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) ...

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.

This thesis focuses on leveraging Image Processing, Computer Vision, Machine Learning, and Deep Learning, particularly the Vision Transformer (ViT) model, for early identification of Alzheimer’s ...

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 ...

Introduction: Deep learning has significantly advanced medical image analysis, enabling precise feature extraction and pattern recognition. However, its application in computational material science ...

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the ...

This project implements three image segmentation algorithms - Region Growing, Watershed, and K-Means, to separate an object from its background, evaluated using the Jaccard Similarity Coefficient.

Abstract: Cell image segmentation is an important step in medical image processing and analysis system. The watershed algorithm is usually used to segment cells in the image. However, it is difficult ...