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Asset identification using image descriptors
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AI
DOI：10.1007/s11042-013-1688-1.png)
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Abstract
En 中文
Managing Information Technology (IT) assets in data centers is a time consuming and error prone process. IT personnel typically identify misplaced assets manually by cross checking and visually inspecting assets. An automated way of keeping track of assets using portable devices reduces human error and improves productivity. The proposed asset management application on the tablet captures images of assets and searches an annotated database to identify the asset. Matching performance and response time of asset matching is evaluated using three different image feature descriptors. Methods to reduce feature extraction and matching complexity were developed. Performance and accuracy tradeoffs were studied, domain specific problems were identified, and optimizations for portable platforms were made. The results show that the proposed methods reduce complexity of asset matching by 67 % when compared to the matching process using standard image feature descriptors.
Keywords:
Asset management
Image descriptors
Data center
Complexity reduction
SIFT
SURF
FAST
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W
