Article identification for inventory list in a warehouse environment
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
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.
Place, publisher, year, edition, pages
2014. , 57 p.
Object recognition, SIFT feature, Feature matching, DBSCAN, RANSAC, Bag of Words
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:hh:diva-27132Local ID: IDE1407OAI: oai:DiVA.org:hh-27132DiVA: diva2:766369
Subject / course
Computer science and engineering
Åstrand, Björn, AdjunktShahbandi, Saeed Gholami, PhD
Verikas, Antanas, Professor