Browsing by Author "Martins, Alexandre"
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- The Faro museum poster collection: demo of a prototype for a digital exhibitPublication . Carrega, Jorge; Mendes da Silva, Bruno; d'Orey, Rui; Martins, AlexandreIn 2022, the CIAC - Center for Arts and Communication Research and the Municipal Museum of Faro initiated a collaboration to study, enrich, and promote a collection of posters that belonged to the Portuguese scenographer Joaquim António Viegas. This partnership includes, among other initiatives, the production of an exhibition, set to take place in 2024, dedicated to early 20th-century Italian cinema posters. Additionally, a group of CIAC researchers is planning to simultaneously inaugurate a virtual exhibition that not only simulates the physical display but also adds exclusive materials and information to the digital version. In this paper, we detail the initial stages of the development of the 3D virtual exhibition. We describe the progress made so far, and this document serves as a record of the exploratory process surrounding this endeavor. Furthermore, we propose to present the current state of the project through a video demonstration, providing a tour of the virtual room and exhibition. The prototype we are currently working on is intended to serve as a model and proof of concept for digital representations and reinterpretations of future exhibitions featuring this unique legacy.
- How i learned to stop worry[in]g and love the big brother: videosurveillance in the domestic placePublication . Martins, AlexandreThis paper briefly describes the Digital MediaArt project How I Learned to Stop Worry[in]g and Love the Big Brother, an interactive installation that allows the public to police a domestic space through a video surveillance system and, simultaneously and remotely, control a set of smart home devices. This artistic artifact aims to take a critical and reflexive look at the gradual loss of privacy in one of its last strongholds: the place we call home.
- Novel cluster modeling for the spatiotemporal analysis of coastal upwellingPublication . Nascimento, Susana; Martins, Alexandre; Relvas, Paulo; Luis, Joaquim; Mirkin, BorisThis work proposes a spatiotemporal clustering approach for the analysis of coastal upwelling from Sea Surface Temperature (SST) grid maps derived from satellite images. The algorithm, Core-Shell clustering, models the upwelling as an evolving cluster whose core points are constant during a certain time window while the shell points move through an in-and-out binary sequence. The least squares minimization of clustering criterion allows to derive key parameters in an automated way. The algorithm is initialized with an extension of Seeded Region Growing offering self-tuning thresholding, the STSEC algorithm, that is able to precisely delineate the upwelling region at each SST instant map. Yet, the application of STSEC to the SST grid maps as temporal data puts the business of finding relatively stable "time windows", here called "time ranges", for obtaining the core clusters onto an automated footing. The experiments conducted with three yearly collections of SST data of the Portuguese coast shown that the core-shell clusters precisely recognize the upwelling regions taking as ground-truth the STSEC segmentations with Kulczynski similarity score values higher than 98%. Also, the extracted time series of upwelling features presented consistent regularities among the three independent upwelling seasons.
- Piece‐wise constant cluster modelling of dynamics of upwelling patternsPublication . Nascimento, Susana; Martins, Alexandre; Relvas, Paulo; Luis, Joaquim; Mirkin, BorisA comprehensive approach is presented to analyse season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. Our three-stage data recovery clustering method assumes that the season's upwelling can be divided into shorter periods of stability, ranges, each to be represented by a constant core and variable shell parts. Corresponding clustering algorithms parameters are automatically derived by using the least-squares clustering criterion. The approach has been successfully applied to real-world SST data covering two distinct regions: Portuguese coast and Morocco coast, for 16 years each.