홍제형 교수

ABSTRACT We apply a Bayesian parameter estimation technique to a chemical kinetic mechanism for n ‐propylbenzene oxidation in a shock tube to propagate errors in experimental data to errors in Arrhenius parameters and predicted species concentrations. We find that, to apply the methodology successfully, conventional optimization is required as a preliminary step. This is carried out in two stages: First, a quasi‐random global search using a Sobol low‐discrepancy sequence is conducted, followed b

Variable Projection (VarPro) is a framework to solve optimization problems efficiently by optimally eliminating a subset of the unknowns. It is in particular adapted for Separable Nonlinear Least Squares (SNLS) problems, a class of optimization problems including low-rank matrix factorization with missing data and affine bundle adjustment as instances. VarPro-based methods have received much attention over the last decade due to the experimentally observed large convergence basin for certain pro

Matrix factorization (or low-rank matrix completion) with missing data is a key computation in many computer vision and machine learning tasks, and is also related to a broader class of nonlinear optimization problems such as bundle adjustment. The problem has received much attention recently, with renewed interest in variable-projection approaches, yielding dramatic improvements in reliability and speed. However, on a wide class of problems, no one approach dominates, and because the various ap

Face recognition now requires a large number of labelled masked face images in the era of this unprecedented COVID19 pandemic. Unfortunately, the rapid spread of the virus has left us little time to prepare for such dataset in the wild. To circumvent this issue, we present a 3D model-based approach called WearMask3D for augmenting face images of various poses to the masked face counterparts. Our method proceeds by first fitting a 3D morphable model on the input image, second overlaying the mask

Re-assembling multiple pots accurately from numerous 3D scanned fragments remains a challenging task to this date. Previous methods extract all potential matching pairs of pot sherds and considers them simultaneously to search for an optimal global pot configuration. In this work, we empirically show such global approach greatly suffers from false positive matches between sherds inflicted by indistinc-tive sharp fracture surfaces in pot fragments. To mitigate this problem, we take inspirations f

Bundle adjustment is a nonlinear refinement method for camera poses and 3D structure requiring sufficiently good initialization. In recent years, it was experimentally observed that useful minima can be reached even from arbitrary initialization for affine bundle adjustment problems (and fixed-rank matrix factorization instances in general). The key success factor lies in the use of the variable projection (VarPro) method, which is known to have a wide basin of convergence for such problems. In

Self-calibrating an array of 3-D field sensors, such as three-axis magnetometers and accelerometers, requires estimation of two variable sets—each sensor’s intrinsic model that maps its input field to the corresponding measurement and each sensor’s coordinates relative to a common frame of reference within the array. In this work, we propose the first unified self-calibration method for arrays of same-type 3-D field sensors, which is robust to anomalous sensor measurements unlike previous algori

Self-calibration of a magnetometer usually requires controlled magnetic environment as the calibration output can be affected by field distortions from nearby magnetic objects. In this article, we develop a two-stage method that can accurately self-calibrate magnetometer from measurements containing anomalous readings due to local magnetic disturbances. The method proceeds by robustly fitting an ellipsoid to measurement data via L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="

The task of virtually reassembling an axially symmetric pot from its fragments can be greatly simplified by utilizing the constraints induced by the pot's axis of symmetry. This requires accurate estimation of the axis for each sherd, whose 3D data typically contain gross outliers arising from surface artifacts, noisy surface normals and unfiltered data along the break surface. In this work, we propose a simple two-stage robust axis estimator, PotSAC, which is based on a variant of the random sa

BACKGROUND: Accurate measurement of the hip-knee-ankle (HKA) angle is essential for informed clinical decision-making in the management of knee osteoarthritis (OA). Knee OA is commonly associated with varus deformity, where the alignment of the knee shifts medially, leading to increased stress and deterioration of the medial compartment. The HKA angle, which quantifies this alignment, is a critical indicator of the severity of varus deformity and helps guide treatment strategies, including corre

File fragment classification (FFC) is the task of identifying the file type given a small fraction of binary data, and serves a crucial role in digital forensics and cybersecurity. Recent studies have adopted convolutional neural networks (CNNs) for this problem, significantly improving the accuracy over the traditional methods relying on handcrafted features. In this paper, we aim to expand on the recent performance gain by better leveraging the large amount of digital files available for train

File fragment classification is a crucial task in digital forensics and cybersecurity, and has recently achieved significant improvement through the deployment of convolutional neural networks (CNNs) compared to traditional handcrafted feature-based methods. However, CNN-based models exhibit inherent biases that can limit their effectiveness for larger datasets. To address this limitation, we propose the Cross-Attention Multi-Scale Performer (XMP) model, which integrates the attention mechanisms

The recent success in revealing scene details from sparse 3D point clouds obtained via structure-from-motion has raised significant privacy concerns in visual localization. One prominent approach for mitigating this issue is to lift 3D points to 3D lines thereby reducing the effectiveness of the scene inversion attacks, but this comes at the cost of in-creased algorithmic complexity for camera localization due to weaker geometric constraints induced by line clouds. To overcome this limitation, w