Hilton Bristow
Hilton Bristow
CSIRO, Queensland University of Technology
Verified email at csiro.au
Title
Cited by
Cited by
Year
Fast convolutional sparse coding
H Bristow, A Eriksson, S Lucey
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013
2722013
Dense semantic correspondence where every pixel is a classifier
H Bristow, J Valmadre, S Lucey
Proceedings of the IEEE International Conference on Computer Vision, 4024-4031, 2015
442015
Why do linear SVMs trained on HOG features perform so well?
H Bristow, S Lucey
arXiv preprint arXiv:1406.2419, 2014
402014
Optimization methods for convolutional sparse coding
H Bristow, S Lucey
arXiv preprint arXiv:1406.2407, 2014
352014
In defense of gradient-based alignment on densely sampled sparse features
H Bristow, S Lucey
Dense Image Correspondences for Computer Vision, 135-152, 2016
132016
V1-inspired features induce a weighted margin in SVMs
H Bristow, S Lucey
European Conference on Computer Vision, 59-72, 2012
42012
Regression-based image alignment for general object categories
H Bristow, S Lucey
arXiv preprint arXiv:1407.1957, 2014
32014
Registration and representation in computer vision
HK Bristow
Queensland University of Technology, 2016
2016
Home/Publications
K Muelling, J Kober, O Kroemer, J Peters, S Bhattacharya, M Likhachev, ...
Journal Article 33 (3), 273-290, 2012
2012
Home/Publications
K Muelling, J Kober, O Kroemer, J Peters, S Bhattacharya, M Likhachev, ...
Journal Article 33 (3), 273-290, 2012
2012
Home/Publications
M Tesch, J Schneider, H Choset, D Park, CL Zitnick, D Ramanan, P Dollár, ...
Journal Article 23 (9), 1131-1158, 2009
2009
Home/Publications
MH Nguyen, L Torresani, F De la Torre Frade, C Rother, KM Kitani, Y Sato, ...
Workshop Paper 2009, 2009
2009
Analysing X-means Clustering for Reproducibility, Validity and Effectiveness
H Bristow
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Articles 1–13