Nikolaos Doulamis

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Nowadays, there are available extremely large collections of images located on distributed and heterogeneous platforms over the web. The proliferation of billions of shared photos has outpaced the current technology for browsing such collections, but at the same time it spurred the emergence of new image retrieval techniques based not only on photos' visual(More)
— An adaptive algorithm for extracting foreground objects from background in videophone or videoconference applications is presented in this paper. The algorithm uses a neural network architecture that classifies the video frames in regions-of-interest (ROI) and non-ROI areas, also being able to automatically adapt its performance to scene changes. The(More)
Computational Grids are a promising platform for solving large-scale resource intensive problems [1]. The concept of grid computing is gaining popularity in the last decade due to the rapid growth of the Internet as a medium for global communication and the development of faster hardware and more sophisticated software. Grid computing clusters wide variety(More)
An efficient algorithm for humans' retrieval from large video databases is presented in this paper. Such an extraction is very useful for a variety of applications, including video surveillance for security purposes and systems of speaker identification. A human face and body detector is first proposed, based on a simple probabilistic model, to(More)
Grid Infrastructures have been used to solve large scale scientific problems that do not have special requirements on QoS. However, the introduction and success of the Grids in commercial applications as well, entails the provision of QoS mechanisms which will allow for meeting the special requirements of the users-customers. In this paper we present an(More)
In this paper an unsupervised scheme for stereoscopic video object extraction is presented based on a neural network classi-fier. More particularly, the procedure includes: (A) A retraining algorithm for adapting neural network weights to current conditions and (B) An active contour module, which extracts the retraining set. The retraining algorithm takes(More)
A dynamically trained neural network is proposed in this paper proper for adapting the network performance to non stationary image or video inputs. The scheme includes, on one hand, a retrieval mechanism which selects the most appropriate network from the system memory and, on the other hand, a weight perturbation procedure which adapts the network weights(More)
This paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network-genetic algorithm approach. The originality of the work presented in this paper is that it tackles the iron loss reduction problem during the transformer production phase, while previous works were concentrated on the(More)
—Detection of moving objects in videos is a crucial step towards successful surveillance and monitoring applications. A key component for such tasks is usually called background subtraction and tries to extract regions of interest from the image background for further processing or action. For this reason, its accuracy and its real-time performance is of(More)