Per-Pixel Feedback for improving Semantic Segmentation
Aditya Ganeshan
openalex +2 more sources
ABSTRACT As anthropogenic pressures increasingly impact marine ecosystems and the biodiversity they support, governance mechanisms for international biodiversity conservation have emerged. Seaweed habitats are important repositories for marine biodiversity, and they provide crucial ecosystem services that support both ocean and human health.
Shaun Beattie+7 more
wiley +1 more source
RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene. [PDF]
Nie D+5 more
europepmc +1 more source
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality
Menandro Roxas+4 more
openalex +2 more sources
Driving SDG15: The Role of HEIs in Biodiversity Conservation Through Digitalization and Reporting
ABSTRACT Education, research, and public engagement are key strategies guiding European higher education institutions (HEIs) in advancing the United Nations Sustainable Development Goals (SDGs). Through stakeholder, legitimacy, and resource‐based view theories, this study examines the contributions of HEIs to saving Life on Land (SDG15), focusing on ...
Assunta Di Vaio+4 more
wiley +1 more source
Road surface semantic segmentation for autonomous driving. [PDF]
Zhao H+5 more
europepmc +1 more source
Chalcogenide materials emerge as efficient agents for water purification, enabling adsorptive and photocatalytic removal of dyes, pharmaceuticals, and pesticides. This review highlights recent advances in synthesis, structural tuning, and pollutant interaction mechanisms, while addressing challenges of toxicity and scalability. Insights into the future
Damilola Caleb Akintayo+2 more
wiley +1 more source
Retraction: An improved beluga whale optimizer-Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images. [PDF]
PLOS One Editors.
europepmc +1 more source
Self‐Driving Microscopes: AI Meets Super‐Resolution Microscopy
This review examines the use of machine learning to automate super‐resolution optical microscopy, enabling the microscope to autonomously make decisions on what, when, and how to image. By eliminating the need for human intervention, this approach has the potential to enhance the versatility and accessibility of super‐resolution microscopy.
Edward N. Ward+3 more
wiley +1 more source
Semantic segmentation model of multi-source remote sensing images was used to extract winter wheat at tillering stage. [PDF]
Wu Y, Tang L, Yuan S.
europepmc +1 more source