Zobrazeno 1 - 10
of 24
pro vyhledávání: '"M. Dorozynski"'
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-3-2024, Pp 169-177 (2024)
Semantic segmentation is essential in the field of remote sensing because it is used for various applications such as environmental monitoring and land cover classification. Recent advancements aim to collectively classify data from diverse sensors a
Externí odkaz:
https://doaj.org/article/759ee5a8569947448e8a79616d9d748e
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-2024, Pp 107-115 (2024)
Knowledge about land cover is relevant for many different applications such as updating topographic information systems, monitoring the environment, and planning future land cover. Particularly for monitoring, it is of interest to be not only aware o
Externí odkaz:
https://doaj.org/article/35bfab8ab95b4dfab5f4d79b55b474e9
Autor:
M. Dorozynski
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 175-184 (2023)
Collecting knowledge in the form of databases consisting of images and descriptive texts that represent objects from past centuries is a fundamental part of preserving cultural heritage. In this context, images with known information about depicted a
Externí odkaz:
https://doaj.org/article/2085018d76684bb79d6dc32a22d23bd2
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2022, Pp 509-516 (2022)
Automatic detection and tracking of individual animals is important to enhance their welfare and to improve our understanding of their behaviour. Due to methodological difficulties, especially in the context of poultry tracking, it is a challenging t
Externí odkaz:
https://doaj.org/article/2ab4946add354d8d962af192f53fc072
Autor:
M. Dorozynski, F. Rottensteiner
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2022, Pp 777-785 (2022)
Learning from imbalanced class distributions generally leads to a classifier that is not able to distinguish classes with few training examples from the other classes. In the context of cultural heritage, addressing this problem becomes important whe
Externí odkaz:
https://doaj.org/article/197222bb5bc749919f24233a259a3702
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 641-648 (2020)
This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (htt
Externí odkaz:
https://doaj.org/article/b5b38c0ca1a64733a3f658ad3b5ff4da
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-2-W6, Pp 47-54 (2019)
This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the place and time of origin and the production technique. The proposed method was developed in the context of the EU project SI
Externí odkaz:
https://doaj.org/article/4201288703a447f687ef3157d23389ab
Akademický článek
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Conference
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