MAR shopping assistant usage: Delay, error, and utility

Ganapathy, Subhashini; Anderson, Glen J.; Kozintsev, I.

doi:10.1109/vr.2011.5759471

Cited by 13 publications

(3 citation statements)

References 3 publications

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“…As a result, the processing time and transmission time both increase. However, users are unlikely to accept too long a waiting time [5][6] [7]. In addition, a longer waiting time can cause a user to become anxious and move the camera to an inappropriate position and capture irrelevant content that will not match one of the predefined images and then exacerbates the problem.…”

Section: Introductionmentioning

confidence: 99%

Quality of experience-based image feature selection for mobile augmented reality applications

Cao

Ritz

Raad

2014

2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS)

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Mobile augmented reality applications rely on automatically recognising a visual scene through matching of derived image features. To ensure the Quality of Experience (QoE) perceived by users, such applications should achieve high matching accuracy meanwhile minimizing the waiting time to meet real-time requirement. An efficient solution is to develop an effective feature selection method to select the most robust features against distortions caused by camera capture to achieve high matching accuracy whilst transmission and matching process of the features are significant reduced. Feature selection is also beneficial to reducing the computational complexities of the matching system so that waiting time is minimized and hence user QoE is maximised. In this paper, a QoE estimation for state-of-the-art feature selection in MPEG-7 CDVS based on waiting time and matching accuracy as judged by retrieval experiments on a realistic image dataset with realworld distortions caused by image capture is analysed. The predicted QoE results suggest that feature selection can provide good QoE to users. Abstract-Mobile augmented reality applications rely on automatically recognising a visual scene through matching of derived image features. To ensure the Quality of Experience (QoE) perceived by users, such applications should achieve high matching accuracy meanwhile minimizing the waiting time to meet real-time requirement. An efficient solution is to develop an effective feature selection method to select the most robust features against distortions caused by camera capture to achieve high matching accuracy whilst transmission and matching process of the features are significant reduced. Feature selection is also beneficial to reducing the computational complexities of the matching system so that waiting time is minimized and hence user QoE is maximised. In this paper, a QoE estimation for state-of-the-art feature selection in MPEG-7 CDVS based on waiting time and matching accuracy as judged by retrieval experiments on a realistic image dataset with real-world distortions caused by image capture is analysed. The predicted QoE results suggest that feature selection can provide good QoE to users.

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Section: Introductionmentioning

confidence: 99%

Quality of experience-based image feature selection for mobile augmented reality applications

Cao

Ritz

Raad

2014

2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS)

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“…images in a newspaper or magazine) [1] [2] or Augmented Reality applications [3] where users scan across images with their mobile device camera to receive related content must minimize system latency, this is defined as the total time between image capture on the device and matching result returned from the server. To minimize system latency, both processing time on the mobile device and transmission time to a server of the resulting image information must be minimized.…”

Section: Introductionmentioning

confidence: 99%

“…Hence the key challenge is how to minimize the transmission of visual information and reduce processing in the mobile device whilst achieving realtime and highly accurate retrieval against distortion. This has previously been shown to be essential to maximizing user good Quality of Experience (QoE) for such applications [1] [2][5] [6].…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate low bit rate retrieval-by-capture applications

Cao

Ritz

Raad

2014

2014 19th International Conference on Digital Signal Processing

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Retrieval-by-capture applications allow a user to scan a printed picture in a newspaper or magazine with a mobile camera to then automatically trigger the display of predetermined related content. To ensure a high user Quality of Experience (QoE), this paper proposes a fast and accurate low bit rate solution for retrieval-by-capture applications based on extracting Scale Invariant Feature Transform (SIFT) features from images reconstructed from the low spatial frequency components. The system applies a two-dimensional block-based Discrete Cosine Transform (DCT), as typically used in image coders, and only encodes and transmits the resulting DC components. Received images are decoded using these components and SIFT features are derived and then used to find a matching image within a database of candidate images. Results show that the proposed system achieves more than 97% matching accuracy when evaluated for a wide range of typical image distortions including scaling, rotation, additive noise, image blurring and illumination. Transmission data rates are comparable to existing compressed domain image features whilst significantly reducing system latency.

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Design Guidelines for Mobile Augmented Reality: User Experience

Ganapathy

2012

Human Factors in Augmented Reality Environments

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MAR shopping assistant usage: Delay, error, and utility

Abstract: EXTENDED ABSTRACTThis poster will present findings from a study of a shopping assistant prototype with simulated augmented reality information.

Cited by 13 publications

References 3 publications

Quality of experience-based image feature selection for mobile augmented reality applications

Quality of experience-based image feature selection for mobile augmented reality applications

Fast and accurate low bit rate retrieval-by-capture applications

Design Guidelines for Mobile Augmented Reality: User Experience

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