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Indoor environment classification, also known as indoor environment recognition, is a highly appreciated perceptual ability in mobile robots. In this paper, we present a novel approach which is centered on biologically inspired methods for recognition and representation of indoor environments. First, global visual features are extracted by using the GIST descriptor, and then we use the subsequent...
Outlier and anomaly detection are widely used in several fields of study such as social networks, statistics, and knowledge discovery. In social networks, it is useful to detect structural abnormalities which are different from the typical behavior of the social network in order to maintain the network security and privacy. In this paper, we suggest a new approach for outlier and anomalous behavior...
We present in this paper a real-time method for visual categorization to do robot grasping. We describe an object database with SURF feature points which we quantify with the Kmeans clustering algorithm to make visual words. Then, we train a Support Vector Machine classifier having as entries the distribution of the bag of features extracted earlier. Likewise, we do object recognition using the SVM...
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