Laboratorio: introduction to computer vision and image processing
A.A. 2024/2025
Obiettivi formativi
The aim of this workshop is to provide students with a foundational understanding of computer vision and image processing techniques, enabling them to analyze and interpret visual data within the context of social science research. Students will learn to apply various user-friendly computer vision tools to analyze and interpret visual data relevant to the humanities. The workshop will emphasize practical applications in visual art analysis and humanities research.
Risultati apprendimento attesi
By the end of this workshop, students will be able to understand the fundamental concepts of computer vision and image processing, including how machines interpret visual data, identify machine learning approaches used in computer vision, particularly supervised learning methods relevant to visual art analysis, apply user-friendly computer vision tools (e.g., ImageJ) to process and analyze visual data in the context of publishing and communication, and identify and discuss the applications of computer vision and image processing in the fields of art, communication, and fashion.
Students will have access to course materials and supplemental resources on the myAriel platform, as well as the opportunity to consult with the instructor during office hours or via email.
Students will have access to course materials and supplemental resources on the myAriel platform, as well as the opportunity to consult with the instructor during office hours or via email.
Periodo: Secondo semestre
Corso singolo
Questo insegnamento può essere seguito come corso singolo.
Programma e organizzazione didattica
Edizione unica
Responsabile
Programma
This course will combine technical theory with hands-on practice, focusing on essential computer vision tasks such as segmentation, object detection, and image analysis, within the context of social sciences and humanities research. Key topics will include supervised learning techniques, working with annotated datasets, and applying visual data analysis in fields like art, cultural heritage, and image processing. During practical sessions, students will use computational tools to analyze and process visual data, including annotating datasets of paintings or cultural artifacts and experimenting with object detection tools to identify features in artistic or historical contexts. Using platforms like Google Colab, Label Studio, Roboflow, and ImageJ, students will gain experience in dataset preparation, annotation, and image processing, building skills in both dataset creation and visual data analysis.
Prerequisiti
Familiarity with using computers and basic software applications. No prior knowledge of programming or computer science is required, but an interest in exploring digital tools and technologies in humanities research is recommended.
To apply for admission to the workshop, it is mandatory to follow the instructions on the webpage: https://eccm.cdl.unimi.it/it/insegnamenti/laboratori
To apply for admission to the workshop, it is mandatory to follow the instructions on the webpage: https://eccm.cdl.unimi.it/it/insegnamenti/laboratori
Metodi didattici
This course takes a hands-on, interactive approach, where students will apply computational techniques to analyze historical materials. Active participation is required throughout the course, including conducting research and presenting findings in class. Class attendance is mandatory.
Materiale di riferimento
1) Slides, handouts, and scholarly articles provided by the instructor
2) Books on computer vision to support learning (these are not a prerequisite, just references):
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola, and William T. Freeman (MIT Press, 2024): A comprehensive introduction to core computer vision concepts, suitable for those new to the field. (Online https://mitpress.ublish.com/ebook/foundations-of-computer-vision-1/12791/418)
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Adaptive Computation and Machine Learning series): A more advanced text focused on deep learning techniques, ideal for students seeking in-depth understanding of the algorithms behind computer vision. (online: https://www.deeplearningbook.org/)
- Biological and Computer Vision by Gabriel Kreiman (Cambridge University Press, 2021): Explores both biological and computational vision, providing useful insights into vision systems and their application in computer vision, accessible for students with a basic understanding of the subject. (online: https://klab.tch.harvard.edu/publications/Books/BiologicalAndComputerVision/TableOfContents.html)
- Artificial Intelligence, A Guide for Thinking Humans by Melanie Mitchell (2019): Provides a clear and accessible overview of AI concepts, challenges, and the history of the field. It is a helpful resource for students seeking to understand the broader context of AI and its implications beyond computer vision (https://melaniemitchell.me/aibook/)
- Pixels & Paintings: Foundations of Computer-assisted Connoisseurship by David G. Stork (2023): A specialized resource connecting computer vision to art and heritage analysis, can be useful for students interested in art and cultural heritage applications.
3) Transkribus: Offers valuable online resources for handwritten text recognition. The platform provides guides and tools for working with historical manuscript images, suitable for students engaged in transcription tasks: https://www.transkribus.org/blog
2) Books on computer vision to support learning (these are not a prerequisite, just references):
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola, and William T. Freeman (MIT Press, 2024): A comprehensive introduction to core computer vision concepts, suitable for those new to the field. (Online https://mitpress.ublish.com/ebook/foundations-of-computer-vision-1/12791/418)
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Adaptive Computation and Machine Learning series): A more advanced text focused on deep learning techniques, ideal for students seeking in-depth understanding of the algorithms behind computer vision. (online: https://www.deeplearningbook.org/)
- Biological and Computer Vision by Gabriel Kreiman (Cambridge University Press, 2021): Explores both biological and computational vision, providing useful insights into vision systems and their application in computer vision, accessible for students with a basic understanding of the subject. (online: https://klab.tch.harvard.edu/publications/Books/BiologicalAndComputerVision/TableOfContents.html)
- Artificial Intelligence, A Guide for Thinking Humans by Melanie Mitchell (2019): Provides a clear and accessible overview of AI concepts, challenges, and the history of the field. It is a helpful resource for students seeking to understand the broader context of AI and its implications beyond computer vision (https://melaniemitchell.me/aibook/)
- Pixels & Paintings: Foundations of Computer-assisted Connoisseurship by David G. Stork (2023): A specialized resource connecting computer vision to art and heritage analysis, can be useful for students interested in art and cultural heritage applications.
3) Transkribus: Offers valuable online resources for handwritten text recognition. The platform provides guides and tools for working with historical manuscript images, suitable for students engaged in transcription tasks: https://www.transkribus.org/blog
Modalità di verifica dell’apprendimento e criteri di valutazione
Assessment Method: Evaluation at the end of the course.
Type of Examination: In addition to attending lessons, students will complete a final assessment, which includes an oral exam and a presentation of a project to be agreed upon with the instructor. Detailed project guidelines will be provided during the course.
Evaluation Criteria: Ability to demonstrate and discuss key concepts; critical reflection on the completed work; clarity of communication; proficiency in using relevant terminology; efficient use of the computational tools introduced in the course; overall effectiveness of the presentation.
Type of evaluation method: approval of 3 CFUs.
Assessment result: approved/not approved.
The format of the assessment for students with disabilities should be arranged in advance with the lecturer.
Type of Examination: In addition to attending lessons, students will complete a final assessment, which includes an oral exam and a presentation of a project to be agreed upon with the instructor. Detailed project guidelines will be provided during the course.
Evaluation Criteria: Ability to demonstrate and discuss key concepts; critical reflection on the completed work; clarity of communication; proficiency in using relevant terminology; efficient use of the computational tools introduced in the course; overall effectiveness of the presentation.
Type of evaluation method: approval of 3 CFUs.
Assessment result: approved/not approved.
The format of the assessment for students with disabilities should be arranged in advance with the lecturer.
Docente/i