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- W2904266972 abstract "For this study, we focus on autonomous locomotion based on visual landmarks that recognizes surrounding environments based on saliency characteristics. This paper presents a feature extraction method combined with saliency maps (SMs), histograms of oriented gradients (HOG) features, and accelerated KAZE (AKAZE) descriptors to describe image features as visual landmarks without removing human regions as dynamic objects. As semantic scene recognition, we used a method combined with self-organizing maps (SOMs) based on bag of features for creating codebooks as visual words and counter propagation networks (CPNs) based on topological learning of neighborhood and competition for creating a category maps (CMs) that converts input features into a low dimensional space. We used a mobile robot for obtaining clockwise datasets (CWDs) and counter CW datasets (CCWDs). The experimental obtained results revealed that recognition accuracies (RAs) for CWDs and CCWDs, were, respectively 70.76% for 26 categories and 72.24% for 25 categories. Based on this result as an original ground truth (GT) pattern, we change label patterns (LPs) of five types according to mapping results on CMs for selection." @default.
- W2904266972 created "2018-12-22" @default.
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- W2904266972 date "2018-10-01" @default.
- W2904266972 modified "2023-09-23" @default.
- W2904266972 title "Semantic Scene Recognition and Zone Labeling for Mobile Robot Benchmark Datasets based on Category Maps" @default.
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