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Additional resources for Agents and Artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers
If this is given, the algorithm is able to improve the error rate by calculating optimal weight for each weak classifier. The output of the nth weak classifier to the input x is yn = hn (x). If every yn is weighted with an αn which is created during the training process, the strong classifier is given by: N αn hn (x) H (x) = sign n=1 (1) AdaBoost Based Door Detection 41 Fig. 2. Characterizing features of doors 3 Detection of Door Features Our approach is illustrated in figure 3. As mentioned before, we use the robot’s camera image and the laser-based distance signal for the door detection.
As shown in figure 1, doors can have different poses, lighting situations, reflections, as well as completely different features. The main features of a door are illustrated in figure 2. g. by its color or texture with respect to the color or texture of the surrounding wall. Door gaps or door knobs are indicators of a door, too. Even if some of these features are detected in a single camera image, a robust algorithm should detect the door by using the remaining features. In recent work , these two issues of door detection were solved by extracting several door features from the robots camera images and applying the AdaBoost algorithm .
References over the xmark database (from ) 3 Case Study: Mining Internet Auction Data This section discusses a concrete usage of XQuake to mine an artificial XML dataset modeling an Internet auction site. 1 Description of the Database The aim of the xmark project2 is to provide a benchmark suite that allows users and developers to gain insights into the characteristics of their XML repositories. The structure of the document is modeled after a database as deployed by an Internet auction site.