Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.14.coimg-a14
Abstract More than ever before, computers and computation are critical to the image formation process. Across diverse applications and fields, remarkably similar imaging problems appear, requiring sophisticated mathematical, statistical, and algorithmic tools. This conference focuses on imaging as a marriage of computation with physical devices. It emphasizes the interplay between mathematical theory, physical models, and computational algorithms that enable effective current and future imaging systems. Contributions to the conference are solicited on topics ranging from fundamental theoretical advances to detailed system-level implementations and case studies.
{"title":"Computational Imaging XXI Conference Overview and Papers Program","authors":"","doi":"10.2352/ei.2023.35.14.coimg-a14","DOIUrl":"https://doi.org/10.2352/ei.2023.35.14.coimg-a14","url":null,"abstract":"Abstract More than ever before, computers and computation are critical to the image formation process. Across diverse applications and fields, remarkably similar imaging problems appear, requiring sophisticated mathematical, statistical, and algorithmic tools. This conference focuses on imaging as a marriage of computation with physical devices. It emphasizes the interplay between mathematical theory, physical models, and computational algorithms that enable effective current and future imaging systems. Contributions to the conference are solicited on topics ranging from fundamental theoretical advances to detailed system-level implementations and case studies.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.15.color-a15
Abstract Color imaging has historically been treated as a phenomenon sufficiently described by three independent parameters. Recent advances in computational resources and in the understanding of the human aspects are leading to new approaches that extend the purely metrological view of color towards a perceptual approach describing the appearance of objects, documents and displays. Part of this perceptual view is the incorporation of spatial aspects, adaptive color processing based on image content, and the automation of color tasks, to name a few. This dynamic nature applies to all output modalities, including hardcopy devices, but to an even larger extent to soft-copy displays with their even larger options of dynamic processing. Spatially adaptive gamut and tone mapping, dynamic contrast, and color management continue to support the unprecedented development of display hardware covering everything from mobile displays to standard monitors, and all the way to large size screens and emerging technologies. The scope of inquiry is also broadened by the desire to match not only color, but complete appearance perceived by the user. This conference provides an opportunity to present, to interact, and to learn about the most recent developments in color imaging and material appearance researches, technologies and applications. Focus of the conference is on color basic research and testing, color image input, dynamic color image output and rendering, color image automation, emphasizing color in context and color in images, and reproduction of images across local and remote devices. The conference covers also software, media, and systems related to color and material appearance. Special attention is given to applications and requirements created by and for multidisciplinary fields involving color and/or vision.
{"title":"Color Imaging XXVIII: Displaying, Processing, Hardcopy, and Applications Conference Overview and Papers Program","authors":"","doi":"10.2352/ei.2023.35.15.color-a15","DOIUrl":"https://doi.org/10.2352/ei.2023.35.15.color-a15","url":null,"abstract":"Abstract Color imaging has historically been treated as a phenomenon sufficiently described by three independent parameters. Recent advances in computational resources and in the understanding of the human aspects are leading to new approaches that extend the purely metrological view of color towards a perceptual approach describing the appearance of objects, documents and displays. Part of this perceptual view is the incorporation of spatial aspects, adaptive color processing based on image content, and the automation of color tasks, to name a few. This dynamic nature applies to all output modalities, including hardcopy devices, but to an even larger extent to soft-copy displays with their even larger options of dynamic processing. Spatially adaptive gamut and tone mapping, dynamic contrast, and color management continue to support the unprecedented development of display hardware covering everything from mobile displays to standard monitors, and all the way to large size screens and emerging technologies. The scope of inquiry is also broadened by the desire to match not only color, but complete appearance perceived by the user. This conference provides an opportunity to present, to interact, and to learn about the most recent developments in color imaging and material appearance researches, technologies and applications. Focus of the conference is on color basic research and testing, color image input, dynamic color image output and rendering, color image automation, emphasizing color in context and color in images, and reproduction of images across local and remote devices. The conference covers also software, media, and systems related to color and material appearance. Special attention is given to applications and requirements created by and for multidisciplinary fields involving color and/or vision.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.8.iqsp-a08
Abstract We live in a visual world. The perceived quality of images is of crucial importance in industrial, medical, and entertainment application environments. Developments in camera sensors, image processing, 3D imaging, display technology, and digital printing are enabling new or enhanced possibilities for creating and conveying visual content that informs or entertains. Wireless networks and mobile devices expand the ways to share imagery and autonomous vehicles bring image processing into new aspects of society. The power of imaging rests directly on the visual quality of the images and the performance of the systems that produce them. As the images are generally intended to be viewed by humans, a deep understanding of human visual perception is key to the effective assessment of image quality.
