The primary objective of this work is to implement an algorithm for contextual segmentation of morphological features in a material microstructure dataset. Towards this goal, a task pipeline has been ...

PyTorch implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation - vinceecws/SegNet_PyTorch. ... This model was employed to examine the feasibility of machine ...

This article proposes a simple and effective method for image subject segmentation. Our research mainly focuses on the characteristics of material images in the experimental platform. Through in-depth ...

Note that machine learning is commonly used for image segmentation in computer vision (Kumar and Hebert, 2003; Tu and Bai, 2010) and medical image analysis (Tu et al., 2007; Morra et al., 2009; Tu and ...

Until recently, segmentation required large, compute-intensive neural networks. This made it difficult to run these deep learning models without a connection to cloud servers. In their latest work ...

This study implements image processing and Mask Regional Convolutional Neural Network (Mask RCNN) on high resolution images to create an object detection and segmentation model for aquaculture ...

It is a deep learning framework intended for image classification and segmentation. It has features like simple CPU and GPU switching, optimised model definition and configuration, computation ...