Lecture

Concept-Based Image Systems

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In course

HUM-369: Digital humanities

Ce cours explore comment les Humanités se transforment à l'ère de l'intelligence artificielle et des données massives. Chaque séance aborde le futur d'une discipline. Les étudiants développent un prototype numérique (Vibe coding) pour accelerer la recherch ...

Description

This lecture explores different types of image systems, including concept-based, named entity-based, and perspective connections-based systems. It delves into the centrality of document-images, coherent subsets in corpora, and the analysis of graphics. The lecture discusses the nature of concept-based image systems, their hierarchical structure, and their role in artificial vision. It also covers named entity-based systems, their unique identification of entities, and integration with international databases like Wikidata. Moreover, the lecture explains perspective connections-based systems, focusing on establishing visual relationships between images from different perspectives. The lecture concludes with a discussion on morphological relationships in image systems, emphasizing the formalization of morphological patterns and the challenges in detecting invariance across diverse materials and textures.

Instructors (3)

Frédéric Kaplan

Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Short biography Pro

Isabella Di Lenardo

Ph.D. in Theories and Art History. Isabella di Lenardo is senior scientist in Digital Humanities and Urban History. Her research activity is focused on digital tools and methods applied to Urban History. She is an expert in ancient cartography, city representations, cadastral sources interpreted through digital modeling, extraction and analysis systems.She's also interested in network analysis questioning the production and circulation of artistic and architectural knowledge in Europe XVIth – XVIIIth centuries in particular on North-South relationships and influences. The development of collaboration projects with European institutions and the activity carried out on various initiatives supported by the European Commission have allowed her to acquire specialism in the field of science applied to heritage, developing in particular specific skills for the enhancement of Cultural Heritage through digital tools. She leads projects in collaboration with Bibliothèque national de France (Paris), Institut National d’Histoire de l’Art (Paris), Ecole nationale des chartes (Paris), Université Paris I Panthéon-Sorbonne, Centre Allemand d’Histoire de l’Art (Paris), Musée du Louvre, Archives Nationales de Paris, Bibliothèque Historique de la Ville de Paris, Réunion des Musées Nationaux. She supervised all the urban modeling simulations for the Venice Time Machine project and acted as content curator for all the exhibitions (Venice Biennale, Grand Palais-Paris, Datasquare - ArtLab-Lausanne, Museo Correr-Venice). She was Head of “Replica” project : digitizing 1 million photos of artworks in the Fondazione Giorgio Cini (Venice) and visual patterns extraction through a search engine for visual similarities. She has a Ph.D in Art History and also studied Archaeology and Urban Studies. She has published essays and articles about Venetian Art and Urban History and participated in many Art exhibitions in European museums. She coordinated summer schools and workshops about Digital Tools for Art History and Urban History. She attended conferences on digital methods for History in universities and patrimonial institutions.

Official source

Ontological neighbourhood

Information engineering

Artificial intelligence: Computer vision

Related concepts (32)

Image analysis

Image analysis or imagery analysis is the extraction of meaningful information from s; mainly from s by means of techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information.

Texture mapping

Texture mapping is a method for mapping a texture on a . Texture here can be high frequency detail, surface texture, or color. The original technique was pioneered by Edwin Catmull in 1974. Texture mapping originally referred to diffuse mapping, a method that simply mapped pixels from a texture to a 3D surface ("wrapping" the image around the object).

Procedural texture

In computer graphics, a procedural texture is a texture created using a mathematical description (i.e. an algorithm) rather than directly stored data. The advantage of this approach is low storage cost, unlimited texture resolution and easy texture mapping. These kinds of textures are often used to model surface or volumetric representations of natural elements such as wood, marble, granite, metal, stone, and others. Usually, the natural look of the rendered result is achieved by the usage of fractal noise and turbulence functions.

Scale space

Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the scale-space representation, parametrized by the size of the smoothing kernel used for suppressing fine-scale structures.

Scale-invariant feature transform

The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, , 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database.

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Maud Ehrmann

Maud Ehrmann is a research scientist at EPFL’s Digital Humanities Laboratory lead by Prof. Frédéric Kaplan . She holds a PhD in Computational Linguistics from the Paris Diderot Universtiy (Paris 7) and has been engaged in a large number of scientific projects centred on information extraction and text analysis, both for present-time and historical documents. Her main research interests span Natural Language Processing and Digital Humanities and include, among others, historical text annotation, historical data processing and representation, named entity recognition, and multilingual linguistic resources creation. Her current work at the DHLAB focuses on ‘impresso - Media Monitoring of the Past’ , a SNF sinergia project she initiated with Marten Düring ( C2DH ) and Simon Clematide ( ICL ) and which aims at enabling critical analysis of historical newspapers. In addition to the overall project management, her contributions to this project include system design and data management, annotation and benchmarking and named entity processing. Besides, she participates to the activities of the Venice Time Machine , working particularly on information extraction and knowledge representation tasks. Previously, she worked on the Garzoni project where she supervised and contributed to the development of a web-based transcription and annotation interface - in collaboration with Orlin Topalov, and built a linked data-based historical knowledge base. She also contributed to the Le Temps Digital Archives project . Prior to joining the DHLAB, she worked at the Linguistics Computing Laboratory at the Sapienza University of Rome with Roberto Navigli, where she worked on the BabelNet resource - a very large multilingual encyclopaedic dictionary and semantic network - and contributed to the LIDER project. Before that, she has been working for four years at the European Commission’s Joint Research Centre in Ispra, Italy, as member of the OPTIMA unit (now Text and Data mining unit), which develops innovative and application-oriented solutions for retrieving and extracting information from the Internet with a focus on high multilinguality. Together with Erik van der Goot, Ralf Steinberger , Hristo Tanev, Leo della Rocca and many others, she contributed to the development of the Europe Media Monitor (EMM). Prior, she worked at the Xerox Europe Research Centre in Grenoble, France (now Naver Labs Europe ) in the Parsing & Semantics group led by Frédérique Segond, first as PhD candidate supported through a CIFRE grant under the supervision of Caroline Brun and Bernard Victorri , then as a post-doctoral researcher. There her research focused mainly on the automatic processing and fine-grained analysis of entities of interest, specifically named entities and temporal expressions.

Related concepts (32)

Scale-invariant feature transform