Paper Info
Title
Briskola: BRISK optimized for low-power ARM architectures
Abstract
Local visual features are commonly adopted to accomplish analysis tasks such as object recognition/tracking and image retrieval. Recently, several visual features extraction algorithms tailored to low-power architectures have been proposed, in order to enable image analysis on energy-constrained devices such as smart-phones or Visual Sensor Networks (VSN). In this work, we dissect and analyze BRISK, a state-of-the-art low-power visual feature extractor, in order to evaluate the impact of its individual building blocks on the overall energy consumption. For each building block, we propose a solution to limit the energy consumption without affecting the overall analysis performance. The resulting BRISKOLA (BRISK Optimized for Low-power ARM architectures) feature extractor exhibits energy savings up to 30% with respect to the original implementation.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7026151
Image Processing
Keywords
Field
DocType
feature extraction,image retrieval,object recognition,object tracking,optimisation,BRISK optimization,binary robust invariant scalable keypoints,energy consumption,energy-constrained devices,features extraction algorithms,image analysis,image retrieval,local visual features,low-power ARM architectures,low-power visual feature extractor,object recognition,object tracking,ARM,BRISK,Local Visual Features,Visual Sensor Networks
Computer vision,ARM architecture,Pattern recognition,Computer science,Image retrieval,Artificial intelligence,Extractor,Energy consumption,Wireless sensor network,Cognitive neuroscience of visual object recognition
Conference
ISSN
Citations 
PageRank 
1522-4880
8
0.53
References 
Authors
9
4
Name
Order
Citations
PageRank
Luca Baroffio123614.46
Canclini, A.2161.03
Matteo Cesana382663.33
Alessandro Redondi428025.99