A markerless augmented reality mobile navigation system with multiple targets display function

Wang, Ching-Sheng; Hung, Shih-Hui; Chiang, Ding-Jung

doi:10.1109/icasi.2017.7988439

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pombo et al [16] designed an interactive application on MAR to recognize activities of geocaching in outdoor environments. A mobile navigation system is proposed in [17], to present the display function of markerless augmented reality which is able to support the multiple targets. Zhang [18] designed a framework for MAR game using image recognition techniques.…”

Section: Augmented Reality Based Researchmentioning

confidence: 99%

A Novel Lightweight Approach for Video Retrieval on Mobile Augmented Reality Environment

et al. 2018

View full text Add to dashboard Cite

Mobile Augmented Reality merges the virtual objects with real world on mobile devices, while video retrieval brings out the similar looking videos from the large-scale video dataset. Since mobile augmented reality application demands the real-time interaction and operation, we need to process and interact in real-time. Furthermore, augmented reality based virtual objects can be poorly textured. In order to resolve the above mentioned issues, in this research, we propose a novel, fast and robust approach for retrieving videos on the mobile augmented reality environment using an image and video queries. In the beginning, Top-K key-frames are extracted from the videos which significantly increases the efficiency. Secondly, we introduce a novel frame based feature extraction method, namely Pyramid Ternary Histogram of Oriented Gradient (PTHOG) to extract the shape feature from the virtual objects in an effective and efficient manner. Thirdly, we utilize the Double-Bit Quantization (DBQ) based hashing to accomplish the nearest neighbor search efficiently, which produce the candidate list of videos. Lastly, the similarity measure is performed to re-rank the videos which are obtained from the candidate list. An extensive experimental analysis is performed in order to verify our claims.

show abstract

Section: Augmented Reality Based Researchmentioning

confidence: 99%