Topic Editors

Dr. Wei Zhou
School of Computer Science and Informatics, Cardiff University, Cathays, Cardiff CF24 4AG, UK
School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518037, China
Dr. Wenhan Yang
Peng Cheng Laboratory, Shenzhen 518066, China

Visual Computing and Understanding: New Developments and Trends

Abstract submission deadline
30 December 2024
Manuscript submission deadline
30 March 2025
Viewed by
1102

Topic Information

Dear Colleagues,

We as humans have to handle massive amounts of visual information in our daily lives. As a result, there has been recent growing interest in the advancement of artificial intelligence-based perception and analysis algorithms in the field of computer vision and image processing.

Despite significant successes in visual computing and understanding in recent years, new developments and trends in the methods in which these achievements are made are still in their infancy, especially for many complex real-world applications.

The aim of this topic is to progress this field by collecting research on both the theorical and applied issues related to advances in visual computing and understanding. All interested authors are invited to submit their innovative manuscripts on (but are not limited to) the following  topics:

  • Image/video acquisition, fusion, and generation;
  • Image/video coding, restoration, and quality assessment;
  • Image/video classification, segmentation, and detection;
  • Deep learning-based methods for image processing and analysis;
  • Deep learning-based methods for video processing and analysis;
  • Deep learning-based computer vision methods for 3D models;
  • Intelligent vision methods for autonomous driving systems;
  • Robotic vision and its applications;
  • Biomedical vision analysis and applications;
  • Advances in visual computing theories.

Dr. Wei Zhou
Dr. Guanghui Yue
Dr. Wenhan Yang
Topic Editors

Keywords

  • image processing and video processing
  • visual computing and deep learning
  • computer vision and robotic vision
  • autonomous driving
  • biomedical vision
  • image acquisition and image fusion
  • generative models
  • video coding and image restoration
  • quality assessment
  • visual understanding
  • feature extraction and object detection
  • image classification
  • semantic segmentation
  • saliency detection
  • perception modelling

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Computers
computers
2.8 4.7 2012 17.7 Days CHF 1800 Submit
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Information
information
3.1 5.8 2010 18 Days CHF 1600 Submit
Journal of Imaging
jimaging
3.2 4.4 2015 21.7 Days CHF 1800 Submit

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Published Papers (1 paper)

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14 pages, 5118 KiB  
Article
Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance
by Zengyun Liu, Zexun Zheng, Tianyi Qin, Liying Xu and Xu Zhang
Electronics 2024, 13(5), 982; https://doi.org/10.3390/electronics13050982 - 4 Mar 2024
Viewed by 593
Abstract
With the exploration of subterranean scenes, determining how to ensure the safety of subterranean pedestrians has gradually become a hot research topic. Considering the poor illumination and lack of annotated data in subterranean scenes, it is essential to explore the LiDAR-based domain adaptive [...] Read more.
With the exploration of subterranean scenes, determining how to ensure the safety of subterranean pedestrians has gradually become a hot research topic. Considering the poor illumination and lack of annotated data in subterranean scenes, it is essential to explore the LiDAR-based domain adaptive detectors for localizing the spatial location of pedestrians, thus providing instruction for evacuation and rescue. In this paper, a novel domain adaptive subterranean 3D pedestrian detection method is proposed to adapt pre-trained detectors from the annotated road scenes to the unannotated subterranean scenes. Specifically, an instance transfer-based scene updating strategy is designed to update the subterranean scenes by transferring instances from the road scenes to the subterranean scenes, aiming to create sufficient high-quality pseudo labels for fine-tuning the pre-trained detector. In addition, a pseudo label confidence-guided learning mechanism is constructed to fully utilize pseudo labels of different qualities under the guidance of confidence scores. Extensive experiments validate the superiority of our proposed domain adaptive subterranean 3D pedestrian detection method. Full article
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