Title:
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Hybridnet for depth estimation and semantic segmentation
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Author:
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Sánchez Escobedo, Dalila; Lin, Xiao; Casas Pla, Josep Ramon; Pardàs Feliu, Montse
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental; Universitat Politècnica de Catalunya. EC - Enginyeria de la Construcció; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
Abstract:
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Abstract:
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Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation, depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network. Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks, the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both. Experimental results demonstrate that HybridNet results are comparable with the state of the art methods, as well as the single task methods that HybridNet is based on. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo -Image processing -Depth estimation -Hybrid convolutional network -Semantic segmentation -Imatges -- Processament |
Rights:
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Document type:
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Article - Published version Conference Object |
Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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