{"id":{"repo_id":"bielefeld","oai_identifier":"oai:pub.uni-bielefeld.de:2994291"},"canonical_url":"https://search.dev.ndltd.org/etd/bielefeld/oai:pub.uni-bielefeld.de:2994291","repository":{"repo_id":"bielefeld","name":"Universität Bielefeld","base_url":"https://pub.uni-bielefeld.de/oai"},"display":{"title":"Computer vision with limited domain knowledge and annotations. Reducing domain expert effort from image acquisition to interpretation","abstract":"This thesis explores methods in machine learning and computer vision to extract meaningful semantic information from images, with a primary focus on marine images, while addressing challenges associated with limited prior knowledge and annotation constraints. Traditional deep learning approaches in computer vision—such as image classification, object detection, and semantic segmentation—have achieved impressive results but depend heavily on large datasets of labeled images and predefined class information. However, marine imaging often lacks these resources, as species in remote marine environments are not always known in advance, and annotated images are scarce due to limited access to trained experts. This work develops and evaluates novel techniques to address these gaps, aiming to reduce the need for domain specific labeled training data and the effort required to obtain it.","abstract_html":"This thesis explores methods in machine learning and computer vision to extract meaningful semantic information from images, with a primary focus on marine images, while addressing challenges associated with limited prior knowledge and annotation constraints. Traditional deep learning approaches in computer vision—such as image classification, object detection, and semantic segmentation—have achieved impressive results but depend heavily on large datasets of labeled images and predefined class information. However, marine imaging often lacks these resources, as species in remote marine environments are not always known in advance, and annotated images are scarce due to limited access to trained experts. This work develops and evaluates novel techniques to address these gaps, aiming to reduce the need for domain specific labeled training data and the effort required to obtain it.","abstract_has_math":false,"creators":["Möller, Torben"],"institution":"Universität Bielefeld","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-18","date_published":"2024-07-18","updated_at":"2026-07-27T18:50:01Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://pub.uni-bielefeld.de/record/2994291","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Möller, Torben"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universitätsbibliothek Bielefeld"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Universität Bielefeld"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis explores methods in machine learning and computer vision to extract meaningful semantic information from images, with a primary focus on marine images, while addressing challenges associated with limited prior knowledge and annotation constraints. Traditional deep learning approaches in computer vision—such as image classification, object detection, and semantic segmentation—have achieved impressive results but depend heavily on large datasets of labeled images and predefined class information. However, marine imaging often lacks these resources, as species in remote marine environments are not always known in advance, and annotated images are scarce due to limited access to trained experts. This work develops and evaluates novel techniques to address these gaps, aiming to reduce the need for domain specific labeled training data and the effort required to obtain it."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Computer vision with limited domain knowledge and annotations. Reducing domain expert effort from image acquisition to interpretation"]}]}],"canonical_facts":{"dc:creator":["Möller, Torben"],"dc:description.abstract":["This thesis explores methods in machine learning and computer vision to extract meaningful semantic information from images, with a primary focus on marine images, while addressing challenges associated with limited prior knowledge and annotation constraints. Traditional deep learning approaches in computer vision—such as image classification, object detection, and semantic segmentation—have achieved impressive results but depend heavily on large datasets of labeled images and predefined class information. However, marine imaging often lacks these resources, as species in remote marine environments are not always known in advance, and annotated images are scarce due to limited access to trained experts. This work develops and evaluates novel techniques to address these gaps, aiming to reduce the need for domain specific labeled training data and the effort required to obtain it."],"dc:format.medium":["application/pdf"],"dc:publisher":["Universitätsbibliothek Bielefeld"],"dc:title":["Computer vision with limited domain knowledge and annotations. Reducing domain expert effort from image acquisition to interpretation"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Bielefeld"]},"updated_at":"2026-07-27T18:50:01Z"}