Special Issue "Advances in Body Scanning"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "AI in Imaging".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 2162

Special Issue Editors

National Research Council Canada, 1200 Montréal Road, Ottawa, ON K1A 0R6, Canada
Interests: machine learning; computer vision; deep learning; signal processing; geometry processing
Special Issues, Collections and Topics in MDPI journals
Dr. Chang Shu
E-Mail Website
Guest Editor
Digital Technologies Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
Interests: digital human modeling; geometry processing; computer vision; computer graphics

Special Issue Information

Dear Colleagues,

Recent years have witnessed rapid advancements in body scanning technologies, which cover a pipeline from imaging, modeling, and processing to analysis and visualization. Along this pipeline, there has been exciting research progress in active and passive scanning technologies for the human body, leveraging imaging sensors at a lower cost but with a higher resolution, speed, and accuracy. The advanced capability of capturing the human body in 3D and 4D has led to a wide range of applications, including anthropometry, ergonomics, apparel, health, sports, entertainment, and communications.

The recent research advancement in artificial intelligence (AI) has permeated through each step of the body scanning pipeline. In particular, machine learning and deep learning have contributed to automating the scanning and processing steps, e.g., registration, landmark localization, and body measurement. This has led to an easier adoption of body scanning technologies and consequently new insights into the human body shape for innovative applications.

The Journal of Imaging invites you to submit to a Special Issue on “Advances in Body Scanning”. The aim of this Special Issue is to solicit research, experimental and review papers on acquisition and formation, processing and analysis, understanding and visualization, as well as emerging domains and applications. We request contributions representing body scanning and the benefit of AI to the pipeline.

Dr. Pengcheng Xi
Dr. Chang Shu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • body scanning
  • imaging
  • sensing
  • active scanning
  • passive scanning
  • time-of-flight sensing
  • modeling
  • data processing
  • automation
  • artificial intelligence
  • deep learning

Published Papers (1 paper)

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Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud
J. Imaging 2022, 8(6), 168; https://doi.org/10.3390/jimaging8060168 - 13 Jun 2022
Viewed by 1587
Estimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and [...] Read more.
Estimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain. Full article
(This article belongs to the Special Issue Advances in Body Scanning)
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