Results 51 to 60 of about 3,488 (207)

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, J. Miller, A. Maftei, S. G. Kobourov   +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, Rita Cucchiara, CUCCHIARA, Rita, MANFREDI, MARCO, GRANA, Costantino, Marco Manfredi   +5 more
core   +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

A deep learning‐enabled toolkit for the 3D segmentation of ventricular cardiomyocytes

The Journal of Physiology, EarlyView.
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, Fabio Frangiamore, Frédéric Sonak, Josef Madl, Thomas Seidel, Peter Kohl, Eva A. Rog‐Zielinska   +6 more
wiley   +1 more source

Improved Spatial-Spectral Superpixel Hyperspectral Unmixing

Remote Sensing, 2019
In this paper, an unsupervised unmixing approach based on superpixel representation combined with regional partitioning is presented. A reduced-size image representation is obtained using superpixel segmentation where each superpixel is represented by ...
Mohammed Q. Alkhatib, Miguel Velez-Reyes   +1 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, Mirna Merkler, Gabriela Gil, Rebecca Davie, Shuzo Sakata   +4 more
wiley   +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, Verma, B, Zhang, L, Brijesh Verma (9842312), Brijesh Verma, Ligang Zhang (9847844)   +5 more
core   +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, Junkai Lin, Licheng Jiao, Xiaohui Yang, Yangyang Li   +4 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, Airin Antony, Nayanthara Prathap, Jamika Ann Roque, Daniel Peace, Jacquiline Romero   +5 more
wiley   +1 more source

Clinically Deployable Handwriting Biomarkers of Parkinson's Disease via Multiscale Attention and Bayesian–Genetic Optimization

Brain and Behavior, Volume 16, Issue 5, May 2026.
Tablet‐based handwriting tasks (spiral, meander, and wave) are transformed into unified images and analyzed using PD‐MGMA‐DSCNN, a lightweight multiscale gated attention network. Bayesian–genetic optimization improves performance, while SHAP attribution maps provide interpretable handwriting biomarkers for Parkinson's disease screening.
Khosro Rezaee, Ali Khalili Fakhrabadi
wiley   +1 more source