ISWC '97 Proceedings of the 1st IEEE International Symposium on Wearable Computers
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Performance characterization and optimization of mobile augmented reality on handheld platforms
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
Feature tracking and matching in video using programmable graphics hardware
Machine Vision and Applications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Template-based memory access engine for accelerators in SoCs
Proceedings of the 16th Asia and South Pacific Design Automation Conference
CoQoS: Coordinating QoS-aware shared resources in NoC-based SoCs
Journal of Parallel and Distributed Computing
Cost-effectively offering private buffers in SoCs and CMPs
Proceedings of the international conference on Supercomputing
Buffer-integrated-Cache: a cost-effective SRAM architecture for handheld and embedded platforms
Proceedings of the 48th Design Automation Conference
How mobile phones perform in collaborative augmented reality (CAR) applications
The Journal of Supercomputing
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Mobile Augmented Reality (MAR) is an emerging visual computing application for the mobile internet device (MID). In one MAR usage model, the user points the handheld device to an object (like a wine bottle or a building) and the MID automatically recognizes and displays information regarding the object. Achieving this in software on the handheld requires significant compute processing for object recognition and matching. In this paper, we identify hotspot functions of the MAR workload on a low-power x86 platform that motivates acceleration. We present the detailed design of two hardware accelerators, one for object recognition (MAR-HA) and the other for match processing (MAR-MA). We also quantify the performance and area efficiency of the hardware accelerators. Our analysis shows that hardware acceleration has the potential to improve the individual hotspot functions by as much as 20x, and overall response time by 7x. As a result, user response time can be reduced significantly.