Results 121 to 130 of about 19,033 (238)
UrbanClipAtlas: A Visual Analytics Framework for Event and Scene Retrieval in Urban Videos
Computer Graphics Forum, EarlyView.Abstract Extracting actionable insights from long‐duration urban videos is often labor‐intensive: analysts must manually sift through raw footage to pinpoint target events or uncover broader behavioral trends. In this work, we present UrbanClipAtlas, a visual analytics system for exploring long urban videos recorded at street intersections ...
Joel Perca +5 more
wiley +1 more source
Textile and colour defect detection using deep learning methods
Coloration Technology, EarlyView.Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui +2 more
wiley +1 more source
A Unified Deep Learning Framework for Instance Segmentation Across Diverse Cytological Stains
Cytopathology, EarlyView.Transformer‐based unified cytology segmentation across Papanicolaou, Feulgen and AgNOR achieves stain‐invariant performance. Mask2Former maximises boundary precision (AP75) on the combined dataset, enabling one model to replace multiple stain‐specific deployments without accuracy loss while simplifying clinical integration.
Luís Otávio Santos +6 more
wiley +1 more source
A New Experimental Setup to Study the Olfactory Behaviour of Trichogramma Egg Parasitoids
Entomologia Experimentalis et Applicata, EarlyView.We developed an experimental setup to assess odor‐induced egg‐laying behavior in tiny egg‐parasitoid wasps. This Y‐shaped olfactometer, coupled with an AI‐based image‐recognition model, automatically quantifies egg parasitism by Trichogramma wasps. Olfactory‐driven egg‐laying preferences were analyzed using Bayesian inference.
Cécile Bresch, Louise van Oudenhove
wiley +1 more source
International Endodontic Journal, EarlyView.ABSTRACT Aim Apical periodontitis (AP) diagnosis primarily relies on periapical radiographs (PRs) and the Periapical Index (PAI) scoring system. However, existing automated approaches often simplify PAI into binary categories or ignore essential clinical metadata, limiting diagnostic performance and applicability.
Jiyun Lee +3 more
wiley +1 more source
STMIIT—Symbol Tags for Massive Insects Identification and Tracking
Insect Science, EarlyView.
Ruigang Wang +7 more
wiley +1 more source
Artificial Intelligence in Periodontology: A Systematic Review
Journal of Periodontal Research, EarlyView.AI shows promise across periodontology, with deep learning achieving strong performance for image‐based diagnosis of periodontitis. However, limited data diversity, inconsistent metrics, and scarce external validation raise concerns about generalizability and clinical applicability.
Antonin Tichy +7 more
wiley +1 more source
FieldDino: Rapid In‐Field Stomatal Anatomy and Physiology Phenotyping
Plant, Cell &Environment, EarlyView.ABSTRACT Stomatal anatomy and physiology define CO2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding.
Edward Chaplin +3 more
wiley +1 more source
Restoration Ecology, EarlyView.Abstract Introduction Endangered fish species, such as Delta smelt (Hypomesus transpacificus), in the San Francisco Estuary are threatened by a multitude of anthropogenic stressors. Tidal wetland restoration can partially mitigate these stressors by increasing food availability of aquatic invertebrate prey, but the efficacy of restoration remains ...
Gabriel Ng +3 more
wiley +1 more source
Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang +12 more
wiley +1 more source