Future Trends in Applications of Neural Networks for Vision-Based Autonomous Tasks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 382

Special Issue Editors


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Guest Editor
Department of Integrated System Engineering, School of Global Convergence Studies, Inha University, 100 Inharo, Nam-gu, Incheon 22212, Republic of Korea
Interests: computer vision in autonomous vehicles; applied artificial intelligence; neural networks; digital image processing; applied integrated systems

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Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6725 Szeged, Hungary
Interests: inertial sensors; sensor fusion; localization; intelligent control; robotics

Special Issue Information

Dear Colleagues,

The field of computer vision has experienced remarkable growth in recent years, mainly due to the use of neural networks. Autonomous vision-based applications have become essential in a variety of industries from autonomous driving robotics to surveillance and augmented reality. These neural-network-powered systems have changed the landscape of visual awareness, enabling machines to understand, meet and interact with the world around them. This Special Issue provides a platform to explore and discuss the latest applications, developments and future directions in the use of neural networks for autonomous vision-based tasks.

We invite contributions demonstrating new applications or improvements in neural networks applied in areas including, but not limited to, the following:

  • Autonomous Navigation and Robot Control:

   - Real-time object detection, segmentation and tracking;

   - Simultaneous localization and mapping (SLAM);

   - Route planning and obstacle avoidance;

   - Recognition of gestures and human–robot interaction.

  • Computer vision for autonomous vehicles:

   - Advanced driver assistance systems (ADASs);

   - Traffic signs and directions;

   - Autonomous driving: thinking and decision making.

  • Inspection and Security:

   - Intrusion detection and anomaly detection;

   - Facial recognition and biometric authentication;

   - Accident analysis and incident detection.

  • Augmented and virtual reality:

   - Augmented reality object recognition and interaction;

   - Virtual object management and integration;

   - Immersive virtual reality experience.

  • Environmental Research and Agriculture:

   - Identification and control of crop diseases;

   - Wildlife management and conservation efforts;

   - Remote control of autonomous machinery.

Dr. Vijay Kakani
Dr. Ákos Odry
Guest Editors

Manuscript Submission Information

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Keywords

  • computer vision processing algorithms
  • neural networks
  • vision-based autonomous tasks

Published Papers (1 paper)

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Research

21 pages, 37995 KiB  
Article
An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting
by Gundu Mohamed Hassan, Zijian Min, Vijay Kakani and Geun-Sik Jo
Electronics 2024, 13(15), 2972; https://doi.org/10.3390/electronics13152972 (registering DOI) - 27 Jul 2024
Abstract
Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. [...] Read more.
Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. In this paper, we adopt an optimization perspective and introduce a novel maximum consensus robust fitting algorithm that incorporates the maximum entropy framework into the consensus maximization problem. Specifically, we incorporate the probability distribution of inliers calculated using maximum entropy with consensus constraints. Furthermore, we introduce an improved relaxed and accelerated alternating direction method of multipliers (R-A-ADMMs) strategy tailored to our framework, facilitating an efficient solution to the optimization problem. Our proposed algorithm demonstrates superior performance compared to state-of-the-art methods on both synthetic and contaminated real datasets, particularly when dealing with contaminated datasets containing a high proportion of outliers. Full article
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