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Applied Sciences
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Artificial Intelligence in Computer Vision and Object Detection
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Artificial Intelligence in Computer Vision and Object Detection

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

Deadline for manuscript submissions: 20 February 2025 | Viewed by 2252

Special Issue Editors

Dr. Luis Alberto Morales-Hernández

E-Mail Website
Guest Editor
Laboratory of Artificial Vision and Thermography/Mechatronics, Faculty of Engineering, Autonomous University of Queretaro, Campus San Juan del Rio, San Juan del Rio 76807, Queretaro, Mexico
Interests: artificial intelligence; artificial vision; thermography and mechatronics
Dr. Saul Tovar-Arriaga

E-Mail Website
Guest Editor
Faculty of Engineering, Autonomous University of Queretaro, Cerro de las Campanas S/N, Santiago de Queretaro, Queretaro 76010, Mexico
Interests: image-based diagnosis; artificial intelligence; medical robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence is an essential tool in many technological systems, conferring them the ability to think as a human would. Some of its most important applications include computer vision systems and object recognition. The former can be understood as systems that allow a scene of the real world to be digitally captured in order to analyze it and extract its information. The latter focuses on finding or locating a specific object in a digital image. Therefore, in an intergrated system, computer vision allows a scene from the real world to be captured and processed, so that the artificial intelligence can then decide how to act; this can be applied in, for example, diagnosis or the detection of objects in engineering, medicine or health sciences. The aim of this Special Issue is to publish novel scientific articles on the application of artificial intelligence to computer vision or object recognition. It is important to mention that this Special Issue is not limited to any specific artificial intelligence technique or application. Therefore, all original articles and reviews that meet the main objective are welcome.

Dr. Luis Alberto Morales-Hernández
Dr. Saul Tovar-Arriaga
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

  • deep learning
  • machine learning
  • image processing
  • segmentation
  • tracking
  • thermography
  • features

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Published Papers (2 papers)

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Research

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14 pages, 4243 KiB  
Article
Multi-Object Tracking with Grayscale Spatial-Temporal Features
by Longxiang Xu and Guosheng Wu
Appl. Sci. 2024, 14(13), 5900; https://doi.org/10.3390/app14135900 - 5 Jul 2024
Viewed by 826
Abstract
In recent multiple object tracking (MOT) research, there have not been many traditional methods and optimizations for matching. Most of today’s popular tracking methods are implemented using deep learning. But many monitoring devices do not have high computing power, so real-time tracking via [...] Read more.
In recent multiple object tracking (MOT) research, there have not been many traditional methods and optimizations for matching. Most of today’s popular tracking methods are implemented using deep learning. But many monitoring devices do not have high computing power, so real-time tracking via neural networks is difficult. Furthermore, matching takes less time than detection and embedding, but it still takes some time, especially for many targets in a scene. Therefore, in order to solve these problems, we propose a new method by using grayscale maps to obtain spatial-temporal features based on traditional methods. Using this method allows us to directly find the position and region in previous frames of the target and significantly reduce the number of IDs that the target needs to match. At the same time, compared to some end-to-end paradigms, our method can quickly obtain spatial-temporal features using traditional methods, which reduces some calculations. Further, we joined embedding and matching to further reduce the time spent on tracking. Our method reduces the calculations in feature extraction and reduces unnecessary matching in the matching stage. Our method was evaluated on benchmark dataset MOT16, and it achieved great performance; the tracking accuracy metric MOTA reached 46.7%. The tracking FPS reached 17.6, and it ran only on a CPU without GPU acceleration. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computer Vision and Object Detection)
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Review

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18 pages, 1057 KiB  
Review
Advancing in RGB-D Salient Object Detection: A Survey
by Ai Chen, Xin Li, Tianxiang He, Junlin Zhou and Duanbing Chen
Appl. Sci. 2024, 14(17), 8078; https://doi.org/10.3390/app14178078 - 9 Sep 2024
Viewed by 983
Abstract
The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces [...] Read more.
The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces the amount of data needed to process while enhancing the accuracy and efficiency of information extraction. In recent years, SOD has made significant progress in many areas such as deep learning, multi-modal fusion, and attention mechanisms. Additionally, it has expanded in real-time detection, weakly supervised learning, and cross-domain applications. Depth images can provide three-dimensional structural information of a scene, aiding in a more accurate understanding of object shapes and distances. In SOD tasks, depth images enhance detection accuracy and robustness by providing additional geometric information. This additional information is particularly crucial in complex scenes and occlusion situations. This survey reviews the substantial advancements in the field of RGB-Depth SOD, with a focus on the critical roles played by attention mechanisms and cross-modal fusion methods. It summarizes the existing literature, provides a brief overview of mainstream datasets and evaluation metrics, and quantitatively compares the discussed models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computer Vision and Object Detection)
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