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Auflistung nach Schlagwort "segmentation"

1 - 3 von 3
  • Konferenzbeitrag
    Farmers’ attitudes towards data security in agriculture when using digital technologies
    (43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Gabriel, Andreas
    This article explores the question of which farmers are aware and also trust the current data protection guidelines for the sharing of agricultural data when using digital technologies. A decision tree analysis was used to identify the characteristics of group segments that differ in their attitudes (awareness, confidence) towards current data protection guidelines. Although a large proportion of respondents said they were aware of the current data protection guidelines, most farmers are skeptical and do not trust their effectiveness. Three segments were identified that are more open to monetary or information-based compensations in return for sharing their farm data with third parties.
  • Konferenzbeitrag
    Mapping invasive Lupine on grasslands using UAV images and deep learning
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Wijesingha, Jayan; Schulze-Brüninghoff, Damian; Wachendorf, Michael
    Semi-natural grasslands are threatened by invasive species. This study employs high-resolution images captured by an unmanned aerial vehicle (UAV) and deep learning techniques to map Lupine (Lupinus polyphyllus Lindl.) in grasslands, which is one of the most common invasive species in European grasslands. The methodology involves RGB image acquisition, structure from motion processing, canopy height modelling, and deep learning semantic segmentation model development. The resulting models were trained on RGB data, canopy surface height data, and their combination. The models demonstrate high accuracy and efficacy in identifying Lupine distribution. These models offer a valuable tool for continuously monitoring and managing invasive Lupine, with potential applications in similar environments without retraining. The method is beneficial for early-stage invasion detection, facilitating more targeted management efforts for ecologists.
  • Konferenzbeitrag
    Robust Clustering-based Segmentation Methods for Fingerprint Recognition
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Ferreira, Pedro M.; Sequeira, Ana F.; Cardoso, Jaime S.; Rebelo, Ana
    Fingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option - a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures.