This article based on the current robot to achieve the identification of objects exist a number of issues to design. In order to achieve the robots in the family scene to identify specific objects. Thus verifying its feasibility and practicability.Based on SURF algorithm and SVM classifier to extract local features and training, this paper proposes a PCA algorithm and Bag-of-Visual-Word algorithm to reduce the dimensionality and clustering of extracted features to facilitate SVM training while improving recognition accuracy and reducing computation time. At the same time using multi-view and Image Pyramid segmentation method to solve the occlusion and complex background recognition.All experiments were performed using the Webots robotics development platform and the OpenCV library.Experimental results show that the above method can ensure the real-time performance while ensuring the accuracy of recognition. It has a certain feasibility and practical value.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.