Advances in Computer Vision and Semantic Segmentation, 2nd Edition

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 294

Special Issue Editors


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Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea SA2 8PP, UK
Interests: visual analytics; machine learning; digital geometry processing; pattern recognition and vision; multi-dimensional data analysis; information retrieval and indexing
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Department of Computer Science, Durham University, Durham DH1 3LE, UK
Interests: computer graphics; geometric modelling and processing; collaborative virtual environments; visual aesthetics; educational techno
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Guest Editor
Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea SA2 8PP, UK
Interests: computer vision; image processing; machine learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science, University of Birmingham, Edgbaston Birmingham B15 2TT, UK
Interests: computer vision; machine learning; medical imaging
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Special Issue Information

Dear Colleagues,

Semantic segmentation is a core problem for many applications, such as image manipulation, facial segmentation, healthcare, security and surveillance, medical imaging and diagnosis, aerial and satellite image surveying and processing, city 3D modeling, and scene understanding. It is also an important building block in more complex systems, including autonomous cars, drones, and human-centric robots.

The recent advances in deep learning techniques (e.g., CNN, FCN, UNet, graph LSTM, spatial pyramid, attentional modelling, and transformer) have fostered many great improvements in semantic segmentation, not only improving speed and accuracy but also inspiring other areas such as instance and panoptic segmentation.

This Special Issue welcomes research papers on semantic segmentation (and its broader areas, including instance and panoptic segmentation) and advanced computer vision applications relating to semantic segmentation. It covers possible research and application areas, including multimodal segmentation (e.g., referring to image segmentation), salient object detection and segmentation, 3D (point cloud and meshes) semantic segmentation, video semantic segmentation, and many others. Papers focusing on new data (e.g., hyper-spectral data, MRI CT, point cloud, and meshes) and new deep architectures, techniques, and learning strategies (e.g., weakly supervised/unsupervised semantic segmentation, zero/few-shot learning, domain adaptation, real-time processing, contextual information, transfer learning, reinforcement learning, and the critical issue of acquiring training data) are all welcome.

Dr. Gary KL Tam
Dr. Frederick W. B. Li
Prof. Dr. Xianghua Xie
Dr. Jianbo Jiao
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

  • semantic segmentation
  • instance segmentation
  • panoptic segmentation
  • multimodal segmentation
  • referring image segmentation
  • salient object detection and segmentation
  • 3D semantic segmentation
  • video semantic segmentation
  • weakly supervised semantic segmentation
  • unsupervised semantic segmentation
  • advanced machine learning segmentation techniques
  • medical semantic segmentation

Published Papers (1 paper)

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Research

14 pages, 2038 KiB  
Article
An Efficient Semantic Segmentation Method for Remote-Sensing Imagery Using Improved Coordinate Attention
by Yan Huo, Shuang Gang, Liang Dong and Chao Guan
Appl. Sci. 2024, 14(10), 4075; https://doi.org/10.3390/app14104075 (registering DOI) - 10 May 2024
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Abstract
Semantic segmentation stands as a prominent domain within remote sensing that is currently garnering significant attention. This paper introduces a pioneering semantic segmentation model based on TransUNet architecture with improved coordinate attention for remote-sensing imagery. It is composed of an encoding stage and [...] Read more.
Semantic segmentation stands as a prominent domain within remote sensing that is currently garnering significant attention. This paper introduces a pioneering semantic segmentation model based on TransUNet architecture with improved coordinate attention for remote-sensing imagery. It is composed of an encoding stage and a decoding stage. Notably, an enhanced and improved coordinate attention module is employed by integrating two pooling methods to generate weights. Subsequently, the feature map undergoes reweighting to accentuate foreground information and suppress background information. To address the issue of time complexity, this paper introduces an improvement to the transformer model by sparsifying the attention matrix. This reduces the computing expense of calculating attention, making the model more efficient. Additionally, the paper uses a combined loss function that is designed to enhance the training performance of the model. The experimental results conducted on three public datasets manifest the efficiency of the proposed method. The results indicate that it excels in delivering outstanding performance for semantic segmentation tasks pertaining to remote-sensing images. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Semantic Segmentation, 2nd Edition)
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