用模糊理论提升视觉任务对不确定性的处理能力
Fuzzy Theory in Computer Vision: A Review
- 引入模糊逻辑模拟人类推理，处理图像中的模糊与噪声
- 结合深度学习模型，提升复杂场景下识别与分割性能
- 适合医疗影像、自动驾驶等需可解释决策的场景
当前计算机视觉应用广泛，本文综述模糊逻辑在该领域的应用，强调其在处理图像数据中不确定性、噪声和不精确性方面的作用。模糊逻辑能够建模渐变过程与类人推理，为物体识别、图像分割和特征提取提供更灵活且可解释的解决方案。文中讨论了模糊聚类、模糊推理系统、二型模糊集及基于规则的决策等关键技术，并涵盖医学成像、自动驾驶系统和工业检测等应用场景。此外，还探讨了模糊逻辑与卷积神经网络（CNN）等深度学习模型的融合，以增强复杂视觉任务的表现。最后，分析了混合模糊-深度学习模型与可解释人工智能等新兴趋势。
原文摘要 · Abstract (English)
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.
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