Published in IEEE transactions on pattern analysis and machine intelligence
In some significant applications such as data forecasting, the locations of missing entries cannot obey any non-degenerate distributions, questioning the validity of the prevalent assumption that the missing data is randomly chosen according to some probabilistic model. To break through the limits of random sampling, we explore in this paper the pr...
Published in IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes. Our key observation is that the recovery of geodesic distance with the heat method [1] can be reformulated as optimization of its gradients subject to integrability, which can be solved using an efficient first-order method that requires...
Published in IEEE transactions on pattern analysis and machine intelligence
Much progress has been made for non-rigid structure from motion (NRSfM) during the last two decades, which made it possible to provide reasonable solutions for synthetically-created benchmark data. In order to utilize these NRSfM techniques in more realistic situations, however, we are now facing two important problems that must be solved: First, g...
Published in IEEE transactions on pattern analysis and machine intelligence
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that an energy-based spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a...
Published in IEEE transactions on pattern analysis and machine intelligence
This paper presents a novel local image descriptor called Pattern of Local Gravitational Force (PLGF). It is inspired by Law of Universal Gravitation. PLGF is a hybrid descriptor, which is a combination of two feature components: one is the Pattern of Local Gravitational Force Magnitude (PLGFM), and another is Pattern of Local Gravitational Force A...
Published in IEEE transactions on pattern analysis and machine intelligence
SafePredict is a novel meta-algorithm that works with any base prediction algorithm for online data to guarantee an arbitrarily chosen correctness rate, 1-ϵ, by allowing refusals. Allowing refusals means that the meta-algorithm may refuse to emit a prediction produced by the base algorithm so that the error rate on non-refused predictions does not ...
Published in IEEE transactions on pattern analysis and machine intelligence
We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learn...
Published in IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequencelet or seqlet, a sub-sequence wherein the frames share strong similarities and are thus gr...
Published in IEEE transactions on pattern analysis and machine intelligence
Here, we propose a novel method to estimate the parameters of non-planar objects with thin film surfaces. Being able to estimate the optical parameters of objects with thin film surfaces has a wide range of applications from industrial inspections to biological and archaeology research. However, there are many challenging issues that need to be ove...
Published in IEEE transactions on pattern analysis and machine intelligence
Large-scale 3D point clouds (LS3DPCs) captured by terrestrial LiDAR scanners often include virtual points which are generated by glass reflection. The virtual points may degrade the performance of various computer vision techniques when applied to LS3DPCs. In this paper, we propose a virtual point removal algorithm for LS3DPCs with multiple glass p...
