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- W2945367781 abstract "In this paper, oblique airborne images with very high resolution are used to address the problem from aerial views in urban areas. Traditional classification method (i.e., random forests) is compared with state-of-the-art fully convolutional networks (FCNs). Random forests use hand-craft image features including red, green, blue (RGB), scale-invariant feature transform (SIFT), and Texton, and point cloud features consisting of normal vector and planarity extracted from different scales. In contrast, the inputs of FCNs are the RGB bands and the third components of normal vectors. In both cases, three-dimensional (3D) features are projected back into the image space to support the facade interpretation. Fully connected conditional random field (CRF) is finally taken as a post-processing of the FCN to refine the segmentation results. Several tests have been performed and the achieved results show that the models embedding the 3D component outperform the solution using only images. FCNs significantly outperformed random forests, especially for the balcony delineation." @default.
- W2945367781 created "2019-05-29" @default.
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- W2945367781 date "2019-06-01" @default.
- W2945367781 modified "2023-09-26" @default.
- W2945367781 title "Semantic Façade Segmentation from Airborne Oblique Images" @default.
- W2945367781 doi "https://doi.org/10.14358/pers.85.6.425" @default.
- W2945367781 hasPublicationYear "2019" @default.
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