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 752

Special Issue Editors


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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

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

Published Papers (1 paper)

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Research

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://0-doi-org.brum.beds.ac.uk/10.3390/app14083338 - 15 Apr 2024
Viewed by 539
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
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