Jiajie Li

ORCID: 0000-0001-7249-4036
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About
Contact & Profiles
Research Areas
  • Robotics and Sensor-Based Localization
  • Advanced Vision and Imaging
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Video Surveillance and Tracking Methods
  • Radiomics and Machine Learning in Medical Imaging
  • Medical Imaging Techniques and Applications
  • Lung Cancer Diagnosis and Treatment
  • 3D Surveying and Cultural Heritage
  • Advanced Image and Video Retrieval Techniques

South China University of Technology
2024

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
2022-2023

Wuhan University
2022-2023

10.1007/s11517-024-03169-x article EN Medical & Biological Engineering & Computing 2024-07-17

Real-time 3D reconstruction combined with MAVs has garnered significant attention in a variety of fields, including building maintenance, geological exploration, emergency rescue, and cultural heritage protection. While possess the advantages speed lightness, they also exhibit strong image blur limited computational resources. To address these limitations, this paper presents novel approach for onboard, depth-only, real-time capable accommodating fast-moving MAVs. Our primary contribution is...

10.3390/drones7060358 article EN cc-by Drones 2023-05-29

Visual geo-localization can achieve UAVs (Unmanned Aerial Vehicles) position during GNSS (Global Navigation Satellite System) denial or restriction. However, The performance of visual is seriously impaired by illumination variation, different scales, viewpoint difference, spare texture, and computer power UAVs, etc. In this paper, a fast detector-free two-stage matching method proposed to improve the low-altitude UAVs. A perspective transformation module are incorporated into coarse fine...

10.3390/rs14225879 article EN cc-by Remote Sensing 2022-11-20

Human activities are inherently complex, and even simple household tasks involve numerous object interactions. To better understand these behaviors, it is crucial to model their dynamic interactions with the environment. The recent availability of affordable head-mounted cameras egocentric data offers a more accessible efficient means human-object in 3D environments. However, most existing methods for human activity modeling either focus on reconstructing models hand-object or human-scene...

10.48550/arxiv.2406.19811 preprint EN arXiv (Cornell University) 2024-06-28
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