Efficient Human Pose Estimation with Image-dependent Interactions

Human pose estimation from 2D images is one of the most challenging and computationally-demanding problems in computer vision. Standard models such as Pictorial Structures consider interactions between kinematically connected joints or limbs, leading to inference cost that is quadratic in the number of pixels. As a result, researchers and practitioners have restricted themselves to simple models which only measure the quality of limb-pair possibilities by their 2D geometric plausibility. In this talk, we propose novel methods which allow for efficient inference in richer models with data-dependent interactions. First, we introduce structured prediction cascades, a structured analog of binary cascaded classifiers, which learn to focus computational effort where it is needed, filtering out many states cheaply while ensuring the correct output is unfiltered. Second, we propose a way to decompose models of human pose with cyclic dependencies into a collection of tree models, and provide novel methods to impose model agreement. Finally, we develop a local linear approach that learns bases centered around modes in the training data, giving us image-dependent local models which are fast and accurate. These techniques allow for sparse and efficient inference on the order of minutes or seconds per image. As a result, we can afford to model pairwise interaction potentials much more richly with data-dependent features such as contour continuity, segmentation alignment, color consistency, optical flow and multiple modes. We show empirically that these richer models are worthwhile, obtaining significantly more accurate pose estimation on popular datasets

Mechanisms of atrial flutter following epicardial high intensity focused ultrasound left atrial ablative procedures during concomitant cardiac surgery

AbstractIntroductionIatrogenic atrial tachyarrhythmias have increased with the widespread application of left atrial ablative procedures to treat atrial fibrillation.Methods and resultsEntrainment and activation mapping were utilized to study the mechanisms of atrial flutter in two patients who presented with atypical atrial flutter after high intensity focused ultrasound (HIFU) atrial ablation for persistent atrial fibrillation during the course of concomitant cardiac surgery. Case 1: Atrial flutter with CL of 340ms was demonstrated to be mediated by entry into and exit from the partially isolated posterior left atrium (LA) with conduction delay across at least one of the connections. The exit site was near the left superior pulmonary vein (LSPV) and the entrance site was near the right inferior pulmonary vein (RIPV) as demonstrated by activation and entrainment mapping. Case 2: Entrainment mapping was highly suggestive of inferior exit from the HIFU ablation line between the two inferior pulmonary veins. Flutter terminated during trans-septal procedure and could not be re-induced. Activation mapping of the LA during pacing revealed the inferior exit and left superior entrance site, both of which were successfully ablated, isolating the posterior LA.ConclusionsRe-entrant atrial flutter post-HIFU epicor Maze is caused by slow conduction at entry and exit sites from the otherwise isolated posterior LA wall. In both cases, gaps were found close to the LSPV and RIPV which may reflect difficulty in achieving proper contact between the HIFU device and the left atrial wall at these sites. These gaps are amenable to catheter ablation

Subsemble: An Ensemble Method for Combining Subset-Specific Algorithm Fits

Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive datasets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. We give an oracle result that provides a theoretical performance guarantee for Subsemble. Through simulations, we demonstrate that Subsemble can be a beneficial tool for small to moderate sized datasets, and often has better prediction performance than the underlying algorithm fit just once on the full dataset. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemble relative to the underlying algorithm fit just once on the full dataset