Práticas de comunicação audiovisual participativa: apresentação de um caso de estudo no contexto das instituições culturais

Nesta comunicação apresentaremos as metodologias e conclusões previstas da primeira fase do trabalho de construção de narrativas audiovisuais participativas relacionadas com o projeto “Manobras no Porto”, que irá servir de caso de estudo para o projeto de investigação intitulado: “The Museum of All: Práticas de Comunicação Institucional num Mundo de Redes Participativas”. Recorrendo a técnicas de observação participante, nesta primeira fase, o objectivo é mapear o posicionamento dos parceiros do projeto em estudo e identificar as suas ações de dinamização cultural, recolher informações sobre as problemáticas sociais, económicas e culturais da zona de intervenção do projeto e auscultar os públicos intervenientes sobre as prioridades nos seus quotidianos e vivências. Posteriormente, passaremos a uma fase de proposição, com o objectivo de analisar contextos de cooperação, participação e envolvimento das comunidades na criação colectiva de novas mensagens e narrativas, através da implementação de um conjunto de exercícios audiovisuais participativos.

Espectador Activo: Comunicação audiovisual institucional num mundo de redes participativas

Once delighted by the moving image advent as a new method of realistically presenting reality, the viewer has been reposition himself towards the audiovisual contents he consumes, as he is given the opportunity to create and share his own perspective of that reality. We are living in a new technological setting, governed mainly by factors of interactivity, digital systems and technological convergence. The research project that we will present in this paper focuses on the subject of participatory media and the way cultural institutions are increasingly facing the inevitability of a profound revision of their traditional parameters of unidirectional communication, given the increasing availability of tools for audiovisual production as well as the diversity of networked communication contexts. The Serralves Foundation with its Museum of Contemporary Art, in Porto, Portugal, was the subject of a fi rst study of an empirical nature: a series of audiovisual objects were developed, in order to generate material for analysis and proposition. In this new stage of the project, our aim is to identify new procedures and practices that may be effectively implemented within the institutional universe. We intend to propose effi cient audiovisual communication contexts, including the maximizing of the relationship between institutions and audiences regarding dimensions that are traditionally outside the institutional radar: identity, narrative and affection. The project is currently in the process of surveying and categorization, with the aim of producing a map of different vocations and positions of the various institutions in regards to the aforementioned issues, which require participatory communication.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.