Agostini, Alejandro

[["dc.bibliographiccitation.firstpage","44"],["dc.bibliographiccitation.journal","Artificial Intelligence"],["dc.bibliographiccitation.lastpage","65"],["dc.bibliographiccitation.volume","274"],["dc.contributor.author","Lüddecke, Timo"],["dc.contributor.author","Agostini, Alejandro"],["dc.contributor.author","Fauth, Michael"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2019-07-15T08:04:50Z"],["dc.date.available","2019-07-15T08:04:50Z"],["dc.date.issued","2019"],["dc.description.abstract","The distributional hypothesis states that the meaning of a concept is defined through the contexts it occurs in. In practice, often word co-occurrence and proximity are analyzed in text corpora for a given word to obtain a real-valued semantic word vector, which is taken to (at least partially) encode the meaning of this word. Here we transfer this idea from text to images, where pre-assigned labels of other objects or activations of convolutional neural networks serve as context. We propose a simple algorithm that extracts and processes object contexts from an image database and yields semantic vectors for objects. We show empirically that these representations exhibit on par performance with state-of-the-art distributional models over a set of conventional objects. For this we employ well-known word benchmarks in addition to a newly proposed object-centric benchmark."],["dc.identifier.doi","10.1016/j.artint.2018.12.009"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16274"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61490"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/731761/EU//IMAGINE"],["dc.relation.issn","0004-3702"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0/"],["dc.title","Distributional semantics of objects in visual scenes in comparison to text"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","312"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Freudenberg, Maximilian"],["dc.contributor.author","Nölke, Nils"],["dc.contributor.author","Urban, Kira"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Kleinn, Christoph"],["dc.contributor.author","Agostini, Alejandro"],["dc.date.accessioned","2019-07-09T11:50:04Z"],["dc.date.available","2019-07-09T11:50:04Z"],["dc.date.issued","2019"],["dc.description.abstract","Oil and coconut palm trees are important crops in many tropical countries, which are either planted as plantations or scattered in the landscape. Monitoring in terms of counting provides useful information for various stakeholders. Most of the existing monitoring methods are based on spectral profiles or simple neural networks and either fall short in terms of accuracy or speed. We use a neural network of the U-Net type in order to detect oil and coconut palms on very high resolution satellite images. The method is applied to two different study areas: (1) large monoculture oil palm plantations in Jambi, Indonesia, and (2) coconut palms in the Bengaluru Metropolitan Region in India. The results show that the proposed method reaches a performance comparable to state of the art approaches, while being about one order of magnitude faster. We reach a maximum throughput of 235 ha/s with a spatial image resolution of 40 cm. The proposed method proves to be reliable even under difficult conditions, such as shadows or urban areas, and can easily be transferred from one region to another. The method detected palms with accuracies between 89% and 92%."],["dc.description.sponsorship","Open-Acces-Publikationsfonds 2019"],["dc.identifier.doi","10.3390/rs11030312"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15850"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59694"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.relation","SFB 990: Ökologische und sozioökonomische Funktionen tropischer Tieflandregenwald-Transformationssysteme (Sumatra, Indonesien)"],["dc.relation","SFB 990 | B | B05: Land use patterns in Jambi - quantification of structure, heterogeneity and changes of vegetation and land use as a basis for the explanation of ecological and socioeconomic functions"],["dc.relation.eissn","2072-4292"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.subject.ddc","570"],["dc.subject.gro","sfb990_journalarticles"],["dc.title","Large Scale Palm Tree Detection In High Resolution Satellite Images Using U-Net"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","420"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Robotics and Autonomous Systems"],["dc.bibliographiccitation.lastpage","432"],["dc.bibliographiccitation.volume","57"],["dc.contributor.author","Wörgötter, F."],["dc.contributor.author","Agostini, A."],["dc.contributor.author","Krueger, N."],["dc.contributor.author","Shylo, N."],["dc.contributor.author","Porr, B."],["dc.date.accessioned","2018-11-07T08:30:39Z"],["dc.date.available","2018-11-07T08:30:39Z"],["dc.date.issued","2009"],["dc.description.abstract","Embodied cognition suggests that complex cognitive traits can only arise when agents have a body situated in the world. The aspects of embodiment and situatedness are being discussed here from the perspective of linear systems theory. This perspective treats bodies as dynamic, temporally variable entities, which can be extended (or curtailed) at their boundaries. We show how acting agents can, for example, actively extend their body for some time by incorporating predictably behaving parts of the world and how this affects the transfer functions. We suggest chat primates have mastered this to a large degree increasingly splitting their world into predictable and unpredictable entities. we argue that temporary body extension may have been instrumental in paving the way for the development of higher cognitive complexity as it is reliably widening the cause-effect horizon about the actions of the agent. A first robot experiment is sketched to support these ideas. We continue discussing the concept of Object-Action Complexes (OACs) introduced by the European PACO-PLUS consortium to emphasize the notion that, for a cognitive agent. objects and actions are inseparably intertwined. In another robot experiment we devise a semi-supervised procedure using the OAC-concept to demonstrate how an agent can acquire knowledge about its world. Here the notion of predicting changes fundamentally underlies the implemented procedure and we try to show how this concept can be used to improve the robot's inner model and behaviour. Hence. in this article we have tried to show how predictability can be used to augment the agent's body and to acquire knowledge about the external world, possibly leading to more advanced cognitive traits. (c) 2008 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.robot.2008.06.011"],["dc.identifier.isi","000264894400007"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/16943"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","0921-8890"],["dc.title","Cognitive agents - a procedural perspective relying on the predictability of Object-Action-Complexes (OACs)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]