Image Segmentation Using ConvNets

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 U-NetCNN architecture for semantic segmentation, featuring a U-shaped structure with skip connections. or SegNetDL model for semantic segmentation, utilizing encoder-decoder architecture with skip connections for image understanding., semantic segmentation has witnessed advancements in accuracy and efficiency. Modern architectures often integrate atrous convolutions, dilated convolutions, or spatial pyramid pooling to capture contextual information effectively.