{"title":"Image Quality and System Performance XX Conference Overview and Papers Program","authors":"","doi":"10.2352/ei.2023.35.8.iqsp-a08","DOIUrl":"https://doi.org/10.2352/ei.2023.35.8.iqsp-a08","url":null,"abstract":"Abstract We live in a visual world. The perceived quality of images is of crucial importance in industrial, medical, and entertainment application environments. Developments in camera sensors, image processing, 3D imaging, display technology, and digital printing are enabling new or enhanced possibilities for creating and conveying visual content that informs or entertains. Wireless networks and mobile devices expand the ways to share imagery and autonomous vehicles bring image processing into new aspects of society. The power of imaging rests directly on the visual quality of the images and the performance of the systems that produce them. As the images are generally intended to be viewed by humans, a deep understanding of human visual perception is key to the effective assessment of image quality.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"74 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.4.mwsf-a04
Abstract The ease of capturing, manipulating, distributing, and consuming digital media (e.g., images, audio, video, graphics, and text) has enabled new applications and brought a number of important security challenges to the forefront. These challenges have prompted significant research and development in the areas of digital watermarking, steganography, data hiding, forensics, deepfakes, media identification, biometrics, and encryption to protect owners’ rights, establish provenance and veracity of content, and to preserve privacy. Research results in these areas has been translated into new paradigms and applications for monetizing media while maintaining ownership rights, and new biometric and forensic identification techniques for novel methods for ensuring privacy. The Media Watermarking, Security, and Forensics Conference is a premier destination for disseminating high-quality, cutting-edge research in these areas. The conference provides an excellent venue for researchers and practitioners to present their innovative work as well as to keep abreast of the latest developments in watermarking, security, and forensics. Early results and fresh ideas are particularly encouraged and supported by the conference review format: only a structured abstract describing the work in progress and preliminary results is initially required and the full paper is requested just before the conference. A strong focus on how research results are applied by industry, in practice, also gives the conference its unique flavor.
{"title":"Media Watermarking, Security, and Forensics 2023 Conference Overview and Papers Program","authors":"","doi":"10.2352/ei.2023.35.4.mwsf-a04","DOIUrl":"https://doi.org/10.2352/ei.2023.35.4.mwsf-a04","url":null,"abstract":"Abstract The ease of capturing, manipulating, distributing, and consuming digital media (e.g., images, audio, video, graphics, and text) has enabled new applications and brought a number of important security challenges to the forefront. These challenges have prompted significant research and development in the areas of digital watermarking, steganography, data hiding, forensics, deepfakes, media identification, biometrics, and encryption to protect owners’ rights, establish provenance and veracity of content, and to preserve privacy. Research results in these areas has been translated into new paradigms and applications for monetizing media while maintaining ownership rights, and new biometric and forensic identification techniques for novel methods for ensuring privacy. The Media Watermarking, Security, and Forensics Conference is a premier destination for disseminating high-quality, cutting-edge research in these areas. The conference provides an excellent venue for researchers and practitioners to present their innovative work as well as to keep abreast of the latest developments in watermarking, security, and forensics. Early results and fresh ideas are particularly encouraged and supported by the conference review format: only a structured abstract describing the work in progress and preliminary results is initially required and the full paper is requested just before the conference. A strong focus on how research results are applied by industry, in practice, also gives the conference its unique flavor.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.1.vda-a01
Abstract The Conference on Visualization and Data Analysis (VDA) 2023 covers all research, development, and application aspects of data visualization and visual analytics. Since the first VDA conference was held in 1994, the annual event has grown steadily into a major venue for visualization researchers and practitioners from around the world to present their work and share their experiences. We invite you to participate by submitting your original research as a full paper, for an oral or interactive (poster) presentation, and attending VDA in the upcoming year.
