In the end, critical infrastructure’s biggest quantum risk is the outdated, manual trust models of today. Only by replacing ...

To address the engineering challenge of detecting fine cracks on hybrid wind turbine towers, especially against complex water seepage backgrounds, this study aims to explore optimal image segmentation ...

An app for segmentation and classification of images of cells from optical microscope. This project uses marker controlled watershed (openCv), and pretrained ResNet-50 model (tensorflow) ...

Ever wondered how social media platforms decide how to fill our feeds? They use algorithms, of course, but how do these algorithms work? A series of corporate leaks over the past few years provides a ...

With the ongoing surge in global coastal development, understanding shoreline dynamics has become a critical issue, given the inherent vulnerability of coastal fringes to significant mobility.

Abstract: In order to overcome the problem of over-segmentation, a novel algorithm of watershed segmentation based on morphological gradient reconstructing is proposed in this paper. In the algorithm, ...

ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...

Abstract: Watershed algorithm is applied widely to image segmentation for its fast computing and high accuracy in locating the weak edges of adjacent regions. But classical watershed segmentation is ...