GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion

Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank-1 matrix factorization technique which is more robust to the errors in map initialization. Second, we adopt a recent global SfM method for the pose-graph optimization, which leads to a multi-stage linear formulation and enables L1 optimization for better robustness to false loops. The combination of these two approaches generates more robust reconstruction and is significantly faster (4X) than recent state-of-the-art SLAM systems. We also present a new dataset recorded with ground truth camera motion in a Vicon motion capture room, and compare our method to prior systems on it and established benchmark datasets.Comment: 3DV 2017 Project Page: https://frobelbest.github.io/gsla

The Short Run Impact of Scheduled Macroeconomic Announcements on the Australian Dollar during 1998

This study examines the high frequency reaction of the Australian Dollar (AUD) to new information contained in scheduled macroeconomic news releases in Australia for 1998 using Money Market Services trader expectations data. By using exchange rate data sampled at 10-second intervals, major price adjustments are found to begin almost immediately following the initial release of information and are complete within one minute of the announcement. There is some evidence of over-reaction after the initial release but returns in the first minute do not seem to have any meaningful structure that would enable prediction of returns in the second minute. The AUD appears to trade efficiently and the market absorbs new information quickly.

Linear Global Translation Estimation with Feature Tracks

This paper derives a novel linear position constraint for cameras seeing a common scene point, which leads to a direct linear method for global camera translation estimation. Unlike previous solutions, this method deals with collinear camera motion and weak image association at the same time. The final linear formulation does not involve the coordinates of scene points, which makes it efficient even for large scale data. We solve the linear equation based on $L_1$ norm, which makes our system more robust to outliers in essential matrices and feature correspondences. We experiment this method on both sequentially captured images and unordered Internet images. The experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3. Move "Evaluation on synthetic data" out to supplementary file; 4. Divide subsection "Evaluation on general data" to subsections "Experiment on sequential data" and "Experiment on unordered Internet data"; 5. Change Fig. 1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some symbols; 8. Correct some typo

Application of Differential Scanning Calorimetric Method for Assessing and Monitoring Various Physical and Oxidative Properties of Vegetable Oils

Differential scanning calorimetry (DSC) can be a powerful instrumental technique for analyzing oils and fats systems but has tended not to be well understood and used in the field of oils and fats. The main purpose of this project is to develop various techniques based on DSC to study the physical and chemical properties of vegetable oils. High-performance liquid chromatography (HPLC), gas-liquid chromatography (OLC), oxidative stability instrument (OS1), and various standard chemical analyses were used in this investigation to complement the DSC methods. This work is a systematic study of vegetable oils' melting and crystallization profiles by using DSC. The investigation began with the successful comparison of the DSC thermal curves of 17 different vegetable oils. Thorough investigations in this work were also directed towards obtaining basic information about the relationship between thermal profiles and chemical compositions of 17 different vegetable oils. Thereafter, the effects ofDSC scanning rate variation were studied. Scanning rates were found to affect melting/crystallization profile, melting (and/or crystallization) offset (and/or onset) and peak. temperatures, and peak. enthalpies of all vegetable oils. In this study, DSC was utilized to monitor the oxidation of heated oils during deep-fat frying and microwave heating. A statistical comparative study was carried out on the DSC and standard chemical methods. The results revealed that there is good correlation (P < 0.01) between the DSC method and other standard chemical methods. In another study, a new calorimetric technique was developed to determine three important quality indices in deep-fat frying industry namely, total polar compounds (TPC), free fatty acid (FFA) content and iodine value (IV) of heated oils using the DSC cooling profiles. The studies have shown that all DSC methods developed were comparable to the standard American Oil Chemists' Society (AOCS) methods. A simple and efficient DSC technique to determine the oxidative stability of vegetable oils was described. The isothermal DSC technique for direct determination of the oxidative stability of vegetable oils has been built and a comparative study to OSI was demonstrated. The results indicated that there is good correlation (P < 0.01) between the DSC oxidative induction time (To) and OSI values. Isothermal calorimetry was then employed as a general analytical method where the Arrhenius kinetic data for the lipid oxidation of vegetable oils were obtained by measurement of the DSC To at various temperatures. The present study also developed a simple method for measuring the antioxidant activity in RBDPOo using isothermal DSC technique. Generally, this project concluded that DSC appears to be a useful method in determining various physical and chemical parameters of vegetable oils, and it may have the potential to replace the laborious, time- and chemical-consuming standard methods. The various methods developed here can be applied in the oils and fats industry

DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN. However, human labeling is expensive, even impractical, and large quantities of data may not always be available. Inspired by dual learning from natural language translation, we develop a novel dual-GAN mechanism, which enables image translators to be trained from two sets of unlabeled images from two domains. In our architecture, the primal GAN learns to translate images from domain U to those in domain V, while the dual GAN learns to invert the task. The closed loop made by the primal and dual tasks allows images from either domain to be translated and then reconstructed. Hence a loss function that accounts for the reconstruction error of images can be used to train the translators. Experiments on multiple image translation tasks with unlabeled data show considerable performance gain of DualGAN over a single GAN. For some tasks, DualGAN can even achieve comparable or slightly better results than conditional GAN trained on fully labeled data.Comment: Accepted by ICCV 201