Line Matching Using Appearance Similarities and Geometric Constraints

Abstract. Line matching for image pairs under various transformations is a challenging task. In this paper, we present a line matching algorithm which considers both the local appearance of lines and their geometric attributes. A relational graph is built for candidate matches and a spectral technique is employed to solve this matching problem efficiently. Extensive experiments on a dataset which includes various image transformations validate the matching performance and the efficiency of the proposed line matching algorithm.

Error analysis of a first-order IMEX scheme for the logarithmic Schr\"odinger equation

The logarithmic Schr\"odinger equation (LogSE) has a logarithmic nonlinearity $f(u)=u\ln |u|^2$ that is not differentiable at $u=0.$ Compared with its counterpart with a regular nonlinear term, it possesses richer and unusual dynamics, though the low regularity of the nonlinearity brings about significant challenges in both analysis and computation. Among very limited numerical studies, the semi-implicit regularized method via regularising $f(u)$ as $u^{\varepsilon}\ln ({\varepsilon}+ |u^{\varepsilon}|)^2$ to overcome the blowup of $\ln |u|^2$ at $u=0$ has been investigated recently in literature. With the understanding of $f(0)=0,$ we analyze the non-regularized first-order Implicit-Explicit (IMEX) scheme for the LogSE. We introduce some new tools for the error analysis that include the characterization of the H\"older continuity of the logarithmic term, and a nonlinear Gr\"{o}nwall's inequality. We provide ample numerical results to demonstrate the expected convergence. We position this work as the first one to study the direct linearized scheme for the LogSE as far as we can tell.Comment: 19 pages, 5 figure

Line Primitives and Their Applications in Geometric Computer Vision

Line primitives are widely found in structured scenes which provide a higher level of structure information about the scenes than point primitives. Furthermore, line primitives in space are closely related to Euclidean transformations, because the dual vector (also known as Pluecker coordinates) representation of 3D lines is the counterpart of the dual quaternion which depicts an Euclidean transformation. These geometric properties of line primitives motivate the work in this thesis with the following contributions: Firstly, by combining local appearances of lines and geometric constraints between line pairs in images, a line segment matching algorithm is developed which constructs a novel line band descriptor to depict the local appearance of a line and builds a relational graph to measure the pair-wise consistency between line correspondences. Experiments show that the matching algorithm is robust to various image transformations and more efficient than conventional graph based line matching algorithms. Secondly, by investigating the symmetric property of line directions in space, this thesis presents a complete analysis about the solutions of the Perspective-3-Line (P3L) problem which estimates the camera pose from three reference lines in space and their 2D projections. For three spatial lines in general configurations, a P3L polynomial is derived which is employed to develop a solution of the Perspective-n-Line problem. The proposed robust PnL algorithm can efficiently and accurately estimate the camera pose for both small numbers and large numbers of line correspondences. For three spatial lines in special configurations (e.g., in a Manhattan world which consists of three mutually orthogonal dominant directions), the solution of the P3L problem is employed to solve the vanishing point estimation and line classification problem. The proposed vanishing point estimation algorithm achieves high accuracy and efficiency by thoroughly utilizing the Manhattan world characteristic. Another advantage of the proposed framework is that it can be easily generalized to images taken by central catadioptric cameras or uncalibrated cameras. The third major contribution of this thesis is about structure-from-motion using line primitives. To circumvent the Pluecker constraints on the Pluecker coordinates of lines, the Cayley representation of lines is developed which is inspired by the geometric property of the Pluecker coordinates of lines. To build the line observation model, two derivations of line projection functions are presented: one is based on the dual relationship between points and lines; and the other is based on the relationship between Pluecker coordinates and the Pluecker matrix. Then the motion and structure parameters are initialized by an incremental approach and optimized by sparse bundle adjustment. Quantitative validations show the increase in performance when compared to conventional line reconstruction algorithms

SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery

Accurately perceiving location and scene is crucial for autonomous driving and mobile robots. Recent advances in deep learning have made it possible to learn egomotion and depth from monocular images in a self-supervised manner, without requiring highly precise labels to train the networks. However, monocular vision methods suffer from a limitation known as scale-ambiguity, which restricts their application when absolute-scale is necessary. To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images. In particular, we introduce a novel coarse-to-fine training strategy that enables the metric scale to be recovered in a two-stage process. Furthermore, SelfOdom is flexible and can incorporate inertial data with images, which improves its robustness in challenging scenarios, using an attention-based fusion module. Our model excels in both normal and challenging lighting conditions, including difficult night scenes. Extensive experiments on public datasets have demonstrated that SelfOdom outperforms representative traditional and learning-based VO and VIO models.Comment: 14 pages, 8 figures, in submissio

Differences in study workload stress and its associated factors between transfer students and freshmen entrants in an Asian higher education context

Unlike the studies of freshmen entrants, the learning experiences of community college transfer (CCT) students in the receiving university is a topic that has only started to gain attention in recent decades. Little is known about the differences between CCT and freshmen entrants with regard to their study workload stress and its relationship with their perceptions of the teaching and learning environment, approaches to learning, self-efficacy and generic skills. The purpose of our study was to address this gap. This was a cross-sectional survey study conducted from April 2018 to November 2018 in a university in Hong Kong. The HowULearn questionnaire was adapted to local usage and validated for data collection. In total, 841 CCT students and 978 freshmen entrants completed the survey. The respondents were aged between 19 and 52 years (mean = 21.6, SD = 1.92), and 66.0% were women. The HowULearn questionnaire was determined by factor analyses to have eight factors. The reliabilities of the eight factors were found to be acceptable (Cronbach alphas = 0.709–0.918). The CCT students scored significantly higher than the freshmen entrants for perceived study workload stress and surface approaches to learning, but lower on teaching for understanding & encouraging learning, peer support, and self-efficacy beliefs. The surface approach to learning, deep & organized studying, alignment & constructive feedback, and generic skills were found to be predictors of study workload stress in both groups of students, and in the overall student data. This study has shown that CCT students and freshmen entrants differed with regard to their study workload stress and learning experiences. Our findings provide a message, both for educators in higher education and policy makers in the government—there is not a one-size-fits-all approach to different student populations when it comes to enhancing their learning experiences.Peer reviewe

A comparison of univariate methods for forecasting electricity demand up to a day ahead

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives

A Holistic Approach to Undesired Content Detection in the Real World

We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation. The success of such a system relies on a chain of carefully designed and executed steps, including the design of content taxonomies and labeling instructions, data quality control, an active learning pipeline to capture rare events, and a variety of methods to make the model robust and to avoid overfitting. Our moderation system is trained to detect a broad set of categories of undesired content, including sexual content, hateful content, violence, self-harm, and harassment. This approach generalizes to a wide range of different content taxonomies and can be used to create high-quality content classifiers that outperform off-the-shelf models.Comment: Oral presentation at AAAI-2

Dividend policy and earnings management across countries

This paper examines whether dividend policy is associated with earnings management and whether the relationship varies across countries with wide-ranging degrees of institutional strength and transparency. Based on a sample of 23,429 corporations from 29 countries, we show that dividend payers manage earnings less than dividend non-payers, and that this evidence is stronger in countries with weak investor protection and high opacity. Further, we find that dividend payers manage earnings less when they issue equity following dividend payments, and that this result is more pronounced in countries with weak institutions and low transparency. Overall, our evidence suggests that firms may employ dividend policies associated with less earnings manipulation to mitigate agency concerns and to establish credible reputation, thereby facilitating access to external funds