Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551571
Title: Object recognition with local features
Author: Alachkar, Bassem
Awarding Body: University of Salford
Current Institution: University of Salford
Date of Award: 2012
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Abstract:
Object recognition is a very challenging problem particularly for mobile robot applications such as service robots. The challenges are due to the variation of the point of view and imaging conditions which may involve occlusion of the object. The suitable approach in our case is the local features-based approach. The methods used in this approach include two stages; the first is the extraction of local features (also called interest points), and the second is the matching operation stage. Scale Invariant Feature Transform (SIFT) is recognised as one of the most robust and efficient algorithms for the extraction of local features. We adopt the SIFT algorithm for the determination of the keypoints used in the matching stage for object detection and pose estimation. The focus of our work is mainly on the matching stage, analysing the different techniques used, and proposing some improvements, taking into consideration the balance between reliability and efficiency required in mobile robot object recognition system. Both sources of information on the disposition of the keypoints are used in the matching operation; the relative positions of the keypoints in the descriptors space and the geometric information about their positions in the image. We use a simplified method of object recognition and pose estimation that is based on robust selection of matching keypoints exploiting in addition to the descriptors matching the geometric information on the keypoints. Clusters of reliably selected matched keypoints are used in object pose estimation using the Least Squares Method. Analysis and experimental results on different points of the matching operation are presented.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.551571  DOI: Not available
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