2 citations · 6 across the 16 of their papers we have counts for
9 papers · 1 filter
Monitored Distillation for Positive Congruent Depth Completion
Tian Yu Liu, Parth Agrawal, Allison Chen +2
We propose a method to infer a dense depth map from a single image, its calibration, and the associated sparse point cloud. In order to leverage existing models (teachers) that pro…
An Adaptive Framework for Learning Unsupervised Depth Completion
Alex Wong, Xiaohan Fei, Byung-Woo Hong +1
We present a method to infer a dense depth map from a color image and associated sparse depth measurements. Our main contribution lies in the design of an annealing process for det…
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
Alex Wong, Byung-Woo Hong, Stefano Soatto
Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth. We follow a geometric approach that exploits abundant…
Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks
Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
We present a stochastic first-order optimization algorithm, named BCSC, that adds a cyclic constraint to stochastic block-coordinate descent. It uses different subsets of the data…
Adaptive Regularization of Some Inverse Problems in Image Analysis
Byung-Woo Hong, Ja-Keoung Koo, Martin Burger +1
We present an adaptive regularization scheme for optimizing composite energy functionals arising in image analysis problems. The scheme automatically trades off data fidelity and r…
Multi-Label Segmentation via Residual-Driven Adaptive Regularization
Byung-Woo Hong, Ja-Keoung Koo, Stefano Soatto
We present a variational multi-label segmentation algorithm based on a robust Huber loss for both the data and the regularizer, minimized within a convex optimization framework. We…
Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems
Byung-Woo Hong, Ja-Keoung Koo, Hendrik Dirks +1
We propose an adaptive regularization scheme in a variational framework where a convex composite energy functional is optimized. We consider a number of imaging problems including…
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
Ganesh Sundaramoorthi, Naeemullah Khan, Byung-Woo Hong
We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoot…
Tracking via Motion Estimation with Physically Motivated Inter-Region Constraints
Omar Arif, Ganesh Sundaramoorthi, Byung-Woo Hong +1
In this paper, we propose a method for tracking structures (e.g., ventricles and myocardium) in cardiac images (e.g., magnetic resonance) by propagating forward in time a previous…