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pro vyhledávání: '"V., Sajith"'
Building Information Modelling (BIM) software use scalable vector formats to enable flexible designing of floor plans in the industry. Floor plans in the architectural domain can come from many sources that may or may not be in scalable vector format
Externí odkaz:
http://arxiv.org/abs/2112.09844
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-M-3-2023, Pp 203-209 (2023)
Deep Learning (DL) networks used in image segmentation tasks must be trained with input images and corresponding masks that identify target features in them. DL networks learn by iteratively adjusting the weights of interconnected layers using backpr
Externí odkaz:
https://doaj.org/article/980c3480bf25476fba976b6da9302268
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-M-3-2023, Pp 219-226 (2023)
The performance of the deep learning-based image segmentation is highly dependent on two major factors as follows: 1) The organization and structure of the architecture used to train the model and 2) The quality of input data used to train the model.
Externí odkaz:
https://doaj.org/article/46768606b0354b1bab75eee6404a4317
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-M-3-2023, Pp 81-85 (2023)
Training Deep Learning (DL) algorithms for segmenting features require hundreds to thousands of input data and corresponding labels. Generating thousands of input images and labels requires considerable resources and time. Hence, it is common practic
Externí odkaz:
https://doaj.org/article/3ed71801d5ff4556b870f16930a13dd2
Akademický článek
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Akademický článek
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Publikováno v:
IEEE Access, Vol 11, Pp 47040-47052 (2023)
Training deep learning-based image segmentation networks require large number of samples of adequate quality. However, obtaining large number of samples is not possible in certain domains. Recent approaches use augmentation and transfer learning tech
Externí odkaz:
https://doaj.org/article/c1bd4373e9d8495cb5c546a87ec2df4d
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-M-2-2022, Pp 91-96 (2022)
Deep learning (DL) algorithms are widely used in object detection such as roads, vehicles, buildings, etc., in aerial images. However, the object detection task is still considered challenging for detecting complex structures, oil pads are one such e
Externí odkaz:
https://doaj.org/article/7a2f89e5581440ee9cfcb6b054eb0ea7
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-M-2-2022, Pp 97-101 (2022)
Deep Learning algorithms are increasingly used for mapping waterbodies in remotely sensed images. DeepLabV3+ is an image segmentation method that includes ASPP and encoder-decoder to retrieve pyramid spatial features at different scales and structura
Externí odkaz:
https://doaj.org/article/bf5d2681c27f4fdea45ae695355319ca
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-M-2-2022, Pp 159-163 (2022)
Dense Residual U-Net (DRU-Net) is a neural network used for image segmentation. It is based on the U-Net architecture and isa combination of modified ResNet as the encoder and modified DenseNet as the decoder blocks. DRU-Net captures both the local a
Externí odkaz:
https://doaj.org/article/51c2e5d329184d16888eecb60fd55aff