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Photorealistic Learned Landscapes for Augmented Reality

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Interframe coding of feature descriptors for mobile augmented reality.

Mina Makar, Vijay Chandrasekhar, Sam S Tsai

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 24, 2014

Summary

This summary is machine-generated.

This study introduces a new method for real-time object recognition in mobile augmented reality (AR). The technique significantly reduces data transmission for AR applications, making them more efficient over wireless networks.

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Area of Science:

Computer Vision
Mobile Computing
Signal Processing

Background:

Mobile augmented reality (AR) demands real-time object recognition and tracking.
Current methods rely on local features from individual video frames, which can be inefficient for streaming.

Purpose of the Study:

To develop a temporally coherent keypoint detector for AR.
To design efficient interframe predictive coding for feature descriptors and keypoint locations.
To enable practical server-based mobile AR over current wireless links.

Main Methods:

Proposed a novel keypoint detector that considers temporal coherence across video frames.
Developed predictive coding techniques to minimize data transmission for patches and feature descriptors.
Focused on modifying previously transmitted data for efficient interframe representation.

Main Results:

Achieved significant bit-rate reduction for transmitting salient information.
The proposed system requires less than one-tenth the bit-rate of compressed video streaming.
Enabled efficient server-based mobile AR with practical data rates.

Conclusions:

The developed system offers a practical solution for streaming mobile AR applications.
Temporal coherence and predictive coding are key to reducing bandwidth requirements.
This approach facilitates image-based retrieval and object localization in AR.