Reliability Analysis for Global Motion Estimation

Digital Object Identifier 10.1109/LSP.2009.2028101Global motion estimation (GME) is the enabling step for many important video exploitation tasks. In this work, we focus on indirect GME methods which have low computational complexity. Typically, an indirect GME method has two major steps. The first step is to find point correspondence between frames through local motion search or feature matching. Then, the second step determines global motion parameters using optimal model fitting, such as least mean-squared error (LMSE) fitting or RANSAC. However, due to image noise and inherent ambiguity in point correspondence, local motion estimation often suffers from relatively large errors, which degrade the performance and reliability of GME. In this work, we propose a method to characterize the reliability of local motion estimation results and use this reliability measure as a weighting factor to determine the importance level of each local motion estimation result during global motion estimation. Our simulation results demonstrate that the proposed scheme is able to significantly improve the accuracy and robustness of global motion estimation with a very small computational overhead.This work was supported in part by the National Institute of Health under Grant 5R21AG026412

Boundary effect and dressed states of a giant atom in a topological waveguide

The interaction between the quantum emitter and topological photonic system makes the photon behave in exotic ways. We here study the properties of a giant atom coupled to two sites of a one-dimensional topological waveguide, which is described by the Su-Schrieffer-Heeger (SSH) chain. We find that the giant atom can act as an effective boundary and induce the chiral zero modes, which are similar to those in the SSH model with open boundary, for the waveguide under the periodical boundary. Except for the boundary effect, we also find that the giant atom can lift energy degeneracy inside the energy bands of the SSH chain and adjust spatial symmetry of the photon distributions for the states of the dressed giant atom and waveguide. That is, the giant atom can be used to change the properties of the topological environment. Our work may stimulate more studies on the interaction between matter and topological environment.Comment: 7 Pages, 4 Figure

The preparation and properties of novel structural carbon foams derived from different mesophase pitches

As a novel porous multi-functional carbon material, carbon foams have high bulk thermal conductivity and low density, making them as excellent materials for thermal management systems applications, such as heat exchangers, space radiators, and thermal protection systems. In this paper, the carbon foams with high thermal conductivity, derived from three kinds of mesophase pitches, were fabricated by the process of foaming, carbonization and graphitization. The microstructures of the foams were examined by scanning electron microscopy. It was found that the pores were uniformly distributed, and the pore wall thickened with increasing foams’ density. The properties of the foams were studied, including compressive strength and thermal conductivity. The results showed that lower density and higher thermal conductivity were achieved for the foams using the two kinds of pitches with higher volatile components. The bulk thermal conductivity of carbon foams were up to 179 W/(m·K) and 201 W/(m·K), for the densities of 0.66 g/cm3 and 0.83 g/cm3, respectively. The foams’ compressive strength was in the range of 1.6 MPa to 3.4 MPa

Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation

Pre-trained Vision-Language Models (VLMs), such as CLIP, have shown enhanced performance across a range of tasks that involve the integration of visual and linguistic modalities. When CLIP is used for depth estimation tasks, the patches, divided from the input images, can be combined with a series of semantic descriptions of the depth information to obtain similarity results. The coarse estimation of depth is then achieved by weighting and summing the depth values, called depth bins, corresponding to the predefined semantic descriptions. The zero-shot approach circumvents the computational and time-intensive nature of traditional fully-supervised depth estimation methods. However, this method, utilizing fixed depth bins, may not effectively generalize as images from different scenes may exhibit distinct depth distributions. To address this challenge, we propose a few-shot-based method which learns to adapt the VLMs for monocular depth estimation to balance training costs and generalization capabilities. Specifically, it assigns different depth bins for different scenes, which can be selected by the model during inference. Additionally, we incorporate learnable prompts to preprocess the input text to convert the easily human-understood text into easily model-understood vectors and further enhance the performance. With only one image per scene for training, our extensive experiment results on the NYU V2 and KITTI dataset demonstrate that our method outperforms the previous state-of-the-art method by up to 10.6\% in terms of MARE.Comment: Accepted by WACV 202

Synchronous droughts and floods in the Southern Chinese Loess Plateau since 1646 CE in phase with decadal solar activities

Droughts and floods are two longstanding and devastating climatic threats to mankind. They are challenging to predict mainly due to the significant spatial and temporal variations of precipitation. Using historical archives back from 1646 CE, here we present a high-resolution catchment level dataset of droughts and floods in the southern Chinese Loess Plateau (hereafter, CLP) within the middle reaches of River Jing. We have analysed the occurrences of floods and droughts based a specially-developed statistics from historical archives, as well as the daily rainfall from present-day observations within the catchment. Overall, our results show that the frequency of droughts and floods in the region is synchronous on decadal timescales with solar activities and the Pacific Decadal Oscillation (hereafter, PDO) index, and they are also broadly in phase with changes in both global and regional reconstructed temperatures. At decadal to interannual timescales, PDO and El Niño and Southern Oscillation (hereafter, ENSO) drive an uneven distribution of precipitation in different seasons in the southern CLP, which could be one of the reasons for the strong association of floods and droughts with the PDO and ENSO signals in our catchment. If the global temperature continues to rise in the future, we expect that the risk of both droughts and floods in the study region will also increase

Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring

DOI 10.1109/TCSVT.2008.2005612In this work, we study how continuous video monitoring and intelligent video processing can be used in eldercare to assist the independent living of elders and to improve the efficiency of eldercare practice. More specifically, we develop an automated activity analysis and summarization for eldercare video monitoring. At the object level, we construct an advanced silhouette extraction, human detection and tracking algorithm for indoor environments. At the feature level, we develop an adaptive learning method to estimate the physical location and moving speed of a person from a single camera view without calibration. At the action level, we explore hierarchical decision tree and dimension reduction methods for human action recognition. We extract important ADL (activities of daily living) statistics for automated functional assessment. To test and evaluate the proposed algorithms and methods, we deploy the camera system in a real living environment for about a month and have collected more than 200 hours (in excess of 600 G bytes) of activity monitoring videos. Our extensive tests over these massive video datasets demonstrate that the proposed automated activity analysis system is very efficient.This work was supported in part by National Institute of Health under Grant 5R21AG026412

All-fiber probing of aluminized RDX particle micro-explosion

Investigating the thermal decomposition characteristics and mechanisms of nano- and submicron-aluminized 1,3,5-trinitro-1,3,5-triazine (RDX) is essential to optimize the formulations and improve combustion/detonation efficiencies. However, no research has focused on the microscopic scale of a single aluminized RDX particle. We demonstrate an all-fiber probing method for the violent thermal decomposition of a single nano-aluminized micron-RDX particle, which we address as micro-explosion in this paper. We believe studying micro-explosion will be beneficial to the research of thermal decomposition. In experiments, we first characterize the micro-explosion as a three-step process, i.e., melting, first decomposition, and second decomposition. Then, we measure micro-explosion properties, i.e., shockwave-like flow velocity, initiation energy threshold, and shockwave-like flow pressure. Among the aluminized RDX particles with 0%, 5%, 10%, 15%, 20%, 25%, and 30% surface coverage ratios (SCRs), the sample with 20% surface coverage ratio shows the highest flow velocity and force, which are about 69.9 mm/s and 39.4 μN, respectively. Moreover, the threshold decreases with rising surface coverage ratios, and the mean threshold of 30% surface coverage ratio is 75 μJ. The experimental results prove that the all-fiber micro-explosion probing method is feasible, safe, and robust