Auflistung nach Schlagwort "semantic segmentation"
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- KonferenzbeitragAI-supported data annotation in the context of UAV-based weed detection in sugar beet fields using Deep Neural Networks(42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Jonas Boysen, Jonas; Stein, AnthonyRecent Deep Learning-based Computer Vision methods proved quite successful in various tasks, also involving the classification, detection and segmentation of crop and weed plants with Convolutional Neural Networks (CNNs). Such solutions require a vast amount of labeled data. The annotation is a tedious and time-consuming task, which often constitutes a limiting factor in the Machine Learning process. In this work, an approach for an annotation pipeline for UAV-based images of sugar beet fields of BBCH-scale 12 to 17 is presented. For the creation of pixel-wise annotated data, we utilize a threshold-based method for the creation of a binary plant mask, a row detection based on Hough Transform and a lightweight CNN for the classification of small, cropped images. Our findings demonstrate that an increased image data annotation efficiency can be reached by using an AI approach already at the crucial Machine Learning-process step of training data collection.
- KonferenzbeitragVisible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Osorio-Roig, Dailé; Rathgeb, Christian; Gomez-Barrero, Marta; Morales-González, Annette; Garea-Llano, Eduardo; Busch, ChristophIris segmentation under visible wavelengths (VWs) is a vital processing step for iris recognition systems operating at-a-distance or in non-cooperative environments. In these scenarios the presence of various artefacts, e.g. occlusions or specular reflections, as well as out-of-focus blur represents a significant challenge. The vast majority of proposed iris segmentation algorithms under VW aim at discriminating the iris and non-iris regions without taking into account the variability that is present in the non-iris region. In this paper, we introduce the idea of segmenting the iris region using a multi-class approach which differentiates additional classes, e.g. pupil or sclera, as opposed to commonly employed bi-class approaches (iris and non-iris). Experimental results conducted on two publicly available databases show that the use of the proposed multi-class approach improves the iris segmentation accuracy. Simultaneously, it also allows for the segmentation of different non-iris regions, e.g. glasses, which could be employed in further application scenarios.