{"title":"Visualization and Data Analysis 2023 Conference Overview and Papers Program","authors":"","doi":"10.2352/ei.2023.35.1.vda-a01","DOIUrl":"https://doi.org/10.2352/ei.2023.35.1.vda-a01","url":null,"abstract":"Abstract The Conference on Visualization and Data Analysis (VDA) 2023 covers all research, development, and application aspects of data visualization and visual analytics. Since the first VDA conference was held in 1994, the annual event has grown steadily into a major venue for visualization researchers and practitioners from around the world to present their work and share their experiences. We invite you to participate by submitting your original research as a full paper, for an oral or interactive (poster) presentation, and attending VDA in the upcoming year.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.7.image-269
Andrew W. Peng, Jiangpeng He, Fengqing Zhu
Food image classification is the groundwork for image-based dietary assessment, which is the process of monitoring what kinds of food and how much energy is consumed using captured food or eating scene images. Existing deep learning based methods learn the visual representation for food classification based on human annotation of each food image. However, most food images captured from real life are obtained without labels, requiring human annotation to train deep learning based methods. This approach is not feasible for real world deployment due to high costs. To make use of the vast amount of unlabeled images, many existing works focus on unsupervised or self-supervised learning to learn the visual representation directly from unlabeled data. However, none of these existing works focuses on food images, which is more challenging than general objects due to its high inter-class similarity and intra-class variance. In this paper, we focus on two items: the comparison of existing models and the development of an effective self-supervised learning model for food image classification. Specifically, we first compare the performance of existing state-of-the-art self-supervised learning models, including SimSiam, SimCLR, SwAV, BYOL, MoCo, and Rotation Pretext Task on food images. The experiments are conducted on the Food-101 dataset, which contains 101 different classes of foods with 1,000 images in each class. Next, we analyze the unique features of each model and compare their performance on food images to identify the key factors in each model that can help improve the accuracy. Finally, we propose a new model for unsupervised visual representation learning on food images for the classification task.
{"title":"Self-supervised visual representation learning on food images","authors":"Andrew W. Peng, Jiangpeng He, Fengqing Zhu","doi":"10.2352/ei.2023.35.7.image-269","DOIUrl":"https://doi.org/10.2352/ei.2023.35.7.image-269","url":null,"abstract":"Food image classification is the groundwork for image-based dietary assessment, which is the process of monitoring what kinds of food and how much energy is consumed using captured food or eating scene images. Existing deep learning based methods learn the visual representation for food classification based on human annotation of each food image. However, most food images captured from real life are obtained without labels, requiring human annotation to train deep learning based methods. This approach is not feasible for real world deployment due to high costs. To make use of the vast amount of unlabeled images, many existing works focus on unsupervised or self-supervised learning to learn the visual representation directly from unlabeled data. However, none of these existing works focuses on food images, which is more challenging than general objects due to its high inter-class similarity and intra-class variance. In this paper, we focus on two items: the comparison of existing models and the development of an effective self-supervised learning model for food image classification. Specifically, we first compare the performance of existing state-of-the-art self-supervised learning models, including SimSiam, SimCLR, SwAV, BYOL, MoCo, and Rotation Pretext Task on food images. The experiments are conducted on the Food-101 dataset, which contains 101 different classes of foods with 1,000 images in each class. Next, we analyze the unique features of each model and compare their performance on food images to identify the key factors in each model that can help improve the accuracy. Finally, we propose a new model for unsupervised visual representation learning on food images for the classification task.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135693973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.3.mobmu-350
