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Improving image segmentation with boundary patch refinement / Xiaolin Hu in International journal of computer vision, vol 130 n° 11 (November 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 11 (November 2022) . - pp 2571 - 2589 Titre : Improving image segmentation with boundary patch refinement Type de document : Article/Communication Auteurs : Xiaolin Hu, Auteur ; Chufeng Tang, Auteur ; Hang Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2571 - 2589 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] détection de contours [Termes IGN] distance euclidienne [Termes IGN] masque [Termes IGN] segmentation d'image [Termes IGN] segmentation fondée sur les contours [Termes IGN] segmentation sémantique Résumé : (auteur) Tremendous efforts have been made on image segmentation but the mask quality is still not satisfactory. The boundaries of predicted masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework, termed BPR, to improve the boundary quality of the prediction of any image segmentation model. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted boundaries. The refinement is accomplished by a boundary patch refinement network at the higher resolution. The trained BPR model can be easily transferred to refine the results of other models as well. Extensive experiments show that the proposed BPR framework yields significant improvements on the semantic, instance, and panoptic segmentation tasks over a variety of baselines on the Cityscapes dataset. Numéro de notice : A2022-741 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01662-0 Date de publication en ligne : 12/08/2022 En ligne : https://doi.org/10.1007/s11263-022-01662-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101719 [article]