Results 41 to 50 of about 2,106 (179)
Advanced Intelligent Systems, Volume 8, Issue 7, July 2026.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often
Ronghua Shang +4 more
doaj +1 more source
Video‐Based Rainfall Opportunistic Sensing in Hydrology: A Lightweight Machine Learning Approach
Water Resources Research, Volume 62, Issue 7, July 2026.Abstract Video‐based rainfall measurement is a frontier topic in opportunistic sensing; however, rapid, accurate, and robust identification of rainfall‐related rain‐streak features from dynamic videos remains a key challenge, especially for monitoring devices with limited computational resources.
Yongcheng Jin +5 more
wiley +1 more source
TurboPixels: A Superpixel Segmentation Algorithm Suitable for Real-Time Embedded Applications
Applied SciencesSuperpixel segmentation aims to produce a consistent grouping of pixels. In recent years, the importance of superpixel segmentation has increased in computer vision since it offers useful primitives for extracting image features and simplifies the ...
Abiel Aguilar-González +5 more
doaj +1 more source
Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing
European Journal of Remote Sensing, 2022 Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of ...
Qiang Guan +4 more
doaj +1 more source
A deep learning‐enabled toolkit for the 3D segmentation of ventricular cardiomyocytes
The Journal of Physiology, Volume 604, Issue 13, Page 5561-5584, 1 July 2026.Abstract figure legend 3D cardiomyocyte segmentation enables comprehensive analyses of myocardial microstructure in health and disease; however, it is technically demanding. We present an open‐source toolkit for this task, which reduces challenges associated with sample preparation, image restoration, segmentation and proofreading.
Joachim Greiner +6 more
wiley +1 more source
Automatic Image Segmentation With Superpixels and Image-Level Labels
IEEE Access, 2019 Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing.
Xinlin Xie +4 more
doaj +1 more source
Multifiber Array‐Based Photometry System for Multiregional Functional Mapping in the Mouse Brain
European Journal of Neuroscience, Volume 63, Issue 12, June 2026.Existing fiber photometry approaches suffer from invasiveness and limited scalability. A newly developed multifiber array‐based photometry system allows targeting multiple brain regions with less invasiveness. The system was validated in two jGCaMP8s‐expressing mouse lines by monitoring GABAergic neural population activity across multiple brain regions
Manil Bradai +4 more
wiley +1 more source
A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Recent developments in hyperspectral sensors have made it possible to acquire hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures.
Subhashree Subudhi +3 more
doaj +1 more source
Advancing Photonic Inverse Design with Interpretable Machine Learning
Advanced Photonics Research, Volume 7, Issue 5, May 2026.The work applies the interpretable machine learning technique called LIME (local interpretable model‐agnostic explanations) to the inverse design of photonic chips, revealing hidden optimization patterns and guiding better starting designs. Using insights from LIME improves performance of two‐mode multiplexers, showing interpretable methods can ...
Lirandë Pira +5 more
wiley +1 more source