| Fast convolutional sparse coding H Bristow, A Eriksson, S Lucey Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 414 | 2013 |
| 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 | 69 | 2015 |
| Optimization methods for convolutional sparse coding H Bristow, S Lucey arXiv preprint arXiv:1406.2407, 2014 | 54 | 2014 |
| Why do linear SVMs trained on HOG features perform so well? H Bristow, S Lucey arXiv preprint arXiv:1406.2419, 2014 | 52 | 2014 |
| In defense of gradient-based alignment on densely sampled sparse features H Bristow, S Lucey Dense image correspondences for computer vision, 135-152, 2016 | 18 | 2016 |
| V1-inspired features induce a weighted margin in SVMs H Bristow, S Lucey Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 6 | 2012 |
| Regression-based image alignment for general object categories H Bristow, S Lucey arXiv preprint arXiv:1407.1957, 2014 | 3 | 2014 |
| Registration and representation in computer vision HK Bristow Queensland University of Technology, 2016 | | 2016 |
| Analysing X-means Clustering for Reproducibility, Validity and Effectiveness H Bristow | | |
