An electron microscopy image can capture atoms arranged in a crystal lattice or defects threading through a semiconductor ...

Automated lesion segmentation is essential for DR screening, but current deep learning models often lack robustness, ...

Abstract: Machine learning algorithm for multi-modal image segmentation is extensively employed in medical analysis and diagnosis. Clustering represents a mainstream approach for image segmentation, ...

Abstract: This paper examines the applicability of the Bat Algorithm (BA) and its variants as optimization frameworks for various image segmentation paradigms. Rather than introducing a new ...

This study aims to investigate the application of visual information processing mechanisms in the segmentation of stem cell (SC) images. The cognitive principles underlying visual information ...

Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and ...

A research team led by Prof. WANG Huanqin at the Institute of Intelligent Machines, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, recently proposed a semi-supervised ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

2025 GIFNet: One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion CVPR 2025 DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image ...

This repository includes an enhanced lightweight grid-based ground segmentation algorithm that efficiently separates ground points from obstacle points in 3D point clouds. The algorithm is designed ...