Results 61 to 70 of about 9,167 (192)

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

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

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

Hybrid CNN–GCN framework for brain tumor MRI classification: A graph‐based approach to smart healthcare diagnostics

Journal of Applied Clinical Medical Physics, Volume 27, Issue 4, April 2026.
Abstract Background Accurate classification of brain tumors is a major challenge in neuro‐oncology, as the heterogeneity of tumor morphology and the overlap of radiological features limit the effectiveness of conventional diagnostic approaches. Early and reliable tumor characterization is essential for treatment planning, prognosis, and improved ...
Mus'ab S. Alkasasbeh, Khalid Hassan Ibnaouf, Naser M. Ahmed, Azhar Abdul Rahman, Dheyaa Nabeel Abbas, Arar Al Tawil, Hajo Idriss, Hamzeh Taha Alkasasbeh   +7 more
wiley   +1 more source

TurboPixels: A Superpixel Segmentation Algorithm Suitable for Real-Time Embedded Applications

Applied Sciences
Superpixel 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, Alejandro Medina Santiago, Jorge Antonio Orozco Torres, J. A. de Jesús Osuna-Coutiño, Madaín Pérez Patricio, Néstor A. Morales-Navarro   +5 more
doaj   +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

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, Tongyu Xu, Shuai Feng, Fenghua Yu, Kai Song   +4 more
doaj   +1 more source

Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation

, 2015
Video segmentation is a stepping stone to understanding video context. Video segmentation enables one to represent a video by decomposing it into coherent regions which comprise whole or parts of objects.
Pavlovic, Vladimir, Yi, Saehoon
core   +1 more source

Design and Optimization of Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces Based on Conditional Multi‐Task Deep Learning

Advanced Science, Volume 13, Issue 24, 27 April 2026.
A conditional multi‐task deep learning framework is developed for designing and optimizing Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces (FHPEMs). This framework achieves joint spectro‐polarimetric learning and unified forward–inverse design.
Chenjie Gong, Haodong Shi, Qi Wang, Yingchao Li, Hongyu Sun, Jiayu Wang, Guanlin Li, Jian Zhang, Qiang Fu, Huilin Jiang   +9 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, Gang Xie, Xinying Xu, Lei Cui, Jinchang Ren   +4 more
doaj   +1 more source