Eberhard Hasche, Oliver Karaschewski, Reiner Creutzburg
With the release of the Apple iPhone 12 pro in 2020, various features were integrated that make it attractive as a recording device for scene-related computer graphics pipelines. The captured Apple RAW images have a much higher dynamic range than the standard 8-bit images. Since a scene-based workflow naturally has an extended dynamic range (HDR), the Apple RAW recordings can be well integrated. Another feature is the Dolby Vision HDR recordings, which are primarily adapted to the respective display of the source device. However, these recordings can also be used in the CG workflow since at least the basic HLG transfer function is integrated. The iPhone12pro's two Laser scanners can produce complex 3D models and textures for the CG pipeline. On the one hand, there is a scanner on the back that is primarily intended for capturing the surroundings for AR purposes. On the other hand, there is another scanner on the front for facial recognition. In addition, external software can read out the scanning data for integration in 3D applications. To correctly integrate the iPhone12pro Apple RAW data into a scene-related workflow, two command-line-based software solutions can be used, among others: dcraw and rawtoaces. Dcraw offers the possibility to export RAW images directly to ACES2065-1. Unfortunately, the modifiers for the four RAW color channels to address the different white points are unavailable. Experimental test series are performed under controlled studio conditions to retrieve these modifier values. Subsequently, these RAW-derived images are imported into computer graphics pipelines of various CG software applications (SideFx Houdini, The Foundry Nuke, Autodesk Maya) with the help of OpenColorIO (OCIO) and ACES. Finally, it will be determined if they can improve the overall color quality. Dolby Vision content can be captured using the native Camera app on an iPhone 12. It captures HDR video using Dolby Vision Profile 8.4, which contains a cross-compatible HLG Rec.2020 base layer and Dolby Vision dynamic metadata. Only the HLG base layer is passed on when exporting the Dolby Vision iPhone video without the corresponding metadata. It is investigated whether the iPhone12 videos transferred this way can increase the quality of the computer graphics pipeline. The 3D Scanner App software controls the two integrated Laser Scanners. In addition, the software provides a large number of export formats. Therefore, integrating the OBJ-3D data into industry-standard software like Maya and Houdini is unproblematic. Unfortunately, the models and the corresponding UV map are more or less machine-readable. So, manually improving the 3D geometry (filling holes, refining the geometry, setting up new topology) is cumbersome and time-consuming. It is investigated if standard techniques like using the ZRemesher in ZBrush, applying Texture- and UV-Projection in Maya, and VEX-snippets in Houdini can assemble these models and textures for manual editing.
随着2020年苹果iPhone 12 pro的发布，各种功能被整合在一起，使其成为与场景相关的计算机图形管道的录制设备。捕获的苹果RAW图像比标准的8位图像具有更高的动态范围。由于基于场景的工作流程自然具有扩展的动态范围(HDR)，因此Apple RAW录制可以很好地集成。另一个特点是杜比视界HDR录音，这主要是适应各自的显示源设备。然而，这些录音也可以在CG工作流程中使用，因为至少基本的HLG传递函数是集成的。iPhone12pro的两个激光扫描仪可以为CG管道生成复杂的3D模型和纹理。一方面，背面有一个扫描仪，主要用于捕捉AR目的的周围环境。另一方面，前面还有一个用于面部识别的扫描仪。此外，外部软件可以读取扫描数据，以便集成到3D应用程序中。要将iPhone12pro Apple RAW数据正确集成到与场景相关的工作流中，可以使用两种基于命令行的软件解决方案:draw和rawtoaces。draw提供了直接将RAW图像导出到ACES2065-1的可能性。不幸的是，用于处理不同白点的四个RAW颜色通道的修饰符不可用。实验测试系列在受控的工作室条件下进行，以检索这些修改值。随后，这些原始衍生的图像被导入到计算机图形管道的各种CG软件应用程序(SideFx胡迪尼，铸造核，Autodesk Maya)与OpenColorIO (OCIO)和ACES的帮助下。最后，它将确定他们是否可以提高整体色彩质量。杜比视界的内容可以使用iPhone 12上的原生相机应用程序捕获。它使用杜比视界配置文件8.4捕获HDR视频，其中包含交叉兼容的HLG Rec.2020基础层和杜比视界动态元数据。在没有相应元数据的情况下导出杜比视界iPhone视频时，只传递HLG基础层。研究了以这种方式传输的iPhone12视频是否能提高计算机图形流水线的质量。3D扫描仪应用软件控制两个集成的激光扫描仪。此外，该软件提供了大量的导出格式。因此，整合OBJ-3D数据到行业标准的软件，如玛雅和胡迪尼是没有问题的。不幸的是，模型和相应的UV图或多或少是机器可读的。因此，手动改进3D几何形状(填充孔，精炼几何形状，设置新的拓扑结构)既麻烦又耗时。它是调查如果标准的技术，如使用ZRemesher在ZBrush，在玛雅应用纹理和紫外线投影，并在胡迪尼vex片段可以组装这些模型和纹理手动编辑。
