A5100343642- Remote Sensing and LiDAR Applications
- Atmospheric and Environmental Gas Dynamics
- Advanced Steganography and Watermarking Techniques
- Geochemistry and Geologic Mapping
- 3D Surveying and Cultural Heritage
- Satellite Image Processing and Photogrammetry
- Computer Graphics and Visualization Techniques
- Advanced Vision and Imaging
- Planetary Science and Exploration
- Geological Modeling and Analysis
- Marine and coastal ecosystems
- Remote Sensing in Agriculture
- Digital Media Forensic Detection
- Generative Adversarial Networks and Image Synthesis
- Topic Modeling
- Calibration and Measurement Techniques
- Robotics and Sensor-Based Localization
- Underwater Acoustics Research
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Diverse Topics in Contemporary Research
- Atmospheric aerosols and clouds
- Natural Language Processing Techniques
- Reinforcement Learning in Robotics
- Advanced Optical Sensing Technologies
United States Geological Survey
2016-2024
Daegu Gyeongbuk Institute of Science and Technology
2024
Virginia Tech
2024
Kangwon National University
2024
Yeungnam University
2002-2022
KBR (United States)
2021
Pohang University of Science and Technology
2020
Naver (South Korea)
2019-2020
Yonsei University
2019
Tokyo Institute of Technology
2018-2019
Mobile robots are required to navigate freely in a complex and crowded environment order provide services humans. For this navigation ability, deep reinforcement learning (DRL)-based methods gaining increasing attentions. However, existing DRL require wide field of view (FOV), which imposes the usage high-cost lidar devices. In paper, we explore possibility replacing expensive devices with affordable depth cameras have limited FOV. First, analyze effect agents. Second, propose LSTM agent...
The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study performance state-of-the-art methods in an integrated way, a voluntary open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated 2016 frame Committee Earth Observation Satellites (CEOS) Working Group Calibration & Validation (WGCV). first exercise extended second edition wherein twelve (AC)...
The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through water with high precision accuracy if bottom peak intensity waveform is greater than noise level. However, passive optical imaging involves measuring radiance after sunlight undergoes downward attenuation on way to sea floor, reflected light then attenuated while moving back upward surface....
A novel technique for synthesizing a hologram of three-dimensional objects from multiple orthographic projection view images is proposed. The are captured under incoherent white illumination and their obtained. multiplied by the corresponding phase terms integrated to form Fourier or Fresnel hologram. Using simple manipulation images, it also possible shift an arbitrary amount along three axes in reconstruction space invert depths with respect given depth plane. principle verified experimentally.
Existing frameworks for image stitching often provide visually reasonable stitchings. However, they suffer from blurry artifacts and disparities in illumination, depth level, etc. Although the recent learning-based stitchings relax such disparities, required methods impose sacrifice of qualities failing to capture high-frequency details stitched images. To address problem, we propose a novel approach, implicit Neural Image Stitching (NIS) that extends arbitrary-scale super-resolution. Our...
Any well-behaved generative model over a variable $\mathbf{x}$ can be expressed as deterministic transformation of an exogenous ('outsourced') Gaussian noise $\mathbf{z}$: $\mathbf{x}=f_\theta(\mathbf{z})$. In such (e.g., VAE, GAN, or continuous-time flow-based model), sampling the target $\mathbf{x} \sim p_\theta(\mathbf{x})$ is straightforward, but from posterior distribution form $p(\mathbf{x}\mid\mathbf{y}) \propto p_\theta(\mathbf{x})r(\mathbf{x},\mathbf{y})$, where $r$ constraint...
As the amount of network traffic is growing exponentially, analysis and classification are playing a significant role for efficient resource allocation management. However, with emerging security technologies, this work becoming more difficult by encrypted communication such as Tor, which one most popular encryption techniques. This paper proposes an approach to classify Tor using hexadecimal raw packet header convolutional neural model. Comparing competitive machine learning algorithms, our...
