Browsing by Keyword "semantic segmentation"
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Item Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles(Institute of Electrical and Electronics Engineers Inc., 2023) Manibardo, Eric L.; Lana, Ibai; Del Ser, Javier; Carballo, Alexander; Takeda, Kazuya; IASemantic segmentation aims at assigning labels to every pixel of a given image. In the context of autonomous vehicles, semantic segmentation models should be trained with data collected from the traffic network through which vehicles are expected to circulate. Road regulation, weather conditions, and other context features may differ between regions, making local semantic segmentation datasets extremely valuable. However, the high ground truth annotation costs represent a hindrance to the development of such models. The upsurge of powerful feature learning architectures leaves room for semantic segmentation models trained on an unsupervised fashion. This observation vertebrates the purpose of this work: to produce coarse segmentation maps for scene understanding without the need of annotated data. We depart from an unsupervised model that yields low-quality results. The proposed methodology establishes a set of guidelines for the enhancement of segmentation maps. Obtained results expose an improvement of the segmentation quality thanks to the application of our devised guidelines, paving the way for the automatic generation of semantic segmentation datasets.Item MVMO: A MULTI-OBJECT DATASET FOR WIDE BASELINE MULTI-VIEW SEMANTIC SEGMENTATION(IEEE Computer Society, 2022) Alvarez-Gila, Aitor; van de Weijer, Joost; Wang, Yaxing; Garrote, Estibaliz; VISUAL; QuantumWe present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116, 000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups.