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Object learning and recognition using human-guided object segmentation and active vision

Zwinderman, M.J. (2009) Object learning and recognition using human-guided object segmentation and active vision. Master's Thesis / Essay, Artificial Intelligence.

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In this thesis, methods are presented that allow a mobile robot equipped with a stereo camera to automatically learn an accurate SURF-keypoint based representation of an arbitrary object. In the approach, a person designates the object to be learned with a laser. By using active vision to filter keypoings, the resulting object representations are robust and recognition time is considerably reduced. The segmentation method was tested on an extensive set of 7 objects, while the creation of object representations and the recognition thereof was tested on a set of 21 objects. The objects vary greatly in size, shape, color and texture. This dataset is considerably larger than those used in similar research. It is shown that by filtering the keypoints using the human segmentation and active vision, the number of keypoints can be greatly reduced while not decreasing recognition accuracy. The recognition was further tested on scenes representing typical office scenarios. It is shown that object recognition works reasonable in highly complex real-world environments, with lighting changes and object occlusions, even when using only one view of the scene.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:28
Last Modified: 15 Feb 2018 07:28

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