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This paper presents a novel method using accelerated KAZE (AKAZE) and Gist for a context-based semantic classification and recognition of indoor scenes used for a vision-based mobile robot. Our method represents spatial relations among categories for mapping neighborhood units on category maps using counter propagation networks (CPNs) while maintaining sequential information of labels generated from...
This study was conducted to create driving episodes using machine-learning-based algorithms that address long-term memory (LTM) and topological mapping. This paper presents a novel episodic memory model for driving safety according to traffic scenes. The model incorporates three important features: adaptive resonance theory (ART), which learns time-series features incrementally while maintaining stability...
This paper presents a novel object extraction method using a micro air vehicle (MAV) for improving the robustness of occlusion. The proposed method is based on saliency of objects for extracting regions of interest (RoIs) using scale invariant feature transform (SIFT) features and segmentation of target objects using GrabCut, which requires advance learning. We obtained original aerial photographic...
This paper presents an unsupervised scene classification method based on context of features for semantic recognition of indoor scenes used for an autonomous mobile robot. Our method creates Visual Words (VWs) of two types using Scale-Invariant Feature Transform (SIFT) and Gist. Using the combination of VWs, our method creates Bags of VWs (BoVWs) to vote to a two-dimensional histogram as context-based...
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