RT Conference Proceedings
T1 A Framework For TV Logos Learning Using Linear Inverse Diffusion Filters For Noise Removal
A1 Ramos-Cózar, Julián
A1 Zeljković, Vesna
A1 González-Linares, José María
A1 Guil-Mata, Nicolás
A1 Tameze, Claude
A1 Valev, Ventzeslav
K1 Filtros digitales (Matemáticas)
K1 Procesado de señales - Técnicas digitales
AB Different logotypes represent significant cues for video annotations. A combination of temporal and spatial segmentation methods can be used for logo extraction from various video contents. To achieve this segmentation, pixels with low variation of intensity over time are detected. Static backgrounds can become spurious parts of these logos. This paper offers a new way to use several segmentations of logos to learn new logo models from which noise has been removed. First, we group segmented logos of similar appearances into different clusters. Then, a model is learned for each cluster that has a minimum number of members. This is done by applying a linear inverse diffusion filter to all logos in each cluster. Our experiments demonstrate that this filter removes most of the noise that was added to the logo during segmentation and it successfully copes with misclassified logos that have been wrongly added to a cluster.
PB IEEE
YR 2013
FD 2013
LK http://hdl.handle.net/10630/5688
UL http://hdl.handle.net/10630/5688
LA eng
NO Julián R. Cózar, Vesna Zeljković, José Mª González-Linares, Nicolás Guil, Claude Tameze, Ventzeslav Valev, "A Framework For TV Logos Learning Using Linear Inverse Diffusion Filters For Noise Removal", 2013 International Conference on High Performance Computing & Simulation (HPCS 2013), pp. 621-625, 2013
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026