Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.7
Journals
Symmetry
Special Issues
New Trends in Symmetry/Asymmetry of Image Processing and Computer Vision
share announcement

New Trends in Symmetry/Asymmetry of Image Processing and Computer Vision

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1768

Special Issue Editors

Dr. Yuanjie Shao

E-Mail Website
Guest Editor
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: computer vision; image processing; few-shot learning
Prof. Dr. Changxin Gao

E-Mail Website
Guest Editor
Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: computer vision; image processing; video analysis

Special Issue Information

Dear Colleagues,

We are delighted to announce the launch of a Special Issue on "New Trends in Symmetry/Asymmetry of Image Processing and Computer Vision". The field of image processing and computer vision has witnessed remarkable advancements in recent years, reshaping the visual relationship between humans and machines. As artificial vision continues to evolve, it permeates various aspects of our lives, revolutionizing industries and enhancing our understanding and interaction with the world. Visual signal processing plays a pivotal role in diverse domains, ranging from manufacturing, medicine, defense, and security to autonomous navigation, robotics, social interaction, and the automotive industry. The synergy of hardware advancements in computers, cameras, portable devices, robots, and graphic cards, combined with increasingly powerful machine learning algorithms, enables us to push the boundaries of what is attainable and sets the stage for future breakthroughs. This Special Issue aims to explore the profound impact of symmetry and asymmetry in image processing and computer vision, shedding light on emerging trends and cutting-edge research. We invite researchers from the fields of machine learning and computer vision to contribute their innovative works in the following areas:

  • Pattern classification and clustering analysis;
  • Object detection, tracking, and recognition;
  • Machine learning;
  • Action recognition;
  • Neural networks and deep learning;
  • Multimedia analysis and reasoning;
  • Feature extraction and feature selection;
  • Medical image processing and analysis;
  • Fundamentals of computer vision;
  • Biometric recognition;
  • Low-level visual understanding, image processing;
  • Remote sensing image interpretation;
  • 3D vision and reconstruction;
  • Optimization and learning methods;
  • Computational photography, sensing, and display technology;
  • Multimodal information processing;
  • Document analysis and recognition;
  • Video analysis and understanding;
  • Character recognition;
  • Visual applications and systems;
  • Face recognition and pose estimation;
  • Vision problems in robotics and autonomous driving.

We encourage researchers to submit their original research articles, reviews, or perspectives that explore the role of symmetry and asymmetry in image processing and computer vision. This Special Issue provides an opportunity to share novel insights, methodologies, and applications, shaping the future of the field.

Dr. Yuanjie Shao
Prof. Dr. Changxin Gao
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. 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 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

  • pattern classification and clustering analysis
  • object detection, tracking, and recognition
  • machine learning
  • action recognition
  • neural networks and deep learning
  • multimedia analysis and reasoning
  • feature extraction and feature selection
  • medical image processing and analysis
  • fundamentals of computer vision
  • biometric recognition
  • low-level visual understanding, image processing
  • remote sensing image interpretation
  • 3D vision and reconstruction
  • optimization and learning methods
  • computational photography, sensing, and display technology
  • multimodal information processing
  • document analysis and recognition
  • video analysis and understanding
  • character recognition
  • visual applications and systems
  • face recognition and pose estimation
  • vision problems in robotics and autonomous driving
  • symmetry

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

33 pages, 17354 KiB  
Article
Index for Quantifying ‘Order’ in Three-Dimensional Shapes
by Takahiro Shimizu, Masaya Okamoto, Yuto Ieda and Takeo Kato
Symmetry 2024, 16(4), 381; https://doi.org/10.3390/sym16040381 - 22 Mar 2024
Viewed by 669
Abstract
In this study, we focused on assessing the symmetry of shapes and quantifying an index of ‘order’ in three-dimensional shapes using curvature, which is important in product design. Specifically, the target three-dimensional shape was divided into two segments, and the Jensen–Shannon distance was [...] Read more.
In this study, we focused on assessing the symmetry of shapes and quantifying an index of ‘order’ in three-dimensional shapes using curvature, which is important in product design. Specifically, the target three-dimensional shape was divided into two segments, and the Jensen–Shannon distance was calculated for the distribution of the Casorati curvatures in both segments to determine the similarity between them. This was proposed as an indicator of the ‘order’ exhibited by the shape. To validate the effectiveness of the proposed index, sensory evaluation experiments were conducted on three shapes: extruded, rotated, and vase. For the rotated shape, the coefficient of determination between the proposed index and the sensory evaluation value of ‘order’ on a 5-point Likert scale was found to be less than 0.1. The reason for the poor correlation coefficient of determination may be attributed to the bias in human perception, where individuals tend to perceive mirror symmetry with respect to the plane that includes the vertical axis when recognizing the mirror symmetry of an object. In contrast, for the extruded and vase shapes, the coefficients of determination were 0.36 and 0.66, respectively, supporting the validity of the proposed index. Nonetheless, the coefficient of determination decreased slightly for familiar extruded shapes and asymmetric vase shapes. In future research, our aim is to quantify ‘aesthetic preference’ by combining the ‘order’ and ‘complexity’ indexes. Full article
(This article belongs to the Special Issue New Trends in Symmetry/Asymmetry of Image Processing and Computer Vision)
►▼ Show Figures

Figure 1

19 pages, 6665 KiB  
Article
PointNAC: Copula-Based Point Cloud Semantic Segmentation Network
by Chunyuan Deng, Ruixing Chen, Wuyang Tang, Hexuan Chu, Gang Xu, Yue Cui and Zhenyun Peng
Symmetry 2023, 15(11), 2021; https://doi.org/10.3390/sym15112021 - 06 Nov 2023
Cited by 1 | Viewed by 790
Abstract
Three-dimensional point cloud data generally contain complex scene information and diversified category structures. Existing point cloud semantic segmentation networks tend to learn feature information between sampled center points and their neighboring points, while ignoring the scale and structural information of the spatial context [...] Read more.
Three-dimensional point cloud data generally contain complex scene information and diversified category structures. Existing point cloud semantic segmentation networks tend to learn feature information between sampled center points and their neighboring points, while ignoring the scale and structural information of the spatial context of the sampled center points. To address these issues, this paper introduces PointNAC (PointNet based on normal vector and attention copula feature enhancement), a network designed for point cloud semantic segmentation in large-scale complex scenes, which consists of the following two main modules: (1) The local stereoscopic feature-encoding module: this feature-encoding process incorporates distance, normal vectors, and angles calculated based on the cosine theorem, enabling the network to learn not only the spatial positional information of the point cloud but also the spatial scale and geometric structure; and (2) the copula-based similarity feature enhancement module. Based on the stereoscopic feature information, this module analyzes the correlation among points in the local neighborhood. It enhances the features of positively correlated points while leaving the features of negatively correlated points unchanged. By combining these enhancements, it effectively enhances the feature saliency within the same class and the feature distinctiveness between different classes. The experimental results show that PointNAC achieved an overall accuracy (OA) of 90.9% and a mean intersection over union (MIoU) of 67.4% on the S3DIS dataset. And on the Vaihingen dataset, PointNAC achieved an overall accuracy (OA) of 85.9% and an average F1 score of 70.6%. Compared to the segmentation results of other network models on public datasets, our algorithm demonstrates good generalization and segmentation capabilities. Full article
(This article belongs to the Special Issue New Trends in Symmetry/Asymmetry of Image Processing and Computer Vision)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop