Real-World Applications and Techniques of Computer Vision in Behavioral Signal Sensing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 1808

Special Issue Editors

INRIA, Sophia Antipolis, 2004 route des Lucioles, BP 93, 06902 Sophia Antipolis, France
Interests: video understanding; action recognition and detection; gesture recognition; object detection; satellite imaging; remote sensing; image generation
Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Interests: image/video signal processing; pattern recognition; computer vision; deep learning; artificial intelligence
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Special Issue Information

Dear Colleagues,

We are delighted to announce and invite you to submit your papers for the Special Issue “Real-World Applications and Techniques of Computer Vision in Behavioral Signal Sensing”.

Recently, many advances have been achieved in the development of various sensors, such as visible light, near-infrared, thermal camera sensors, as well as intelligent devices, such as smart wearables, robots, and intelligent vehicles. These sensors and devices, as well as their combination, have been used in the development of various applications in computer vision, biometrics, video surveillance, medical image analysis, and computer-aided diagnosis. However, at present, modern smart devices and applications are not able to provide personalized services tailored to the specific needs of users due to limited interaction with the user and insufficient understanding of their situation. In recent years, many efforts have been made to use these tools, especially with the development of computer vision-based applications to achieve these goals, and significant progress has been achieved. Despite these advances, there is still much room for improvement in these programs to transform them from purely laboratory and controlled tools to real-world applications. The main purpose of this Special Issue is to invite high-quality and advanced research and applied articles that examine these challenging issues in computer vision for a more accurate analysis of human behavior. The presented articles can range from recognizing gestures and activities using sensory data to interpreting a higher level of this information for cognitive and social analysis.

Topics of interest for this Special Issue include but are not limited to:

  • Action/activity recognition and detection;
  • Cognitive models for interpretation;
  • Deep learning for visual understanding;
  • Supervised, semi-supervised, and unsupervised learning methods to analyze human behavior;
  • Face and gesture recognition;
  • Analysis of human behavior to understand physiological/cognitive disorders;

Analysis of interactive behavior, from human-human, human-object, and human-computer to human-robot interactions;

  • Multimodal behavior understanding, using different sensory input, e.g., audio, wearables;
  • Video surveillance and event detection;
  • Analysis of social impact of human behavior;
  • Datasets and applications in healthcare, education, etc.

Dr. Negin Farhood
Prof. Dr. Byung-Gyu Kim
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

  • action/activity recognition and detection
  • cognitive models for interpretation
  • deep learning for visual understanding
  • supervised, semi-supervised, and unsupervised learning methods to analyze human behavior
  • face and gesture recognition
  • analysis of human behavior to understand physiological/cognitive disorders
  • analysis of interactive behavior, from human-human, human-object, and human-computer to human-robot interactions
  • multimodal behavior understanding, using different sensory input, e.g., audio, wearables
  • video surveillance and event detection
  • analysis of social impact of human behavior
  • datasets and applications in healthcare, education, etc

Published Papers (1 paper)

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Research

13 pages, 3609 KiB  
Article
Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
by Haoyang Kang, Lu Leng and Byung-Gyu Kim
Appl. Sci. 2022, 12(19), 9629; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199629 - 25 Sep 2022
Cited by 4 | Viewed by 1262
Abstract
Image-based data hiding methods have been used in the development of various applications in computer vision. At present, there are two main types of data hiding based on secret sharing, namely dual-image data hiding and multi-image data hiding. Dual-image data hiding is a [...] Read more.
Image-based data hiding methods have been used in the development of various applications in computer vision. At present, there are two main types of data hiding based on secret sharing, namely dual-image data hiding and multi-image data hiding. Dual-image data hiding is a kind of secret sharing-based data hiding in the extreme case. During the image transmission and storage process, the two shadow images are visually highly similar. Multi-image data hiding disassembles the cover image into multiple meaningless secret images through secret sharing. Both of the above two methods can easily attract attackers’ attention and cannot effectively guarantee the security of the secret message. In this paper, through the Shamir threshold scheme for secret sharing, the secret message is disassembled into multiple subsecrets that are embedded in the smooth blocks of multiple different images, by substituting the bitmap of block truncation coding. Thus, the shortcomings of the above two data hiding methods are effectively avoided. The proposed method embeds the secret messages in the compressed images, so it satisfactorily balances the visual quality and the embedding capacity. In our method, the shadow images make sense while they are not visually similar. The compression ratio is four, so the embedding capacity of our method has an obvious advantage under the same storage space. Full article
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