Image Segmentation Using ConvNets
Explore image segmentation concepts and techniques using convolutional neural networks such as U-Net and advanced models. Understand semantic, instance, and panoptic segmentation along with attention mechanisms to improve pixel-level classification and mask generation. This lesson guides you through practical implementation using pretrained U-Net models in a Jupyter Notebook to segment images effectively.
Our exploration into image segmentation is pivotal for understanding computer vision applications. Image segmentation involves partitioning an image into meaningful segments, enabling the identification and analysis of objects or regions. Within the realm of deep learning, convolutional neural networks (CNNs) have proven instrumental in automating this process.
Types of image segmentation
Semantic segmentation, a foundational concept, focuses on pixel-level classification, assigning each pixel to a specific class. Leveraging deep neural networks like