Image-Searching for Office Equipment Using Bag-of-Keypoints and AdaBoost
Seiji Aoyagi, Atsushi Kohama, Yuki Inaura,
Masato Suzuki, and Tomokazu Takahashi
Faculty of Engineering, Kansai University, 3-3-35 Yamate-cho, Suita, Osaka 564-8680, Japan
For an indoor mobile robot’s Simultaneous Localization And Mapping (SLAM), a method of processing only one monocular image (640×480 pixel) of the environment is proposed. This method imitates a human’s ability to grasp at a glance the overall situation of a room, i.e., its layout and any objects or obstacles in it. Specific object recognition of a desk through the use of several camera angles is dealt with as one example. The proposed method has the following steps. 1) The bag-of-keypoints method is applied to the image to detect the existence of the object in the input image. 2) If the existence of the object is verified, the angle of the object is further detected using the bag-ofkeypoints method. 3) The candidates for the projection from template image to input image are obtained using Scale Invariant Feature Transform (SIFT) or edge information. Whether or not the projected area correctly corresponds to the object is checked using the AdaBoost classifier, based on various image features such as Haar-like features. Through these steps, the desk is eventually extractedwith angle information if it exists in the image.
Masato Suzuki, and Tomokazu Takahashi, “Image-Searching for Office Equipment Using Bag-of-Keypoints and AdaBoost,” J. Robot. Mechatron., Vol.23, No.6, pp. 1080-1090, 2011.
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