The accuracy assessment of airborne lidar point cloud typically estimates vertical by computing RMSEz (root mean square error the z coordinate) from ground check points (GCPs). Due to low density cloud, there is often not enough accurate semantic context find an conjugate point. To advance in full three-dimensional (3D) context, geometric features, such as three-plane intersection or two-line point, are used. Although still low, features mathematically modeled many points. Thus, provide a...
This paper introduces the Generative Flow Ant Colony Sampler (GFACS), a novel neural-guided meta-heuristic algorithm for combinatorial optimization. GFACS integrates generative flow networks (GFlowNets) with ant colony optimization (ACO) methodology. GFlowNets, model that learns constructive policy in spaces, enhance ACO by providing an informed prior distribution of decision variables conditioned on input graph instances. Furthermore, we introduce combination training tricks, including...
Deep reinforcement learning (RL) is being actively studied for robot navigation due to its promise of superior performance and robustness. However, most existing deep RL agents are trained using fixed parameters, such as maximum velocities weightings reward components. Since the optimal choice parameters depends on use-case, it can be difficult deploy methods in a variety real-world service scenarios. In this paper, we propose novel method that adapt policy wide range functions without...
In this paper, we propose a method to learn unified representations of multilingual speech and text with single model, especially focusing on the purpose synthesis. We represent audio units, quantized features encoded from self-supervised model. Therefore, can focus their linguistic content by treating as pseudo build representation text. Then, train an encoder-decoder structured model Unit-to-Unit Translation (UTUT) objective data. Specifically, conditioning encoder source language token...
Kim, M.; Kopilevich, Y.; Feygels, V.; Park, J.Y., and Wozencraft, J., 2016. Modeling of airborne bathymetric lidar waveforms. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, Applications. Journal Coastal Research, Special Issue, No. 76, pp. 18–30. Coconut Creek (Florida), ISSN 0749-0208.Modeling the optical power return waveform is performed for radiometrically calibrated CZMIL (Coastal Zone Mapping Imaging Lidar,...
We have developed a combined atmospheric-oceanographic spectral optimization solution decomposing measured airborne radiance data from the passive spectrometer into environmental parameters of interest. In this model, we hold depth measurements lidar as fixed constraints, thereby gaining degree freedom in solution, and extending deeper waters than achieved with alone. paper, illustrate results the data processing procedure and assess accuracy estimated IOPs (Inherent Optical Properties)...
The capability to jointly process multi-modal information is becoming an essential task. However, the limited number of paired data and large computational requirements in learning hinder development. We propose a novel Tri-Modal Translation (TMT) model that translates between arbitrary modalities spanning speech, image, text. introduce viewpoint, where we interpret different as languages, treat translation well-established machine problem. To this end, tokenize speech image into discrete...
SUMMARY Three-dimensional finite-element models, which can handle the stress perturbations caused by subsurface mechanical heterogeneities and fault interactions, have been combined with finite source inversion to estimate coseismic slip distribution over plane. However, mesh grid for discretizing governing equations in model significantly affects numerical accuracy. In this study, we performed kinematic idealized (regular observation point array; M1A–M1D) regional (GEONET, GPS Earthquake...
The Leica Geosystems CountryMapper hybrid system has the potential to collect data that satisfy U.S. Geological Survey (USGS) National Geospatial Program (NGP) and 3D Elevation (3DEP) Department of Agriculture (USDA) Imagery (NAIP) requirements in a single collection. This research will help 3DEP determine if this sensor meet current future topographic lidar collection requirements. We performed an accuracy analysis assessment on point cloud produced from CountryMapper. boresighting...
CZMIL will simultaneously acquire lidar and passive spectral data. These data be fused to produce enhanced seafloor reflectance images from each sensor, combined at a higher level achieve classification. In the DPS software, first processed solve for depth, attenuation, reflectance. The depth measurements then used constrain optimization of data, resulting water column estimates recursively improve lidar. Finally, cube with texture metrics estimated topography classifications seafloor.