Fig 2 - uploaded by Ashis Gopal Banerjee
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Persistence images (PIs) for two sample objects. PIs, obtained using height functions in different directions, together have enough information to distinguish between the two objects.
Source publication
Object recognition in unseen indoor environments has been challenging for most state-of-the-art object detectors. To address this challenge, we propose the use of topologically persistent features for object recognition. We extract two kinds of persistent features from binary segmentation maps, namely sparse PI features and amplitude features, by a...
Contexts in source publication
Context 1
... from the corresponding d PDs. Sparse sampling is performed separately for PIs of different directions. For every direction k, corresponding PIs for all training object segmentation maps are vectorized and arranged into columns of a matrix X k N , where N is the number of training object segmentation maps, and k indicates the k th direction. Fig. 2 shows sample PIs generated for different objects using height functions in multiple ...
Context 2
... from the corresponding d PDs. Sparse sampling is performed separately for PIs of different directions. For every direction k, corresponding PIs for all training object segmentation maps are vectorized and arranged into columns of a matrix X k N , where N is the number of training object segmentation maps, and k indicates the k th direction. Fig. 2 shows sample PIs generated for different objects using height functions in multiple ...
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