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Article identification for inventory list in a warehouse environment
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
2014 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

In this paper, an object recognition system has been developed that uses local image features. In the system, multiple classes of objects can be recognized in an image. This system is basically divided into two parts: object detection and object identification. Object detection is based on SIFT features, which are invariant to image illumination, scaling and rotation. SIFT features extracted from a test image are used to perform a reliable matching between a database of SIFT features from known object images. Method of DBSCAN clustering is used for multiple object detection. RANSAC method is used for decreasing the amount of false detection. Object identification is based on 'Bag-of-Words' model. The 'BoW' model is a method based on vector quantization of SIFT descriptors of image patches. In this model, K-means clustering and Support Vector Machine (SVM) classification method are applied.

sted, utgiver, år, opplag, sider
2014. , s. 57
Emneord [en]
Object recognition, SIFT feature, Feature matching, DBSCAN, RANSAC, Bag of Words
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-27132Lokal ID: IDE1407OAI: oai:DiVA.org:hh-27132DiVA, id: diva2:766369
Fag / kurs
Computer science and engineering
Presentation
(engelsk)
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Examiner
Tilgjengelig fra: 2014-12-01 Laget: 2014-11-26 Sist oppdatert: 2014-12-01bibliografisk kontrollert

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