Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Recent Applications of Computer Vision for Automation and Robotics
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Applications of Computer Vision for Automation and Robotics

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

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 4295

Special Issue Editors

Prof. Dr. Xiaobin Zhu

E-Mail Website
Guest Editor
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: image and video processing; pattern recognition; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Jianqing Zhu

E-Mail Website
Guest Editor
College of Engineering, Huaqiao University, Quanzhou 362021, China
Interests: person/vehicle re-identification; image/video understanding and analysis

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your work to our journal. Computer vision applications for automation and robotics have become one of the most rapidly developing areas. Automation and robotics are among the leading areas of practical applications for recently developed artificial intelligence solutions, particularly computer and machine vision algorithms. It opens new challenges and difficulties related to its availability, stability, safety, and computational complexities. This Special Issue tries to highlight the latest research achievements on computer vision applications for automation and robotics to solve these arising challenges. This Special Issue aims to publicize new ideas, original trend analyses, originally developed software, new methods, and other research results concerning computer vision applications for automation and robotics. This Special Issue aims to bring together the research communities interested in computer and machine vision, focusing on both automation and robotics, as well as computer science.

This Special Issue aims to discuss and solve the current key issues and problems related to recent applications of computer vision for automation and robotics. We only accept submissions for articles related to applications of computer vision for automation and robotics.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not be limited to) the following:

  • Video simultaneous localization and mapping (VSLAM) solutions;
  • Image-based navigation of unmanned aerial vehicles (UAVs) and other mobile robots;
  • Texture analysis and shape recognition;
  • Novel image descriptors useful for image-based classification of objects;
  • Feature extraction and image registration;
  • Binarization algorithms and binary image analysis;
  • Fast algorithms useful for embedded solutions;
  • No-reference image quality assessment;
  • Natural image analysis;
  • Applications of computer vision in autonomous vehicles;
  • Image and video scene understanding;
  • Image and video reconstruction;
  • Intelligent transport and logistics;
  • Biomedical engineering;
  • The safety of the human-machine interactions.

We look forward to receiving your contributions.

Dr. Xiaobin Zhu
Dr. Jianqing Zhu
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

  • automation and robotics
  • computer vision applications
  • image analysis
  • video analysis

Published Papers (3 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

10 pages, 3372 KiB  
Article
3D Model Retrieval Algorithm Based on DSP-SIFT Descriptor and Codebook Combination
by Yuefan Hu, Haoxuan Zhang, Jing Gao and Nan Li
Appl. Sci. 2022, 12(22), 11523; https://doi.org/10.3390/app122211523 - 13 Nov 2022
Cited by 2 | Viewed by 1346
Abstract
Recently, extensive research efforts have been dedicated to view-based 3D object retrieval, owing to its advantage of using a set of 2D images to represent 3D objects. Some existing image processing technologies can be employed. In this paper, we adopt Bag-of-Words for view-based [...] Read more.
Recently, extensive research efforts have been dedicated to view-based 3D object retrieval, owing to its advantage of using a set of 2D images to represent 3D objects. Some existing image processing technologies can be employed. In this paper, we adopt Bag-of-Words for view-based 3D object retrieval. Instead of SIFT, DSP-SIFT is extracted from all images as object features. Moreover, two codebooks of the same size are generated by approximate k-means. Then, we combine two codebooks to correct the quantization artifacts and improve recall. Bayes merging is applied to address the codebook correlation (overlapping among different vocabularies) and to provide the benefit of high recall. Moreover, Approximate Nearest Neighbor (ANN) is used to quantization. Experimental results on ETH-80 datasets show that our method improves the performance significantly compared with the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Recent Applications of Computer Vision for Automation and Robotics)
►▼ Show Figures

Figure 1

11 pages, 416 KiB  
Article
Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
by Zhixiong Wu and Tingxi Wen
Appl. Sci. 2022, 12(17), 8792; https://doi.org/10.3390/app12178792 - 1 Sep 2022
Cited by 1 | Viewed by 1115
Abstract
Visible-infrared person re-identification (VIPR) has great potential for intelligent video surveillance systems at night, but it is challenging due to the huge modal gap between visible and infrared modalities. For that, this paper proposes a minimizing maximum feature space deviation (MMFSD) method for [...] Read more.
Visible-infrared person re-identification (VIPR) has great potential for intelligent video surveillance systems at night, but it is challenging due to the huge modal gap between visible and infrared modalities. For that, this paper proposes a minimizing maximum feature space deviation (MMFSD) method for VIPR. First, this paper calculates visible and infrared feature centers of each identity. Second, this paper defines feature space deviations based on these feature centers to measure the modal gap between visible and infrared modalities. Third, this paper minimizes the maximum feature space deviation to significantly reduce the modal gap between visible and infrared modalities. Experimental results show the superiority of the proposed method, e.g., on the RegDB dataset, the rank-1 accuracy reaches 92.19%. Full article
(This article belongs to the Special Issue Recent Applications of Computer Vision for Automation and Robotics)
►▼ Show Figures

Figure 1

18 pages, 3666 KiB  
Article
Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization
by Shengwei Xu, Shanlin Lv, Yaqi Liu, Chao Xia and Nan Gan
Appl. Sci. 2022, 12(13), 6480; https://doi.org/10.3390/app12136480 - 26 Jun 2022
Cited by 3 | Viewed by 1216
Abstract
Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task that aims at detecting and localizing the source and forged regions from a series of input suspected image pairs. In this work, we propose a novel Scale-Adaptive Deep Matching [...] Read more.
Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task that aims at detecting and localizing the source and forged regions from a series of input suspected image pairs. In this work, we propose a novel Scale-Adaptive Deep Matching (SADM) network for CISDL, consisting of a feature extractor, a scale-adaptive correlation module and a novel mask generator. The feature extractor is built on VGG, which has been reconstructed with atrous convolution. In the scale-adaptive correlation computation module, squeeze-and-excitation (SE) blocks and truncation operations are integrated to process arbitrary-sized images. In the mask generator, an attention-based separable convolutional block is designed to reconstruct richer spatial information and generate more accurate localization results with less parameters and computation burden. Last but not least, we design a pyramid framework of SADM to capture multiscale details, which can increase the detection and localization accuracy of multiscale regions and boundaries. Extensive experiments demonstrate the effectiveness of SADM and the pyramid framework. Full article
(This article belongs to the Special Issue Recent Applications of Computer Vision for Automation and Robotics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop