| Summary: | 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 100 === One of the key interests in the field of object recognition is to recognize tools or objects human subjects are using while performing certain tasks. However, for smaller objects such as stationeries or other desk objects, recognition tasks can be challenging, as these objects can be mostly or fully occluded by human hands during manipulation. In this thesis, we have showed that it is possible to make inferences of the objects occluded during a hand-object interaction by observing the shape of the occluding hands. This is done by calculating Fast Point Feature Histograms (FPFHs) for points sampled from the input point cloud clusters, applying Support Vector Machine (SVM) based training and testing to determine points which have high confidence of being related to a certain object, and a scoring system to determine final decisions. Experiments done on our 3750-frame dataset showed a recognition accuracy of 93.61% by using the proposed framework.
|
|---|
