most citedOptimization Methods for Convolutional Sparse Coding

36 citations · 89 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2016

Fast, Dense Feature SDM on an iPhone

Ashton Fagg, Simon Lucey, Sridha Sridharan

In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device. Our contributions are two-fold. Drawing inspiration from the FFT, we propose a…

cs.CV201614 cited

Inverse Compositional Spatial Transformer Networks

Chen-Hsuan Lin, Simon Lucey

In this paper, we establish a theoretical connection between the classical Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer Networks (STNs). STNs are of…

cs.CV20142 cited

Regression-Based Image Alignment for General Object Categories

Hilton Bristow, Simon Lucey

Gradient-descent methods have exhibited fast and reliable performance for image alignment in the facial domain, but have largely been ignored by the broader vision community. They…

cs.CV201430 cited

Why do linear SVMs trained on HOG features perform so well?

Hilton Bristow, Simon Lucey

Linear Support Vector Machines trained on HOG features are now a de facto standard across many visual perception tasks. Their popularisation can largely be attributed to the step-c…

cs.CV201436 cited

Optimization Methods for Convolutional Sparse Coding

Hilton Bristow, Simon Lucey

Sparse and convolutional constraints form a natural prior for many optimization problems that arise from physical processes. Detecting motifs in speech and musical passages, super-…

cs.CV20143 cited

Correlation Filters with Limited Boundaries

Hamed Kiani Galoogahi, Terence Sim, Simon Lucey

Correlation filters take advantage of specific properties in the Fourier domain allowing them to be estimated efficiently: O(NDlogD) in the frequency domain, versus O(D^3 + ND^2) s…