{"title":"iPhone12 imagery in scene-referred computer graphics pipelines","authors":"Eberhard Hasche, Oliver Karaschewski, Reiner Creutzburg","doi":"10.2352/ei.2023.35.3.mobmu-350","DOIUrl":"https://doi.org/10.2352/ei.2023.35.3.mobmu-350","url":null,"abstract":"With the release of the Apple iPhone 12 pro in 2020, various features were integrated that make it attractive as a recording device for scene-related computer graphics pipelines. The captured Apple RAW images have a much higher dynamic range than the standard 8-bit images. Since a scene-based workflow naturally has an extended dynamic range (HDR), the Apple RAW recordings can be well integrated. Another feature is the Dolby Vision HDR recordings, which are primarily adapted to the respective display of the source device. However, these recordings can also be used in the CG workflow since at least the basic HLG transfer function is integrated. The iPhone12pro's two Laser scanners can produce complex 3D models and textures for the CG pipeline. On the one hand, there is a scanner on the back that is primarily intended for capturing the surroundings for AR purposes. On the other hand, there is another scanner on the front for facial recognition. In addition, external software can read out the scanning data for integration in 3D applications. To correctly integrate the iPhone12pro Apple RAW data into a scene-related workflow, two command-line-based software solutions can be used, among others: dcraw and rawtoaces. Dcraw offers the possibility to export RAW images directly to ACES2065-1. Unfortunately, the modifiers for the four RAW color channels to address the different white points are unavailable. Experimental test series are performed under controlled studio conditions to retrieve these modifier values. Subsequently, these RAW-derived images are imported into computer graphics pipelines of various CG software applications (SideFx Houdini, The Foundry Nuke, Autodesk Maya) with the help of OpenColorIO (OCIO) and ACES. Finally, it will be determined if they can improve the overall color quality. Dolby Vision content can be captured using the native Camera app on an iPhone 12. It captures HDR video using Dolby Vision Profile 8.4, which contains a cross-compatible HLG Rec.2020 base layer and Dolby Vision dynamic metadata. Only the HLG base layer is passed on when exporting the Dolby Vision iPhone video without the corresponding metadata. It is investigated whether the iPhone12 videos transferred this way can increase the quality of the computer graphics pipeline. The 3D Scanner App software controls the two integrated Laser Scanners. In addition, the software provides a large number of export formats. Therefore, integrating the OBJ-3D data into industry-standard software like Maya and Houdini is unproblematic. Unfortunately, the models and the corresponding UV map are more or less machine-readable. So, manually improving the 3D geometry (filling holes, refining the geometry, setting up new topology) is cumbersome and time-consuming. It is investigated if standard techniques like using the ZRemesher in ZBrush, applying Texture- and UV-Projection in Maya, and VEX-snippets in Houdini can assemble these models and textures for manual editing.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"853 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135694709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.3.mobmu-358
Yang Cai, Mel Siegel
Incident Command Dashboard (ICD) plays an essential role in Emergency Support Functions (ESF). They are centralized with a massive amount of live data. In this project, we explore a decentralized mobile incident commanding dashboard (MIC-D) with an improved mobile augmented reality (AR) user interface (UI) that can access and display multimodal live IoT data streams in phones, tablets, and inexpensive HUDs on the first responder’s helmets. The new platform is designed to work in the field and to share live data streams among team members. It also enables users to view the 3D LiDAR scan data on the location, live thermal video data, and vital sign data on the 3D map. We have built a virtual medical helicopter communication center and tested the launchpad on fire and remote fire extinguishing scenarios. We have also tested the wildfire prevention scenario “Cold Trailing” in the outdoor environment.
