Enhancing Visual Understanding with Semantic Segmentation in Computer Vision

EasyChair Preprint 12356

Abstract

This paper explores the significance of semantic segmentation in advancing visual understanding and its applications across various domains. The paper begins by elucidating the fundamental principles of semantic segmentation, highlighting its role in scene parsing, object detection, and image segmentation tasks. It delves into the evolution of semantic segmentation techniques, from traditional methods to state-of-the-art deep learning architectures, such as convolutional neural networks (CNNs) and their variants. Furthermore, the paper investigates the challenges inherent in semantic segmentation, including class imbalance, occlusion, and computational complexity, along with recent advancements and strategies to address these challenges. It also discusses the importance of datasets and evaluation metrics in benchmarking the performance of semantic segmentation models.

Keyphrases: Segmentation, computer, semantic

@booklet{EasyChair:12356,
  author    = {Julia Anderson and Kurez Oroy},
  title     = {Enhancing Visual Understanding with Semantic Segmentation in Computer Vision},
  howpublished = {EasyChair Preprint 12356},
  year      = {EasyChair, 2024}}