The European project Campiello (ESPRIT LTR 25572), as part of its methodology, is conducting an experiment of social observation and intervention regarding the process of community making in Venice, with a special focus on the role of memory collection and ri-elaboration in such a process. In this paper the social observation is presented describing its context, a particular local community inhabiting a Venice neighborhood, and the activities carried on by a local school to collect and represent the community memories. This experience is then used for a reflection about how Information Technology could extend the process and enhance it. For this purpose the process around memories is described according to four sub-activities: collection, dissemination, representation and enrichment. For each of them the needs and requirements coming from the observation are presented and existing possible technologies to support them are briefly described.
Memories and Local Communities: An Experience in Venice
| Alessandra Agostini, Valeria Giannella, Antonietta Grasso, Renata Tinini |
| Designing Collective Memories, 7th Le Travail Humain Workshop, September 14, 1998, Paris, France |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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