Due to the evolution of technologies and methods for realistic 3D reconstruction of objects, in many projects it can be found efficient ways to make research results in digital preservation available on the Internet. 3D visualization of cultural heritage is highlighted in this scenario, helping to expand research activities in this field by providing proper tools to allow for example, remote access to historical artifacts. Thus, visualization systems must be able to handle important aspects in the context of digital preservation, such as user profiles, security and ease access to 3D models. This paper presents the development of an effective web-based 3D visualization system whose architecture offers an easy and fast interactivity with 3D models even when limited computer resources are available. The system has been successfully adopted in developing of 3D Virtual Museums in the Universidade Federal do Parana (UFPR) in Brazil, providing an important tool to promote research, educational, social and cultural activities
Computer theft in computer labs causes academic damage to coursesthat require this resource and ends up directly harming students. Inthis context, this paper describes a methodology applied to detectcomputer removal through video analysis in real-time. For eachframe, image processing and computer vision techniques were used,subtracting background information, binarization, segmentationof the region of interest and definition of contours. The case studywas developed at a Brazilian university. For theft detection, it wasconsidered a black computer tower case carried by people leavingthe laboratory. Monitoring is carried out by a camera positioned infront of the lab exit door. The software developed alerts a suspiciousactivity that may indicate a possible computer theft.
Parking lots are no longer practical solutions but become anothertopic of urban mobility problem due to the difficulty in finding availableparking spaces. This work proposes a parking space detectionsystem to assist drivers. The system detects unoccupied vacanciesby image processing techniques and convolutional neural networks.Vacancies are detected through horizontal markings and by recognizingspaces with or without vehicles. Finally, a mobile applicationmakes available to the user the occupancy status of vacancies. Initialresults showed that the system detects vacancies with visiblemarkings during the daytime. To improve detection in adversesituations, the vacancy detection algorithm is being improved.
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