Deep Semantic Image Segmentation Lecture
Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML), notably in Deep Neural Networks (DNNs), which are essentially massive ‘learning by experience/examples’ systems. Their applications span and revolutionize almost every human activity:
-Autonomous Systems (cars, drones, vessels),
-Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
-Medical Imaging and Diagnosis,
-Financial Engineering (forecasting and analytics), Big Data Analytics,
-Broadcasting, Internet and Communications,
-Robotics/Control
-Intelligent Human-Machine Interaction, Anthropocentric (human-centered)Computing,
-Smart Cities/Buildings and Assisted living.
-Scientific Modeling and Analytics.
Several DNN advances and challenges hit the news almost every day, arising discussions on AI ethics, privacy protection and its societal impact.
Semantic image segmentation is a very important computer vision task with several applications in autonomous systems perception, robotic vision and medical imaging. Recent semantic image segmentation methods rely on deep neural networks and aim to assign a specific class label to each pixel of the input image. This lecture overviews the topic and addresses some of the semantic image segmentation challenges, notably: Deep semantic Image Segmentation architectures. Skip connections. U-nets. BiSeNet. Semantic image segmentation performance, computational complexity and generalization.