With modern computing and processing, more complex tasks can be solved through intelligent technologies and algorithms. Automation is becoming more pervasive with the implementation of smart cities, automated vehicles, intelligent factories, predictive translators, and voice assistants. Furthermore, these applications are characterized by incorporating various machine intelligence, deep learning, and artificial learning technologies, which paves the way for increased efficiency and robustness. Some analyses conducted in real-world environments for automated technology deployment include image processing, environment prediction, and analysis, as well as real-world monitoring. This combination could be used in driverless autonomous cars that continuously monitor and process inputs from their environments.

An emerging technology that aids the above processing is computer vision, which is used to interpret images and real-world entities in the same way human visual perception systems extract information. Moreover, image processing of objects, including the size, distance travelled, and structure, must be carried out to improve the image's quality. Thus, computer vision technology enables real-world entities' modeling and image processing using machine learning techniques. Fuzzy logic technology may be used with neural networks, reinforcement learning, long-term memory, and unsupervized and supervized learning methods. In recent years, fuzzy networks have dealt with complex sources retrieved from the environment. Complex sources could be processed in fuzzy systems using the problem's natural description rather than the precise relationship values. The collected image values associated with computer vision may have uncertain matters that can be precisely resolved through fuzzy image recognition and analysis methods. The fuzzy assisted analysis technique is used in various applications, including remote processing, digital image classification, gesture recognition in medical analysis, and sensor-enabled intelligent environments. Fuzzy methods, deep learning, and artificial intelligence methods such as K-nearest Neighbour (KNN), robust vision models, sparse training, Support Vector Machine (SVM), 3D modeling, and self-supervized learning models are available.

The aim of this Special Issue is to illustrate the technologies and models associated with computer vision technology, aided in image recognition and analysis by fuzzy models. Thus, the technological implications of artificial intelligence, deep learning models, and machine learning-assisted algorithms for image recognition and classification could be discussed. Original research and review articles are welcomed.

Potential topics include but are not limited to the following:

• Intelligent image recognition technology assisted by computer vision and fuzzy neural networks
• Enhanced models for the deployment of autonomous cars with the computer vision and deep learning models
• Improved location detection and classification based on 3D modelling and deep learning
• Futuristic implementation of registration and classification of images based on AI modelling
• Multimodal image fusion and image retrieval based on novel classification associated with computer vision technologies
• Enhanced image segmentation and reassembly based on computer vision inputs and image analysis based on ML algorithms
• Effective implementation of fuzzy neural networks in image recognition assisted by computer vision
• A novel framework for image classification and learning based on computer vision associated with CNN
• Effective image classification and recognition associated with computer vision for complex real-time environment
• Edge-based image storage and retrieval for associated computer vision in fuzzy neural networks