","doi":"10.1016/j.vrih.2023.06.010","DOIUrl":"10.1016/j.vrih.2023.06.010","url":null,"abstract":"<div><h3>Background</h3><p>With the development of virtual reality (VR) technology, there is a growing need for customized 3D avatars. However, traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled. This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.</p></div><div><h3>Methods</h3><p>First, we transferred an input real-world portrait to a stylized cartoon image using StyleGAN. We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture. Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision. Finally, we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.</p></div><div><h3>Conclusions</h3><p>Compared with prior arts, the qualitative and quantitative results show that our method achieves better accuracy, aesthetics, and similarity criteria. Furthermore, we demonstrated the capability of the proposed 3D model for real-time facial animation.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 4","pages":"Pages 292-307"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000359/pdfft?md5=e0641053e4314662ffe5dca1c167d86b&pid=1-s2.0-S2096579623000359-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ARGA-Unet: Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation ARGA-Unet：利用残差分组卷积和注意力机制进行脑肿瘤 MRI 图像分割的高级 U 网分割模型

Virtual Reality Intelligent Hardware Pub Date : 2024-06-01 DOI: 10.1016/j.vrih.2023.05.001

Siyi XUN , Yan ZHANG , Sixu DUAN , Mingwei WANG , Jiangang CHEN , Tong TONG , Qinquan GAO , Chantong LAM , Menghan HU , Tao TAN

{"title":"ARGA-Unet: Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation","authors":"Siyi XUN , Yan ZHANG , Sixu DUAN , Mingwei WANG , Jiangang CHEN , Tong TONG , Qinquan GAO , Chantong LAM , Menghan HU , Tao TAN","doi":"10.1016/j.vrih.2023.05.001","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.05.001","url":null,"abstract":"<div><h3>Background</h3><p>Magnetic resonance imaging (MRI) has played an important role in the rapid growth of medical imaging diagnostic technology, especially in the diagnosis and treatment of brain tumors owing to its non-invasive characteristics and superior soft tissue contrast. However, brain tumors are characterized by high non-uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature. In addition, the labeling of tumor areas is time-consuming and laborious.</p></div><div><h3>Methods</h3><p>To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation performance is further considered.</p></div><div><h3>Results</h3><p>In the experiments, the Dice score of the proposed segmentation model reached 97.581%, which is 12.438% higher than that of traditional U-net, demonstrating the effective segmentation of MRI brain tumor images.</p></div><div><h3>Conclusions</h3><p>In conclusion, we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 3","pages":"Pages 203-216"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000232/pdfft?md5=5e16730452951aa1e3b2edacee01d06e&pid=1-s2.0-S2096579623000232-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141481556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face animation based on multiple sources and perspective alignment 基于多源和透视对齐的人脸动画

Virtual Reality Intelligent Hardware Pub Date : 2024-06-01 DOI: 10.1016/j.vrih.2024.04.002

Yuanzong Mei , Wenyi Wang , Xi Liu , Wei Yong , Weijie Wu , Yifan Zhu , Shuai Wang , Jianwen Chen

{"title":"Face animation based on multiple sources and perspective alignment","authors":"Yuanzong Mei , Wenyi Wang , Xi Liu , Wei Yong , Weijie Wu , Yifan Zhu , Shuai Wang , Jianwen Chen","doi":"10.1016/j.vrih.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.vrih.2024.04.002","url":null,"abstract":"<div><h3>Background</h3><p>Face image animation generates a synthetic human face video that harmoniously integrates the identity derived from the source image and facial motion obtained from the driving video. This technology could be beneficial in multiple medical fields, such as diagnosis and privacy protection<em>.</em> Previous studies on face animation often relied on a single source image to generate an output video. With a significant pose difference between the source image and the driving frame, the quality of the generated video is likely to be suboptimal because the source image may not provide sufficient features for the warped feature map.</p></div><div><h3>Methods</h3><p>In this study, we propose a novel face-animation scheme based on multiple sources and perspective alignment to address these issues. We first introduce a multiple-source sampling and selection module to screen the optimal source image set from the provided driving video. We then propose an inter-frame interpolation and alignment module to further eliminate the misalignment between the selected source image and the driving frame.</p></div><div><h3>Conclusions</h3><p>The proposed method exhibits superior performance in terms of objective metrics and visual quality in large-angle animation scenes compared to other state-of-the-art face animation methods. It indicates the effectiveness of the proposed method in addressing the distortion issues in large-angle animation.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 3","pages":"Pages 252-266"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579624000202/pdfft?md5=2a9475967792588ba319db5427a9033d&pid=1-s2.0-S2096579624000202-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of medical ocular image segmentation 医学眼部图像分割综述

Virtual Reality Intelligent Hardware Pub Date : 2024-06-01 DOI: 10.1016/j.vrih.2024.04.001

Lai WEI, Menghan HU

{"title":"A review of medical ocular image segmentation","authors":"Lai WEI, Menghan HU","doi":"10.1016/j.vrih.2024.04.001","DOIUrl":"https://doi.org/10.1016/j.vrih.2024.04.001","url":null,"abstract":"<div><p>Deep learning has been extensively applied to medical image segmentation, resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U-Net in 2015. However, the application of deep learning models to ocular medical image segmentation poses unique challenges, especially compared to other body parts, due to the complexity, small size, and blurriness of such images, coupled with the scarcity of data. This article aims to provide a comprehensive review of medical image segmentation from two perspectives: the development of deep network structures and the application of segmentation in ocular imaging. Initially, the article introduces an overview of medical imaging, data processing, and performance evaluation metrics. Subsequently, it analyzes recent developments in U-Net-based network structures. Finally, for the segmentation of ocular medical images, the application of deep learning is reviewed and categorized by the type of ocular tissue.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 3","pages":"Pages 181-202"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209657962400010X/pdfft?md5=c30a9952442a34ae8a35e52683ed1214&pid=1-s2.0-S209657962400010X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0