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
Computer Vision and Pattern Recognition: Advanced Techniques and Applications
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Computer Vision and Pattern Recognition: Advanced Techniques and Applications

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2688

Special Issue Editors

Dr. Abhijit Sarkar

E-Mail Website
Guest Editor
Division of Freight, Transit, and Heavy Vehicle Safety, Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA
Interests: statistical data analysis; statistical modeling; computer vision; machine learning; deep learning; signal processing; affective computing
Prof. Dr. Lynn Abbott

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Interests: computer vision; image processing; biometrics; sensing for autonomous vehicles

Special Issue Information

Dear Colleagues,

We are thrilled to announce a Special Issue in Applied Science titled “Computer Vision and Pattern Recognition: Advanced Techniques and Applications”. Computer vision and pattern recognition are driving transformative advances across many domains, from healthcare and autonomous vehicles to robotics and augmented reality. The field is continuously evolving through new innovations in sensors, algorithms, and novel architectures. The last few years have seen advances in vision transformers, foundational models, 3D scene understanding, explainability, and self-supervised models. Advances in computer vision and pattern recognition have the potential to make positive impacts in related fields. This Special Issue seeks to showcase the most innovative and impactful research in this rapidly evolving landscape.

We welcome contributions that bridge the gap between computer vision and other domains, fostering interdisciplinary collaboration and driving real-world applications. We invite submissions on a broad range of topics, including but not limited to:

  • Deep learning for computer vision;
  • Object detection and recognition;
  • Image and video analysis;
  • 3D vision and reconstruction;
  • Scene understanding and segmentation;
  • Sensor fusion for 3D scene understanding;
  • Pattern recognition and machine learning;
  • Robotics and vision-based navigation;
  • Medical imaging and healthcare applications;
  • Autonomous vehicles and drones;
  • Human–computer interactions;
  • Vision transformer and applications;
  • Foundational models and applications.

Dr. Abhijit Sarkar
Prof. Dr. Lynn Abbott
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

  • computer vision
  • pattern recognition
  • 3D vision

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

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

29 pages, 14445 KiB  
Article
The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
by Gonçalo J. M. Rosa, João M. S. Afonso, Pedro D. Gaspar, Vasco N. G. J. Soares and João M. L. P. Caldeira
Appl. Sci. 2024, 14(15), 6462; https://doi.org/10.3390/app14156462 - 24 Jul 2024
Viewed by 507
Abstract
Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear [...] Read more.
Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition: Advanced Techniques and Applications)
►▼ Show Figures

Figure 1

16 pages, 6299 KiB  
Article
Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
by Hui Sun, Shuguang Yang, Rui Wang and Kaixin Yang
Appl. Sci. 2024, 14(15), 6459; https://doi.org/10.3390/app14156459 - 24 Jul 2024
Cited by 1 | Viewed by 455
Abstract
Landslides are a kind of geological hazard with great destructive potential. When a landslide event occurs, a reliable landslide segmentation method is important for assessing the extent of the disaster and preventing secondary disasters. Although deep learning methods have been applied to improve [...] Read more.
Landslides are a kind of geological hazard with great destructive potential. When a landslide event occurs, a reliable landslide segmentation method is important for assessing the extent of the disaster and preventing secondary disasters. Although deep learning methods have been applied to improve the efficiency of landslide segmentation, there are still some problems that need to be solved, such as the poor segmentation due to the similarity between old landslide areas and the background features and missed detections of small-scale landslides. To tackle these challenges, a proposed high-resolution semantic segmentation algorithm for landslide scenes enhances the accuracy of landslide segmentation and addresses the challenge of missed detections in small-scale landslides. The network is based on the high-resolution network (HR-Net), which effectively integrates the efficient channel attention mechanism (efficient channel attention, ECA) into the network to enhance the representation quality of the feature maps. Moreover, the primary backbone of the high-resolution network is further enhanced to extract more profound semantic information. To improve the network’s ability to perceive small-scale landslides, atrous spatial pyramid pooling (ASPP) with ECA modules is introduced. Furthermore, to address the issues arising from inadequate training and reduced accuracy due to the unequal distribution of positive and negative samples, the network employs a combined loss function. This combined loss function effectively supervises the training of the network. Finally, the paper enhances the Loess Plateau landslide dataset using a fractional-order-based image enhancement approach and conducts experimental comparisons on this enriched dataset to evaluate the enhanced network’s performance. The experimental findings show that the proposed methodology achieves higher accuracy in segmentation performance compared to other networks. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition: Advanced Techniques and Applications)
►▼ Show Figures

Figure 1

14 pages, 1852 KiB  
Article
Inv-ReVersion: Enhanced Relation Inversion Based on Text-to-Image Diffusion Models
by Guangzi Zhang, Yulin Qian, Juntao Deng and Xingquan Cai
Appl. Sci. 2024, 14(8), 3338; https://doi.org/10.3390/app14083338 - 15 Apr 2024
Viewed by 1274
Abstract
Diffusion models are widely recognized in image generation for their ability to produce high-quality images from text prompts. As the demand for customized models grows, various methods have emerged to capture appearance features. However, the exploration of relations between entities, another crucial aspect [...] Read more.
Diffusion models are widely recognized in image generation for their ability to produce high-quality images from text prompts. As the demand for customized models grows, various methods have emerged to capture appearance features. However, the exploration of relations between entities, another crucial aspect of images, has been limited. This study focuses on enabling models to capture and generate high-level semantic images with specific relation concepts, which is a challenging task. To this end, we introduce the Inv-ReVersion framework, which uses inverse relations text expansion to separate the feature fusion of multiple entities in images. Additionally, we employ a weighted contrastive loss to emphasize part of speech, helping the model learn more abstract relation concepts. We also propose a high-frequency suppressor to reduce the time spent on learning low-frequency details, enhancing the model’s ability to generate image relations. Compared to existing baselines, our approach can more accurately generate relation concepts between entities without additional computational costs, especially in capturing abstract relation concepts. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition: Advanced Techniques and Applications)
►▼ 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