IEEE BITS the Information Theory Magazine最新文献_第2页

Information Processing Challenges at the National Archives 国家档案馆的信息处理挑战

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3186293

J. Bunn

引用次数: 0

Support for AI Research at the Arts and Humanities Research Council 艺术与人文研究委员会对人工智能研究的支持

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3200641

Deborah Abramson Kroll

引用次数: 0

Image Processing Perspectives of X-Ray Fluorescence Data in Cultural Heritage Sciences x射线荧光数据在文物科学中的图像处理前景

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3197100

Henry H. Chopp, A. McGeachy, M. Alfeld, O. Cossairt, M. Walton, A. Katsaggelos

{"title":"Image Processing Perspectives of X-Ray Fluorescence Data in Cultural Heritage Sciences","authors":"Henry H. Chopp, A. McGeachy, M. Alfeld, O. Cossairt, M. Walton, A. Katsaggelos","doi":"10.1109/MBITS.2022.3197100","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3197100","url":null,"abstract":"X-ray fluorescence (XRF) analysis of art objects has rapidly gained popularity since the late 2000s due to its increased accessibility to scientists. This introduced an imaging component whereby the XRF image volume provides clues as to which chemical elements are present and where they are located spatially in the object. However, as is the nature of collecting measurements, there are limitations preventing perfect acquisition; e.g, spatial resolution, signal-to-noise ratio, etc. The field of image processing, in part, aims to overcome these limitations. Image processing applications in XRF imaging are only just starting to arise due to the increased interest and availability in XRF analysis. In this article, we aim to reach readers in XRF imaging or image processing in an effort to call for further research in the field. We review the basics of XRF imaging and analysis that is tailored for those unfamiliar with this imaging modality. We then delve into various publications of image processing methods as applied to XRF data. Throughout this article, we examine (and opine on) the XRF field through a lens of the image processing field.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"136 32","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113940190","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}

引用次数: 1

Image Processing and the Analysis of Paintings—The Case of Serbian Baroque Icons 图像处理与绘画分析——以塞尔维亚巴洛克绘画为例

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3187947

D. K. Crkvenjakov

引用次数: 0

Forecasting Environmental Conditions to Foster Climate Resilience in Heritage 预测环境条件以增强遗产的气候适应能力

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3196332

Bhavesh Shah, Emily R Long

引用次数: 0

Document Image Understanding: Computational Image Processing in the Cultural Heritage Sector 文献图像理解:文化遗产领域的计算图像处理

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3199678

Tan Lu, A. Dooms

{"title":"Document Image Understanding: Computational Image Processing in the Cultural Heritage Sector","authors":"Tan Lu, A. Dooms","doi":"10.1109/MBITS.2022.3199678","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3199678","url":null,"abstract":"Textual documents, such as manuscripts and historical newspapers, make up an important part of our cultural heritage. Massive digitization projects have been conducted across the globe for a better preservation of, and for providing easier access to such, often vulnerable, documents. These digital counterparts also allow to unlock the rich information contained inside and across them thanks to various types of computational models for document image understanding. In this article, we will shed a light on the document image processing pipeline, from scan to information extraction. As it turns out, human perceptual-driven algorithms are among the most powerful approaches for generic document image understanding, required to deal with a myriad of layouts. In this context, we will in particular explain Gestalt visioning and the linked concept of text homogeneity that allows for enhanced layout analysis and even damage recognition, especially relevant in a cultural heritage setting. We conclude with a recent promising development, namely joint visual and language processing, that will take document image understanding to the next level in the future.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130832708","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}

引用次数: 1

Reducing Bias in AI-Based Analysis of Visual Artworks 减少基于人工智能的视觉艺术品分析中的偏差

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3197102

Zhuomin Zhang, Jia Li, David G. Stork, Elizabeth C. Mansfield, John Russell, Catherine Adams, James Ze Wang

{"title":"Reducing Bias in AI-Based Analysis of Visual Artworks","authors":"Zhuomin Zhang, Jia Li, David G. Stork, Elizabeth C. Mansfield, John Russell, Catherine Adams, James Ze Wang","doi":"10.1109/MBITS.2022.3197102","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3197102","url":null,"abstract":"Empirical research in science and the humanities is vulnerable to bias which, by definition, implies incorrect or misleading findings. Artificial intelligence-based analysis of visual artworks is vulnerable to bias in ways specific to the domain. Works of art belong to a distinct cultural category that often prioritizes such characteristics as hand-craftsmanship, uniqueness, originality, and imaginative content; works of art are also responsive to diverse social and cultural contexts. Ascertaining which features of an artwork can be rightly ascribed to an objective “truth,” without which the concept of bias is not even relevant, is itself challenging. Incorporating expert knowledge into machine learning applications can help reduce bias in final estimates. We review several sources of bias that can occur across different stages of AI-based analysis, protocols, and best practices for reducing bias, and approaches to measuring these biases. This systematic investigation of various types of bias can help researchers better understand bias, become aware of practical solutions, and ultimately cultivate the prudent adoption of AI-based approaches to artwork analysis.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126149461","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}

引用次数: 3

Revealing and Reconstructing Hidden or Lost Features in Art Investigation 揭示和重建艺术调查中隐藏或丢失的特征

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3207125

B. Sober, S. Bucklow, Nathan Daly, I. Daubechies, P. Dragotti, C. Higgitt, Jun-Jie Huang, A. Pižurica, Wei Pu, Suzanne Reynolds, Miguel R. D. Rodrigues, C. Schönlieb, Su Yan

{"title":"Revealing and Reconstructing Hidden or Lost Features in Art Investigation","authors":"B. Sober, S. Bucklow, Nathan Daly, I. Daubechies, P. Dragotti, C. Higgitt, Jun-Jie Huang, A. Pižurica, Wei Pu, Suzanne Reynolds, Miguel R. D. Rodrigues, C. Schönlieb, Su Yan","doi":"10.1109/MBITS.2022.3207125","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3207125","url":null,"abstract":"In recent decades, cultural heritage research—and in particular art investigation—has been undergoing a digital revolution. This is due both to improvements in the digitization and the acquisition of artifact’s images generated using traditional 2-D imaging methods as well as the growing adoption of a range of more recently introduced spectroscopic imaging techniques. A number of these imaging modalities use wavelengths of electromagnetic radiation that can penetrate surface layers thus yielding information from hidden features noninvasively. Different techniques are often used in combination to provide evidence of construction, condition, and past treatment. These can also be used to characterize the materials used, how they were combined, and map their distribution, giving insight into an artist’s working method and the means to understand changes that have occurred over time. This wealth of data calls for the development of algorithmic approaches in order to handle and fully explore and interpret it. The questions one seeks to answer are in some cases sufficiently different from those addressed in other fields that no existing off-the-shelf approaches can be applied. In this article, we discuss a few of the algorithmic challenges that arise in art investigation and conservation using modern imaging techniques.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957596","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}

引用次数: 2

Machine Learning in the Eyes of a Painting Conservator 绘画修复师眼中的机器学习

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3205876

Bart Devolder

引用次数: 0

(Re)discovering Laws of Music Theory Using Information Lattice Learning (二)利用信息点阵学习发现音乐理论规律

IEEE BITS the Information Theory Magazine Pub Date : 2022-10-01 DOI: 10.1109/MBITS.2022.3205288

Haizi Yu, L. Varshney, Heinrich Taube, James A. Evans

引用次数: 1