All Outputs (10)

Protecting visual data privacy in offshore industry via underwater image inpainting. (2024)
Presentation / Conference Contribution
TOLIE, H.F., REN, J., CHEN, R. and ZHAO, H. 2024. Protecting visual data privacy in offshore industry via underwater image inpainting. In Proceedings of the 9th International conference on image, vision and computing 2024 (ICIVC 2024), 15-17 July 2024, Suzhou, China. Piscataway: IEEE [online], pages 281-286. Available from: https://doi.org/10.1109/ICIVC61627.2024.10837433

Leveraging advanced artificial intelligence (AI) methodologies offers the advantage of incorporating multiple expert viewpoints, thereby facilitating a more comprehensive inspection of underwater infrastructure. However, the implementation of AI tech... Read More about Protecting visual data privacy in offshore industry via underwater image inpainting..

Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement. (2024)
Presentation / Conference Contribution
TOLIE, H.F., REN, J., HASAN, M.J. and KANNAN, S. 2024. Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement. In Bouma, H., Prabhu, R., Yitzhaky, Y. and Kuijf, H.J. (eds.) Artificial intelligence for security and defence applications II: proceedings of the 2024 SPIE Security + defence, 16-20 September 2024, Edinburgh, UK. Proceedings of SPIE, 13206. Bellingham, WA; SPIE [online], paper 1320605. Available from: https://doi.org/10.1117/12.3030972

This work presents a depth image refinement technique designed to enhance the usability of a commercial camera in underwater environments. Stereo vision-based depth cameras offer dense data that is well-suited for accurate environmental understanding... Read More about Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement..

Promptable sonar image segmentation for distance measurement using SAM. (2024)
Presentation / Conference Contribution
TOLIE, H.F., REN, J., HASAN, M.J., KANNAN, S. and FOUGH, N. 2024. Promptable sonar image segmentation for distance measurement using SAM. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea; learning to measure sea health parameters (IEEE MetroSea 2024), 14-16 October 2024, Portorose, Slovenia. Piscataway: IEEE [online], pages 229-233. Available from: https://doi.org/10.1109/metrosea62823.2024.10765703

The subsea environment presents numerous challenges for robotic vision, including non-uniform light attenuation, backscattering, floating particles, and low-light conditions, which significantly degrade underwater images. This degradation impacts rob... Read More about Promptable sonar image segmentation for distance measurement using SAM..

Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising. (2024)
Presentation / Conference Contribution
LI, Y., REN, J., GAO, Z. and SUN, G. 2024. Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising. In Proceedings of the 2024 IEEE International geoscience and remote sensing symposium (IGARSS 2024), Athens, Greece, 7-12 July 2024. Piscataway: IEEE [online], pages 9210-9213. Available from: https://doi.org/10.1109/igarss53475.2024.10641314

As an effective tool for monitoring surface irregularities in remote sensing, hyperspectral anomaly detection (HAD) has garnered increasing attention. However, how to improve the detection accuracy remains a formidable challenge, due mainly to the no... Read More about Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising..

Enrich, distill and fuse: generalized few-shot semantic segmentation in remote sensing leveraging foundation model's assistance. (2024)
Presentation / Conference Contribution
GAO, T., AO, W., WANG, X.-A., ZHAO, Y., MA, P., XIE, M., FU, H., REN, J. and GAO, Z. 2024. Enrich, distill and fuse: generalized few-shot semantic segmentation in remote sensing leveraging foundation model’s assistance. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Computer Society conference on Computer vision and pattern recognition workshops (CVPRW 2024), 16-22 June 2024, Seattle, WA, USA. Piscataway: IEEE [online], pages 2771-2780. Available from: https://doi.org/10.1109/CVPRW63382.2024.00283

Generalized few-shot semantic segmentation (GFSS) unifies semantic segmentation with few-shot learning, showing great potential for Earth observation tasks under data scarcity conditions, such as disaster response, urban planning, and natural resourc... Read More about Enrich, distill and fuse: generalized few-shot semantic segmentation in remote sensing leveraging foundation model's assistance..

Image enhancement for UAV visual SLAM applications: analysis and evaluation. (2024)
Presentation / Conference Contribution
TIAN, Y., YUE, H. and REN, J. 2024. Image enhancement for UAV visual SLAM applications: analysis and evaluation. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 51-61. Available from: https://doi.org/10.1007/978-981-97-1417-9_20.

Although simultaneous localisation and mapping (SLAM) has been widely applied in a wide range of robotics and navigation applications, its applicability is severely affected by the quality of the acquired images, especially for those in unmanned aeri... Read More about Image enhancement for UAV visual SLAM applications: analysis and evaluation..

Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset. (2024)
Presentation / Conference Contribution
YAN, Y., LI, Y., LIN, H., SARKER, M.M.K., REN, J. and MCCALL, J. 2024. Underwater object detection for smooth and autonomous operations of naval missions: a pilot dataset. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 113-122. Available from: https://doi.org/10.1007/978-981-97-1417-9_11

Underwater object detection is essential for ensuring autonomous naval operations. However, this task is challenging due to the complexities of underwater environments that often degrade image quality, thereby hampering the performance of detection a... Read More about Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset..

MLM-LSTM: multi-layer memory learning framework based on LSTM for hyperspectral change detection. (2024)
Presentation / Conference Contribution
LI, Y., YAN, Y. and REN, C., LIU, Q. and SUN, H. 2024. MLM-LSTM: multi-layer memory learning framework based on LSTM for hyperspectral change detection. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 51-61. Available from: https://doi.org/10.1007/978-981-97-1417-9_5.

Hyperspectral change detection plays a critical role in remote sensing by leveraging spectral and spatial information for accurate land cover variation identification. Long short-term memory (LSTM) has demonstrated its effectiveness in capturing depe... Read More about MLM-LSTM: multi-layer memory learning framework based on LSTM for hyperspectral change detection..

Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. (2024)
Presentation / Conference Contribution
HASAN, M.J., ELYAN, E., YAN, Y., REN, J. and SARKER, M.M.K. 2024. Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 220-228. Available from: https://doi.org/10.1007/978-981-97-1417-9_21

Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to identify the heat los... Read More about Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies..

HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion. (2024)
Presentation / Conference Contribution
WU, Y., ZHANG, X., LIU, Q., XUE, D., SUN, H. and REN, J. 2024. HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 251-263. Available from: https://doi.org/10.1007/978-981-97-1417-9_24

Multi-object tracking in satellite videos (SV-MOT) is one of the most challenging tasks in remote sensing, its difficulty mainly comes from the low spatial resolution, small target and extremely complex background. The widely studied multi-object tra... Read More about HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion..