IEEE Trans Vis Comput Graph - Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices.

Tópicos

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Resumo

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile Augmented Reality system. However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient and distinctive binary descriptor, called Learning-based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Comparing to existing binary descriptors, LLDB has similar efficiency for descriptor construction, while achieves a greater accuracy and faster matching speed when matching over a large database on mobile devices.

Resumo Limpo

effici robust distinct featur descriptor critic user experi scalabl mobil augment realiti system howev exist descriptor either comput expens achiev realtim perform mobil devic smartphon tablet suffici robust distinct identifi correct match larg databas result current mobil ar system still limit capabl great restrict deploy practic paper propos high effici distinct binari descriptor call learningbas local differ binari lldb lldb direct comput binari string imag patch use simpl intens gradient differ test pairwis grid cell within patch select optim set grid cell pair dens sampl grid cell imag patch leverag modifi adaboost algorithm automat extract small set critic one experiment result demonstr lldb extrem fast comput match larg databas due high robust distinct compar exist binari descriptor lldb similar effici descriptor construct achiev greater accuraci faster match speed match larg databas mobil devic

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