Results 71 to 80 of about 6,095 (211)
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 fats for fast foreground extraction
, 2015 Fast foreground extraction is an important and challenging problem. Although GrabCut can perform well in foreground extraction, the average accuracy is not satisfactory, and more importantly, its computational cost is large.
Li, Xuelong +3 more
core +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
Superpixels Optimized by Color and Shape [PDF]
, 2018 Image over-segmentation is formalized as the approximation problem when a large image is segmented into a small number of connected superpixels with best fitting colors. The approximation quality is measured by the energy whose main term is the sum of squared color deviations over all pixels and a regularizer encourages round shapes.
Vitaliy Kurlin, Donald Harvey
openaire +2 more sources
Content-driven superpixels and their applications
, 2013 This thesis develops a new superpixel algorithm that displays excellent visual reconstruction of the original image. It achieves high stability across multiple random initialisations, achieved by producing superpixels directly corresponding to local ...
Lowe, Richard
core +1 more source
Superpixel-based class-semantic texton occurrences for natural roadside vegetation segmentation
, 2017 Vegetation segmentation from roadside data is a field that has received relatively little attention in present studies, but can be of great potentials in a wide range of real-world applications, such as road safety assessment and vegetation condition ...
Ligang Zhang +5 more
core +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
Superpixel Estimation for Hyperspectral Imagery [PDF]
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014 In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and ...
Pegah Massoudifar +2 more
openaire +2 more sources
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
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