Results 91 to 100 of about 9,167 (192)

Hyperspectral Imaging: The Intelligent Eye to Uncover the Password of Plant Science

Modern Agriculture, Volume 3, Issue 2, December 2025.
Hyperspectral imaging (HSI) has emerged as a powerful non‐destructive technique for characterisation of the plant phenotype and physiological traits. The ongoing development of cost‐effective hardware, coupled with standardised acquisition protocols and open‐access spectral libraries, is accelerating its integration with multi‐omics approaches to ...
Jingyan Song, Haifeng Liang, Bingjie Lu, Jing Guo, Yuan Gao, Xiao Hu, Manlin Yang, Xiaofan Li, Zhenyu Wang, Yongqi Chen, Yinyin Zhang, Shen Su, Zhangyun Gao, Shijie Li, Ping Chen, Jing Wang, Wanneng Yang, Hui Feng   +17 more
wiley   +1 more source

An unsupervised semantic segmentation method that combines the ImSE-Net model with SLICm superpixel optimization

International Journal of Digital Earth
In the field of remote sensing, using a large amount of labeled image data to supervise the training of fully convolutional networks for the semantic segmentation of images is expensive.
Zenan Yang, Haipeng Niu, Xiaoxuan Wang, Liang Huang, Kui Yang   +4 more
doaj   +1 more source

Drone‐based polarization imaging system for leaf spot severity determination in peanut plants

The Plant Phenome Journal, Volume 8, Issue 1, December 2025.
Abstract In this study, we introduce a new approach for enhancing peanut phenotyping through a polarization imaging platform. With leaf spot disease posing significant threats to peanut (Arachis hypogae L.) crops, our research addresses the need for accurate and efficient detection methods.
Joshua Larsen, Jeffrey. Dunne, Robert Austin, Cassondra Newman, Michael Kudenov   +4 more
wiley   +1 more source

An Object-Aware Network Embedding Deep Superpixel for Semantic Segmentation of Remote Sensing Images

Remote Sensing
Semantic segmentation forms the foundation for understanding very high resolution (VHR) remote sensing images, with extensive demand and practical application value.
Ziran Ye, Yue Lin, Baiyu Dong, Xiangfeng Tan, Mengdi Dai, Dedong Kong   +5 more
doaj   +1 more source

Relieve the Demand for Labeled Data of Deep Learning Models for Hydraulic Conductivity Field Tasks in Groundwater Through Self‐Supervised Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 4, December 2025.
Abstract Deep learning (DL) has shown great potential in solving groundwater problems but often requires large labeled data sets, which are expensive and time‐consuming to obtain. In this study, we introduce a self‐supervised learning approach based on a masked autoencoder (MAE)—an encoder‐decoder architecture that reconstructs randomly masked input ...
Kai Ji, Zhengkun Zhou, Bo Zou, Ruiting Yang, Baoqing Zhang   +4 more
wiley   +1 more source

Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

IET Image Processing
The fuzzy C‐means clustering (FCM) algorithm is widely used in greyscale and colour image segmentation, especially in real colour images. However, in the process of interested regions extraction, it performs barely satisfactory due to the use of single ...
Xie Zeyu, Luo Xiao, Zhao Defang, Chen Xinyu   +3 more
doaj   +1 more source

Text segmentation using superpixel clustering

IET Image Processing, 2017
Text segmentation is important for text image analysis and recognition; however, it is challenging due to noise and complex background in natural scenes. Superpixel‐based image representation can enhance robustness to noise and local disturbances, but conventional superpixel algorithms are difficult to obtain the complete stroke regions and accurate ...
Yuanping Zhu, Kuang Zhang
openaire   +1 more source

Selective Multiple Classifiers for Weakly Supervised Semantic Segmentation

CAAI Transactions on Intelligence Technology, Volume 10, Issue 6, Page 1688-1702, December 2025.
ABSTRACT Existing weakly supervised semantic segmentation (WSSS) methods based on image‐level labels always rely on class activation maps (CAMs), which measure the relationships between features and classifiers. However, CAMs only focus on the most discriminative regions of images, resulting in their poor coverage performance.
Zilin Guo, Dongyue Wu, Changxin Gao, Nong Sang   +3 more
wiley   +1 more source

Automatic glioma segmentation based on adaptive superpixel

BMC Medical Imaging, 2019
Background The automatic glioma segmentation is of great significance for clinical practice. This study aims to propose an automatic method based on superpixel for glioma segmentation from the T2 weighted Magnetic Resonance Imaging.
Yaping Wu, Zhe Zhao, Weiguo Wu, Yusong Lin, Meiyun Wang   +4 more
doaj   +1 more source

Unsupervised instance segmentation with superpixels

Pattern Recognition
Instance segmentation is essential for numerous computer vision applications, including robotics, human-computer interaction, and autonomous driving. Currently, popular models bring impressive performance in instance segmentation by training with a large number of human annotations, which are costly to collect.
openaire   +2 more sources