Object Recognition Using Multiple Viewpoints
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2016
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
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Reihe / Serie
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Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
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Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Advances in artificial intelligence and computer vision have sparked the emergence of service robots which are intended to work for, and alongside humans in a dynamic environment. One of the biggest challenges besides manipulation of objects and interaction with the environment is cognition. While vision is natural and does not require much conscious thought for most humans, it is still a complex task for computers. However, recent years have shown a lot of development in areas such as detection, recognition and categorization of objects as well as scene recognition. A well tested and often used object recognition approach is feature matching. Feature matching is built upon the extraction of distinctive features of an object to recognize it again in a different image and context. This thesis applies feature matching to the area of service robots, following the use case of a mobile object recognition robot capable of remembering and identifying specific items. A prototype using the Lego Mindstorms EV3 robotics toolkit, an Android smartphone, and the ORB feature matching algorithm provided by the computer vision library OpenCV is proposed and developed. The developed object recognition robot is capable of taking pictures of an object from multiple viewpoints. The information gathered from these viewpoints is then combined using a newly created confidence estimation based on the average Hamming distance of the 100 best matches found between two images to estimate whether the reference object is present or not.The system performs well on the test data set and manages to increase recognition precision by 11% when compared to a single image version of the same algorithm. However, the performance could not be confirmed with the evaluation data set. Analysis has shown, that the proposed approach only works well in combination with images that have strong distinctive features such as text. If such strong distinctive features are missing, the algorithm produces a lot of false positive results and thus delivers an overall unacceptably low accuracy rate.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
HUNKELER, Iris, 2016. Object Recognition Using Multiple Viewpoints. Olten: Hochschule für Wirtschaft FHNW. Verfügbar unter: https://irf.fhnw.ch/handle/11654/39864