Guest Editors
Prof. Dr. Siddhartha Bhattacharyya
Email: siddhartha.bhattacharyya@vsb.cz
Affiliation: Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava 708 00, Czech Republic
Homepage:
Research Interests: computational intelligence, quantum computing, disruptive technologies, etc.
Prof. Jyoti Sekhar Banerjee
Email: jyotisekhar.banerjee@bitcollege.in
Affiliation: Department of Computer Science and Engineering (AI and ML), Techno Bengal Institute of Technology, Kolkata 700091, India
Homepage:
Research Interests: computational intelligence, generative AI, federated learning, edge AI, etc.
Prof. Jan Platoš
Email: jan.platos@vsb.cz
Affiliation: Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava 708 00, Czech Republic
Homepage:
Research Interests: computational intelligence, quantum computing, and edge AI
Prof. Ivan Zelinka
Email: ivan.zelinka@vsb.cz
Affiliation: Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava 708 00, Czech Republic
Homepage:
Research Interests: unconventional algorithms, deterministic chaos, fractal geometry, complexity, quantum computation
Prof. Vaclav Snasel
Email: vaclav.snasel@vsb.cz
Affiliation: Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava 708 00, Czech Republic
Homepage:
Research Interests: artificial intelligence, deep Learning, neural networks, evolutionary computation, quantum computation
Summary
Recently, there has been an increase in the amount of image data that needs to be processed due to the advent of various sensors and acquisition devices. Classical techniques often fall short in providing a faithful understanding of the acquired image information due to underlying uncertainty and complexity. The problem becomes more severe when the acquired information exhibits multiple dimensions, such as in the form of hyperspectral and medical images (especially X-ray and CT images). Hence, scientists have resorted to computational intelligence techniques for the faithful analysis and understanding of multidimensional images.
Lately, quantum computational intelligence, which combines computational intelligence and quantum computing, has yielded faster, more robust, and fail-safe solutions to real-world problems, and image analysis is no exception in this regard. The convergence of quantum computing with computational intelligence tools, such as neural networks, fuzzy sets, and metaheuristics, can surmount the inherent uncertainties and imprecision encountered in analyzing and understanding multidimensional image information. Hence, the field of quantum computational intelligence has aroused interest among the computer vision community regarding multidimensional image processing.
This themed issue aims to report the latest findings in the field of quantum computational intelligence applied to the analysis and understanding of multidimensional images.
The following subtopics are the particular interests of this special issue, including but not limited to:
· Quantum Computational Intelligence Theory & Models
· Quantum-Inspired Neural Networks
· Quantum-Inspired Fuzzy Systems and Neutrosophic Logic
· Quantum Metaheuristics and Optimization Algorithms
· Quantum Circuit Design for Intelligent Systems
· Quantum Inference, Reasoning, and Decision-Making
· Quantum Machine Learning for Image Analysis
· Quantum-Enhanced Segmentation and Thresholding
· Quantum Algorithms for Feature Extraction and Pattern Recognition
· Quantum Computing in Image Denoising and Enhancement
Keywords
quantum machine learning (QML), quantum image processing (QIP), quantum neural networks (QNNs), quantum fuzzy systems, quantum metaheuristics, multidimensional image reconstruction, quantum-enhanced image segmentation, quantum parallelism in image understanding, quantum feature extraction, quantum entanglement-based pattern recognition
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