Deep learning in computer vision
-
M. Chitra
Abstract
Deep learning led to the rebirthrebirth of the subject of computer vision and provided a new driving force in the further development of new theoretical concepts and real-life applications. In this chapter, the authors provide an overview of the more significant waves and new propulsive trends in deep learning for computer vision as well as their evolution and impact. This was achieved by presenting the general principles of deep learning, describing the roles of these new architectures, as well as the advancements in the hardware that enable image computing. Applications of deep learning for dynamic computer vision in real-world operations such as augmented reality, self-driven cars, specific shops, security and surveillance, and healthcare are illustrated. This chapter also provides basic information on model evaluation, hyperparameterhyperparameter tuning, and consideration of the importance of the model’s ethical issues. Discussing these components more comprehensively in the framework of the chapter, the authors point to revolutionary features of contemporary deep learning technologies and their prospects as the future of computer vision research and development.
Abstract
Deep learning led to the rebirthrebirth of the subject of computer vision and provided a new driving force in the further development of new theoretical concepts and real-life applications. In this chapter, the authors provide an overview of the more significant waves and new propulsive trends in deep learning for computer vision as well as their evolution and impact. This was achieved by presenting the general principles of deep learning, describing the roles of these new architectures, as well as the advancements in the hardware that enable image computing. Applications of deep learning for dynamic computer vision in real-world operations such as augmented reality, self-driven cars, specific shops, security and surveillance, and healthcare are illustrated. This chapter also provides basic information on model evaluation, hyperparameterhyperparameter tuning, and consideration of the importance of the model’s ethical issues. Discussing these components more comprehensively in the framework of the chapter, the authors point to revolutionary features of contemporary deep learning technologies and their prospects as the future of computer vision research and development.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
