Indoor Place Categorization Using Co-occurrences of LBPs in Gray and Depth Images from RGB-D Sensors
EST（2014）
摘要
Indoor place categorization is an important capability for service robots working and interacting in human environments. This paper presents a new place categorization method which uses information about the spatial correlation between the different image modalities provided by RGB-D sensors. Our approach applies co-occurrence histograms of local binary patterns (LBPs) from gray and depth images that correspond to the same indoor scene. The resulting histograms are used as feature vectors in a supervised classifier. Our experimental results show the effectiveness of our method to categorize indoor places using RGB-D cameras.
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关键词
rgb-d,feature vector,lbp,rgb-d camera,gray image,rgb-d sensors,human environment,place categorization, co-lbp, rgb-d,service robots,co-lbp,image sensors,supervised classifier,image classification,indoor place categorization,cameras,cooccurrence histograms,local binary patterns,place categorization,place categorization method,spatial correlation,depth image,indoor scene,image colour analysis,image modality,histograms,sensors,correlation,vectors,support vector machines,robots
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