Fuzzy Theory in Computer Vision: A Review

TL;DR

This review paper discusses how fuzzy logic enhances computer vision by managing uncertainty and imprecision, improving tasks like recognition and segmentation, and integrating with deep learning for advanced applications.

Contribution

It provides a comprehensive overview of fuzzy techniques in computer vision, including recent integration with deep learning and emerging hybrid models.

Findings

Fuzzy logic improves object recognition and image segmentation.

Integration with deep learning enhances performance in complex vision tasks.

Emerging hybrid fuzzy-deep models advance explainable AI.

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.