HUMAN4D: A human-centric multimodal dataset for motions and immersive media

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing vari

On the beneficial effect of noise in vertex localization

A theoretical and experimental analysis related to the effect of noise in the task of vertex identication in unknown shapes is presented. Shapes are seen as real functions of their closed boundary. An alternative global perspective of curvature is examined providing insight into the process of noise- enabled vertex localization. The analysis reveals that noise facilitates in the localization of certain vertices. The concept of noising is thus considered and a relevant global method for localizing Global Vertices is investigated in relation to local methods under the presence of increasing noise. Theoretical analysis reveals that induced noise can indeed help localizing certain vertices if combined with global descriptors. Experiments with noise and a comparison to localized methods validate the theoretical results

Broadcast news parsing using visual cues: a robust face detection approach

Automatic content-based analysis and indexing of broadcast news recordings or digitized news archives is becoming an important tool in the framework of many multimedia interactive services such as news summarization, browsing, retrieval and news-on-demand (NoD) applications. Existing approaches have achieved high performance in such applications but heavily rely on textual cues such as closed caption tokens and teletext transcripts. We present an efficient technique for temporal segmentation and parsing of news recordings based on visual cues that can either be employed as a stand-alone application for non-closed captioned broadcasts or integrated with audio and textual cues of existing systems. The technique involves robust face detection by means of color segmentation, skin color matching and shape processing, and is able to identify typical news instances like anchor persons, reports and outdoor shot

Fuzzy image classification using multiresolution neural networks with applications to remote sensing

Recent progress in supervised image classification research, has demonstrated the potential usefulness of incorporating fuzziness in the training, allocation and testing stages of several classification techniques. In this paper a multiresolu-tion neural network approach to supervised classification is presented, exploiting the inherent fuzziness of such tech-niques in order to perform classification at different resolution levels and gain in computational complexity. In par-ticular, multiresolution image analysis is carried out and hierarchical neural networks are used as an efficient archi-tecture for classification of the derived multiresolution image representations. A new scheme is then introduced for transferring classification results to higher resolutions based on the fuzziness of the results of lower resolutions, re-sulting in faster implementation. Experimental results on land cover mapping applications from remotely sensed data illustrate significant improvements in classification speed without deterioration of representation accuracy. 1