Special Issue "Symmetry in Artificial Visual Perception and Its Application"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer and Engineer Science and Symmetry".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editor

Prof. Dr. Janghoon Yang
Website
Guest Editor
Department of New Media, Seoul Media Institute of Technology, Seoul 07590, Korea
Interests: artificial intelligence; control theory; neuroscience; affective computing; reinforcement learning; intervention for special education; and wireless system

Special Issue Information

Dear colleagues,

Symmetry in human neural structure has a great impact on perception. People tend to perceive an object based on salient visual regularity. The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task. With the enormous advancement in artificial intelligence, artificial intelligence in visual perception does better than humans in some specific tasks. However, even the tasks which are known to be highly successful using artificial intelligence often fail in the presence of unexpected uncertainty. Even though many researches in artificial visual perception have studied object classification or segmentation, artificial visual perception is starting to make progress in cyber-physical systems with the convergence of other fields such as control or robotics. In the future, a virtual human with artificial vision capabilities beyond the human biological vision system is expected. The operation of the virtual human is expected to be realized through interaction among many different coupled subsystems. This implies that the task of visual perception is likely to be modeled in conjunction with other sensory or motor systems. Recent advances in multimodal deep learning show a glimpse of realizing the virtual human. The recent advances in artificial visual perception are resulting in many fascinating applications in various fields such as autonomous robots, human–computer interfaces, autonomous driving, smart factories, medical systems, national security, and even the fashion industry.

This Special Issue aims to highlight and advance contemporary research on artificial visual perception and its application to various fields. Theory can be developed further from exploiting theory used in different fields, while application development can be improved by understanding its principle deeply. We invite contributions of both original research and reviews of research that organize the recent research results in a unified and systematic way.

Suggested topics include but are not limited to:

  • Artificial visual perception with partial information.
  • Artificial visual perception for sensorimotor control.
  • Visual perceptual learning.
  • Artificial specificity and plasticity.
  • Artificial vision in autonomous virtual humans.
  • Multimodal deep learning.
  • Multimodal reinforcement learning.
  • Multimodal association learning.
  • Collaborative artificial visual perception.
  • Generative adversarial networks for visual perception. 

Prof. Dr. Janghoon Yang
Guest Editor

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 papers will be 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. Symmetry 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 1400 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

  • visual perception
  • artificial intelligence
  • multimodal learning
  • virtual human
  • computer vision
  • vision-based control

Published Papers (2 papers)

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Research

Open AccessArticle
Braille Recognition for Reducing Asymmetric Communication between the Blind and Non-Blind
Symmetry 2020, 12(7), 1069; https://doi.org/10.3390/sym12071069 - 30 Jun 2020
Abstract
Assistive braille technology has existed for many years with the purpose of aiding the blind in performing common tasks such as reading, writing, and communicating with others. Such technologies are aimed towards helping those who are visually impaired to better adapt to the [...] Read more.
Assistive braille technology has existed for many years with the purpose of aiding the blind in performing common tasks such as reading, writing, and communicating with others. Such technologies are aimed towards helping those who are visually impaired to better adapt to the visual world. However, an obvious gap exists in current technology when it comes to symmetric two-way communication between the blind and non-blind, as little technology allows non-blind individuals to understand the braille system. This research presents a novel approach to convert images of braille into English text by employing a convolutional neural network (CNN) model and a ratio character segmentation algorithm (RCSA). Further, a new dataset was constructed, containing a total of 26,724 labeled braille images, which consists of 37 braille symbols that correspond to 71 different English characters, including the alphabet, punctuation, and numbers. The performance of the CNN model yielded a prediction accuracy of 98.73% on the test set. The functionality performance of this artificial intelligence (AI) based recognition system could be tested through accessible user interfaces in the future. Full article
(This article belongs to the Special Issue Symmetry in Artificial Visual Perception and Its Application)
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Open AccessArticle
Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network
Symmetry 2020, 12(6), 1056; https://doi.org/10.3390/sym12061056 - 25 Jun 2020
Abstract
Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence [...] Read more.
Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research. Full article
(This article belongs to the Special Issue Symmetry in Artificial Visual Perception and Its Application)
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