Frontiers

Cherry-Net: real-time segmentation algorithm of cherry maturity based on improved PIDNet

Introduction: Accurate identification of cherry maturity and precise detection of harvestable cherry contours are essential for the development of cherry-picking robots. However, occlusion, lighting ...
Frontiers

Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection

Introduction: Lung nodule detection is a crucial task for diagnosis and lung cancer prevention. However, it can be extremely difficult to identify tiny nodules in medical images since pulmonary ...
GitHub

-2001-05566--Image-Segmentation-Using-Deep-Learning--A-Survey.yaml

- en: 来源：[https://ar5iv.labs.arxiv.org/html/2001.05566](https://ar5iv.labs.arxiv.org/html/2001.05566) applications such as scene understanding, medical image ...
IEEE

Adaptive Semi-Supervised Fuzzy C-Means Method With Local Spatial Information and Pre-Clustering for Image Segmentation

Abstract: The semi-supervised fuzzy C-means clustering algorithm is an improved version of the fuzzy C-means algorithm, designed to utilize a small amount of supervised information to enhance the ...
Scientific Research Publishing

Blacklaws, C. (2018) Algorithms: Transparency and Accountability. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376 ...

ABSTRACT: The rapidly evolving field of explainable Artificial Intelligence (XAI) offers new avenues in forensic psychiatry, addressing critical needs for transparency and reliability in ...
IEEE

Application of grid-based C-means clustering algorithm for image segmentation

Abstract: C-means clustering algorithms have proven effective for image segmentation, but are limited by the following aspects: 1) the determination of a priori number of clusters. If the number of ...