This paper proposes an outdoor guide system using vision-based augmented reality(AR) on mobile devices.Augmented reality provides a virtual-real fusion display interface for outdoor guide.Vision-based methods are more...This paper proposes an outdoor guide system using vision-based augmented reality(AR) on mobile devices.Augmented reality provides a virtual-real fusion display interface for outdoor guide.Vision-based methods are more accurate than GPS or other hardware-based methods.However,vision-based methods require more resources and relatively strong computing power of mobile devices.A C/S framework for vision based augmented reality system is introduced in this paper.In a server,a vocabulary tree is used for location recognition.In a mobile device,BRISK feature is combined with optical flow methods to track the offline keyframe.The system is tested on UKbench datasets and in real environment.Experimental results show that the proposed vision-based augmented reality system works well and yields relatively high recognition rate and that the mobile device achieves realtime recognition performance.展开更多
In this paper, motivated by the results in compressive phase retrieval, we study the robustness properties of dimensionality reduction with Gaussian random matrices having arbitrarily erased rows. We first study the r...In this paper, motivated by the results in compressive phase retrieval, we study the robustness properties of dimensionality reduction with Gaussian random matrices having arbitrarily erased rows. We first study the robustness property against erasure for the almost norm preservation property of Gaussian random matrices by obtaining the optimal estimate of the erasure ratio for a small given norm distortion rate. As a consequence, we establish the robustness property of Johnson-Lindenstrauss lemma and the robustness property of restricted isometry property with corruption for Gaussian random matrices. Secondly, we obtain a sharp estimate for the optimal lower and upper bounds of norm distortion rates of Gaussian random matrices under a given erasure ratio. This allows us to establish the strong restricted isometry property with the almost optimal restricted isometry property(RIP) constants, which plays a central role in the study of phaseless compressed sensing. As a byproduct of our results, we also establish the robustness property of Gaussian random finite frames under erasure.展开更多
基金Supported by the National Science and Technology Major Project(No.2012ZX03002004)the National High Technology Research and Development Programme of China(No.2013AA013802)
文摘This paper proposes an outdoor guide system using vision-based augmented reality(AR) on mobile devices.Augmented reality provides a virtual-real fusion display interface for outdoor guide.Vision-based methods are more accurate than GPS or other hardware-based methods.However,vision-based methods require more resources and relatively strong computing power of mobile devices.A C/S framework for vision based augmented reality system is introduced in this paper.In a server,a vocabulary tree is used for location recognition.In a mobile device,BRISK feature is combined with optical flow methods to track the offline keyframe.The system is tested on UKbench datasets and in real environment.Experimental results show that the proposed vision-based augmented reality system works well and yields relatively high recognition rate and that the mobile device achieves realtime recognition performance.
基金supported by Natural Sciences and Engineering Research Council of Canada (Grant No. 05865)Zhiqiang Xu was supported by National Natural Science Foundation of China (Grant Nos. 11422113, 91630203, 11021101 and 11331012)National Basic Research Program of China (973 Program) (Grant No. 2015CB856000)
文摘In this paper, motivated by the results in compressive phase retrieval, we study the robustness properties of dimensionality reduction with Gaussian random matrices having arbitrarily erased rows. We first study the robustness property against erasure for the almost norm preservation property of Gaussian random matrices by obtaining the optimal estimate of the erasure ratio for a small given norm distortion rate. As a consequence, we establish the robustness property of Johnson-Lindenstrauss lemma and the robustness property of restricted isometry property with corruption for Gaussian random matrices. Secondly, we obtain a sharp estimate for the optimal lower and upper bounds of norm distortion rates of Gaussian random matrices under a given erasure ratio. This allows us to establish the strong restricted isometry property with the almost optimal restricted isometry property(RIP) constants, which plays a central role in the study of phaseless compressed sensing. As a byproduct of our results, we also establish the robustness property of Gaussian random finite frames under erasure.
