Paper Info
Title
Vehicle logo super-resolution by canonical correlation analysis
Abstract
Recognition of a vehicle make is of interest in the fields of law enforcement and surveillance. In this paper, we develop a canonical correlation analysis (CCA) based method for vehicle logo super-resolution to facilitate the recognition of the vehicle make. From a limited number of high-resolution logos, we populate the training dataset for each make using gamma transformations. Given a vehicle logo from low-resolution source (i.e., surveillance or traffic camera recordings), the learned models yield super-resolved results. By matching the low-resolution image and the generated high-resolution images, we select the final output that is closest to the low-resolution image in the histogram of oriented gradients (HOG) feature space. Experimental results show that our approach outperforms the state-of-the-art super-resolution methods in qualitative and quantitative measures. Furthermore, the super-resolved logos help to improve the accuracy in the subsequent recognition tasks significantly.
Year
DOI
Venue
2012
10.1109/ICIP.2012.6467338
ICIP
Keywords
Field
DocType
high-resolution logos,vehicle make recognition,road vehicles,low-resolution image matching,image matching,gamma transformations,image resolution,image recognition,surveillance,vehicle logo super-resolution,super-resolution,subspace learning,hog feature space,law enforcement,cca based method,canonical correlation analysis,correlation methods,histogram of oriented gradients,super resolution
Computer vision,Feature vector,Pattern recognition,Image matching,Traffic camera,Computer science,Canonical correlation,Logo,Histogram of oriented gradients,Artificial intelligence,Image resolution,Superresolution
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4673-2532-5
978-1-4673-2532-5
2
PageRank 
References 
Authors
0.38
13
3
Name
Order
Citations
PageRank
Le An121711.24
Ninad Thakoor29413.39
Bir Bhanu33356380.19