Visual Computing and Understanding: New Developments and Trends

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

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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All Applied Sciences Computers Electronics Information J. Imaging

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14 pages, 5118 KiB  

Open AccessArticle

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 531

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

(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)

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