Introduction to Computer Vision and Its Applications

This course introduces participants to the foundational concepts and methodologies of computer vision, with a particular focus on applications within the humanities studies. Through theoretical insights and practical tools, it aims to:

Present key artificial intelligence techniques, especially supervised deep learning models.

Explore computer vision tasks such as image classification, semantic segmentation, and text recognition.

Demonstrate how to prepare and process visual datasets for humanities research.

Foster an understanding of tools like Roboflow, Google Teachable Machine, and Transkribus for real-world applications.

By the end of this course, students will be able to:

Understand Core Concepts: Explain the distinctions between AI, machine learning, and deep learning, and describe their relevance to computer vision.

Apply CV Techniques: Identify and implement basic computer vision tasks, such as image classification, using appropriate tools.

Develop Annotated Datasets: Create and manage labeled image datasets suitable for training classification models using platforms like Roboflow.

Experiment with AI Tools: Use Google Teachable Machine for training simple classifiers and interpret the results.

Process Historical Manuscripts: Apply convolutional neural networks for handwritten text recognition through tools such as Transkribus.

Critically Engage with Humanities Data: Reflect on the methodological challenges and opportunities of applying CV in digital humanities contexts.

Students will also have access to all materials via the myAriel platform and can consult the instructor during office hours or by email.