Key Tools and Functions:

• Image Acquisition Toolbox: This toolbox allows you to capture images from various sources like cameras, video files, or image sequences. You can set up real-time image acquisition using functions like imaqhwinfo, videoinput, and imageAcquisition.
• Image Processing Toolbox: This toolbox provides a rich set of functions for image processing tasks, including image filtering, noise reduction, feature extraction, and object detection. You can use these functions to preprocess and analyze the acquired images.
• Deep Learning Toolbox: This toolbox provides tools for designing, training, and deploying deep learning models. You can use it to create custom deep learning models for image classification, object detection, and other tasks.

General Approach:

Additional Considerations:

• Performance: Consider the computational requirements of your image processing and deep learning tasks. You may need to optimize the code and hardware to achieve real-time performance.
• Data Quality: Ensure that the acquired images are of good quality and that the labeling is accurate.
• Data Privacy: If you are collecting and storing personal data, ensure that you comply with relevant data privacy regulations.

While there might not be a specific article on real-time image dataset creation using MATLAB, you can leverage the powerful tools and techniques provided by MATLAB to implement your own solution. By combining these tools and following the general approach outlined above, you can effectively create and manage real-time image datasets for various applications.