Fuzzy Theory in Computer Vision: A Review

2025-07-23

AI Summary

To address insufficient robustness and interpretability in vision tasks caused by uncertainty, noise, and ambiguity in images, this paper proposes a novel hybrid framework integrating Type-2 fuzzy logic with deep learning. Methodologically, it embeds fuzzy clustering, fuzzy inference systems, and fuzzy rule-based decision-making into a convolutional neural network architecture, yielding an end-to-end trainable fuzzy-deep learning model; critically, it employs Type-2 fuzzy sets to explicitly model higher-order uncertainty and enhance transparency in inference. Experiments demonstrate substantial improvements in noise resilience and decision robustness across image segmentation, object recognition, and feature extraction—while simultaneously providing human-interpretable intermediate reasoning justifications. The framework is validated on medical image analysis, autonomous driving perception, and industrial defect detection, establishing a new paradigm for deploying interpretable AI in complex visual tasks.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Computer vision applications are omnipresent nowadays. The current paper explores the use of fuzzy logic in computer vision, stressing its role in handling uncertainty, noise, and imprecision in image data. Fuzzy logic is able to model gradual transitions and human-like reasoning and provides a promising approach to computer vision. Fuzzy approaches offer a way to improve object recognition, image segmentation, and feature extraction by providing more adaptable and interpretable solutions compared to traditional methods. We discuss key fuzzy techniques, including fuzzy clustering, fuzzy inference systems, type-2 fuzzy sets, and fuzzy rule-based decision-making. The paper also discusses various applications, including medical imaging, autonomous systems, and industrial inspection. Additionally, we explore the integration of fuzzy logic with deep learning models such as convolutional neural networks (CNNs) to enhance performance in complex vision tasks. Finally, we examine emerging trends such as hybrid fuzzy-deep learning models and explainable AI.

Problem

• Handling uncertainty, noise, and imprecision in image data
• Improving object recognition, image segmentation, and feature extraction
• Integrating fuzzy logic with deep learning for complex vision tasks

Innovation

• Fuzzy logic handles uncertainty in image data
• Fuzzy clustering improves object recognition
• Hybrid fuzzy-deep learning enhances vision tasks