Results 51 to 60 of about 986,520 (234)
Remote Sensing, 2022 Convolutional neural networks (CNNs) can extract advanced features of joint spectral–spatial information, which are useful for hyperspectral image (HSI) classification.
Cai-Hong Mu, Z. Dong, Yi Liu
semanticscholar +1 more source
A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
Applied Sciences, 2018 An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed.
Wei Zhao +4 more
doaj +1 more source
Artificial Intelligence in Dermatology: Current Applications and Future Directions
JEADV Clinical Practice, EarlyView.This scoping review of 56 studies maps AI applications in dermatology. Image‐based classification for skin cancer detection dominates (48%), followed by clinical decision support (21%), teledermatology triage (11%), and predictive analytics (11%). While deep learning algorithms demonstrate diagnostic performance comparable to clinicians in controlled ...
Sofía Pérez‐Lalinde +1 more
wiley +1 more source
Superpixel image segmentation.
, 2018 (a) original image. (b) result of superpixel segmentation.
Guan Yang (520726) +4 more
core +1 more source
Adaptive Superpixel Segmentation With Non-Uniform Seed Initialization
IEEE Transactions on Big DataSuperpixel segmentation is a powerful image pre-processing tool in computer vision applications. However, fewer superpixel segmentation methods consider automatically determining the number of initial superpixels.
Xin-Lin Xie +3 more
semanticscholar +1 more source
Remote Sensing in Ecology and Conservation, EarlyView.High‐resolution visible‐light imagery from low‐altitude unmanned aerial vehicles, combined with superpixel segmentation and a Random Forest classifier, provides an efficient and scalable framework for mapping and monitoring crustose coralline algae and reef habitats.
Po‐Chien Lin +2 more
wiley +1 more source
A Hybrid Model Based on Superpixel Entropy Discrimination for PolSAR Image Classification
Remote Sensing, 2022 Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel.
Jili Sun, Lingdong Geng, Yize Wang
doaj +1 more source
Quantitative Metrics for Edge Bundling of Network Visualizations
Computer Graphics Forum, EarlyView.Abstract Edge bundling is widely used for reducing visual clutter in large 2D network and trajectory visualizations. Various edge bundling methods have been proposed, each producing qualitatively distinct outputs for the same data; however, few quantitative metrics exist for systematic evaluation. In this paper, we propose a set of quantitative metrics
M. Wallinger +3 more
wiley +1 more source
Learning Superpixel Relations for Supervised Image Segmentation
, 2014 In this paper we propose to extend the well known graph cut segmentation framework by learning superpixel relations and use them to weight superpixel-to-superpixel edges in a superpixel graph.
Costantino Grana +5 more
core +1 more source
Time course sensor‐based phenotyping can predict Ascochyta blight disease severity in Cicer species
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract Ascochyta blight is a widely occurring chickpea fungal disease that can cause severe yield loss. Breeding for crop resistance benefits from high‐throughput evaluation of plant–pathogen interactions in genotypes which can serve as sources of resistance.
Florian Tanner +9 more
wiley +1 more source