Zobrazeno 1 - 10
of 554
pro vyhledávání: '"FOOKES, CLINTON"'
Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning
Autor:
Hewawiththi, Nethmi S., Viduranga, M. Mahesha, Warnasooriya, Vanodhya G., Fernando, Tharindu, Suraweera, Himal A., Sridharan, Sridha, Fookes, Clinton
Unmanned aerial vehicle-assisted disaster recovery missions have been promoted recently due to their reliability and flexibility. Machine learning algorithms running onboard significantly enhance the utility of UAVs by enabling real-time data process
Externí odkaz:
http://arxiv.org/abs/2412.10756
Efficient Position Determination of Highly Directional RF Emitters via Iterated Beampattern Analysis
The localization of RF emitters has attracted significant attention particularly within the domain of electronic warfare. Most localization methods found in open literature are based on omnidirectional emitters. Directional emitters significantly mod
Externí odkaz:
http://arxiv.org/abs/2411.04364
This study introduces a new framework for 3D person re-identification (re-ID) that leverages readily available high-resolution texture data in 3D reconstruction to improve the performance and explainability of the person re-ID task. We propose a meth
Externí odkaz:
http://arxiv.org/abs/2410.00348
Autor:
Ranasingha, Chinthaka, Gammulle, Harshala, Fernando, Tharindu, Sridharan, Sridha, Fookes, Clinton
Early diagnosis of Autism Spectrum Disorder (ASD) is an effective and favorable step towards enhancing the health and well-being of children with ASD. Manual ASD diagnosis testing is labor-intensive, complex, and prone to human error due to several f
Externí odkaz:
http://arxiv.org/abs/2409.18438
Autor:
de Lima, Lucas Carvalho, Griffiths, Ethan, Haghighat, Maryam, Denman, Simon, Fookes, Clinton, Borges, Paulo, Brünig, Michael, Ramezani, Milad
This paper presents a novel approach for robust global localisation and 6DoF pose estimation of ground robots in forest environments by leveraging cross-view factor graph optimisation and deep-learned re-localisation. The proposed method addresses th
Externí odkaz:
http://arxiv.org/abs/2409.16680
Autor:
Mahendren, Sutharsan, Rahman, Saimunur, Koniusz, Piotr, Fernando, Tharindu, Sridharan, Sridha, Fookes, Clinton, Moghadam, Peyman
We propose PseudoNeg-MAE, a novel self-supervised learning framework that enhances global feature representation of point cloud mask autoencoder by making them both discriminative and sensitive to transformations. Traditional contrastive learning met
Externí odkaz:
http://arxiv.org/abs/2409.15832
Occupational outcomes like entrepreneurship are generally considered personal information that individuals should have the autonomy to disclose. With the advancing capability of artificial intelligence (AI) to infer private details from widely availa
Externí odkaz:
http://arxiv.org/abs/2409.03765
The incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). By integrating fundamental knowledge and governing physical laws, these models achieve enhanced robustness and interpretability. In
Externí odkaz:
http://arxiv.org/abs/2408.01026
Autor:
Chierchia, Remi, Lebrat, Leo, Ahmedt-Aristizabal, David, Salvado, Olivier, Fookes, Clinton, Cruz, Rodrigo Santa
Managing chronic wounds is a global challenge that can be alleviated by the adoption of automatic systems for clinical wound assessment from consumer-grade videos. While 2D image analysis approaches are insufficient for handling the 3D features of wo
Externí odkaz:
http://arxiv.org/abs/2407.19652
Autor:
Islam, Md Rakibul, Hassan, Riad, Nazib, Abdullah, Nguyen, Kien, Fookes, Clinton, Islam, Md Zahidul
Deep learning has achieved outstanding accuracy in medical image segmentation, particularly for objects like organs or tumors with smooth boundaries or large sizes. Whereas, it encounters significant difficulties with objects that have zigzag boundar
Externí odkaz:
http://arxiv.org/abs/2407.09828