{"title":"Mobile incident command dashboard (MIC-D)","authors":"Yang Cai, Mel Siegel","doi":"10.2352/ei.2023.35.3.mobmu-358","DOIUrl":"https://doi.org/10.2352/ei.2023.35.3.mobmu-358","url":null,"abstract":"Incident Command Dashboard (ICD) plays an essential role in Emergency Support Functions (ESF). They are centralized with a massive amount of live data. In this project, we explore a decentralized mobile incident commanding dashboard (MIC-D) with an improved mobile augmented reality (AR) user interface (UI) that can access and display multimodal live IoT data streams in phones, tablets, and inexpensive HUDs on the first responder’s helmets. The new platform is designed to work in the field and to share live data streams among team members. It also enables users to view the 3D LiDAR scan data on the location, live thermal video data, and vital sign data on the 3D map. We have built a virtual medical helicopter communication center and tested the launchpad on fire and remote fire extinguishing scenarios. We have also tested the wildfire prevention scenario “Cold Trailing” in the outdoor environment.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135694710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.3.mobmu-357
Pranesh Kumar Narasimhan, Chinmay Bhosale, Muhammad Hasban Pervez, Najiba Zainab Naqvi, Mert Ilhan Ecevit, Klaus Schwarz, Reiner Creutzburg
Open Source Intelligence (OSINT) has come a long way, and it is still developing ideas, and lots of investigations are yet to happen in the near future. The main essential requirement for all the OSINT investigations is the information that is valuable data from a good source. This paper discusses various tools and methodologies related to Facebook data collection and analyzes part of the collected data. At the end of the paper, the reader will get a deep and clear insight into the available techniques, tools, and descriptions about tools that are present to scrape the data out of the Facebook platform and the types of investigations and analyses that the gathered data can do.
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Pub Date : 2023-01-16DOI: 10.2352/ei.2023.35.16.avm-a16
Abstract Advancements in sensing, computing, image processing, and computer vision technologies are enabling unprecedented growth and interest in autonomous vehicles and intelligent machines, from self-driving cars to unmanned drones, to personal service robots. These new capabilities have the potential to fundamentally change the way people live, work, commute, and connect with each other, and will undoubtedly provoke entirely new applications and commercial opportunities for generations to come. The main focus of AVM is perception. This begins with sensing. While imaging continues to be an essential emphasis in all EI conferences, AVM also embraces other sensing modalities important to autonomous navigation, including radar, LiDAR, and time of flight. Realization of autonomous systems also includes purpose-built processors, e.g., ISPs, vision processors, DNN accelerators, as well core image processing and computer vision algorithms, system design and architecture, simulation, and image/video quality. AVM topics are at the intersection of these multi-disciplinary areas. AVM is the Perception Conference that bridges the imaging and vision communities, connecting the dots for the entire software and hardware stack for perception, helping people design globally optimized algorithms, processors, and systems for intelligent “eyes” for vehicles and machines.
{"title":"Autonomous Vehicles and Machines 2023 Conference Overview and Papers Program","authors":"","doi":"10.2352/ei.2023.35.16.avm-a16","DOIUrl":"https://doi.org/10.2352/ei.2023.35.16.avm-a16","url":null,"abstract":"Abstract Advancements in sensing, computing, image processing, and computer vision technologies are enabling unprecedented growth and interest in autonomous vehicles and intelligent machines, from self-driving cars to unmanned drones, to personal service robots. These new capabilities have the potential to fundamentally change the way people live, work, commute, and connect with each other, and will undoubtedly provoke entirely new applications and commercial opportunities for generations to come. The main focus of AVM is perception. This begins with sensing. While imaging continues to be an essential emphasis in all EI conferences, AVM also embraces other sensing modalities important to autonomous navigation, including radar, LiDAR, and time of flight. Realization of autonomous systems also includes purpose-built processors, e.g., ISPs, vision processors, DNN accelerators, as well core image processing and computer vision algorithms, system design and architecture, simulation, and image/video quality. AVM topics are at the intersection of these multi-disciplinary areas. AVM is the Perception Conference that bridges the imaging and vision communities, connecting the dots for the entire software and hardware stack for perception, helping people design globally optimized algorithms, processors, and systems for intelligent “eyes” for vehicles and machines.","PeriodicalId":73514,"journal":{"name":"IS&T International Symposium on Electronic Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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