A5101897430- Advanced Image and Video Retrieval Techniques
- Cross-Border Cooperation and Integration
- Robotics and Sensor-Based Localization
- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Advanced Vision and Imaging
- Advanced Image Processing Techniques
- Image Retrieval and Classification Techniques
- Robotic Path Planning Algorithms
- Medical Image Segmentation Techniques
PLA Information Engineering University
2024-2025
Abstract Visual place recognition (VPR) involves obtaining robust image descriptors to cope with differences in camera viewpoints and drastic external environment changes. Utilizing multiscale features improves the robustness of descriptors; however, existing methods neither exploit generated during feature extraction nor consider redundancy problem when fusing information are enhanced. We propose a novel encoding strategy—convolutional multilayer perceptron orthogonal fusion...
Geolocating a street-view image by matching it with geotagged satellite images is crucial for location assessment. However, the perspective disparity between and presents significant challenges. To address issue, mainstream approach to convert ground-level perspective. So reference are not only required center but also coverage be consistent street view sometimes even consistency in north direction, which difficult achieve practical applications. This paper introduces ground-breaking method...
Cross-view geolocation, which aims to geolocate ground-view images using reference satellite imagery, is a challenging task that requires effective strategies minimize significant disparities between images. In this paper, we propose novel approach address challenge. By exploiting the projection relationship ground and images, are able convert into satellite-view thereby mitigating inherent two perspectives. To enhance converted further, employ conditional generative adversarial network...
Cross-view geo-localization (CVGL), which involves matching and retrieving satellite images to determine the geographic location of a ground image, is crucial in GNSS-constrained scenarios. However, this task faces significant challenges due substantial viewpoint discrepancies, complexity localization scenarios, need for global localization. To address these issues, we propose novel CVGL framework that integrates vision foundational model DINOv2 with an advanced feature mixer. Our introduces...