The field of computer vision has experienced remarkable progress in recent years, largely attributed to the unprecedented advancements in deep learning models and their practical applications across diverse domains. This research topic is dedicated to presenting and exploring the latest developments in computer vision, with a particular emphasis on the transition from theoretical deep learning models to their real-world applications.
This research topic focuses on the practical application of deep learning models in computer vision, translating theoretical advancements into real-world solutions. It offers a platform to share success stories and case studies illustrating the effective deployment of such models in areas like medical imaging, remote sensing, and multimedia affective computing. Furthermore, with the importance of interpretability and transparency in deep learning models emphasized, these models become more complex and understanding their decision-making processes is crucial. The articles in this collection explore techniques to enhance interpretability, providing insights into the inner workings of these systems. Additionally, computer vision plays a pivotal role in addressing challenges in medical applications and Internet of Things (IoT) technologies, which revolutionizes medical imaging, diagnosis, and treatment, and enables visual perception in smart and connected IoT devices.
This research topic covers a wide range of topics, including but not limited to:
- Image classification and segmentation
- Image understanding and scene analysis
- Image denoising and reconstruction
- Psychophysical analysis of visual perception
- Image generation and super-resolution
- Visual perceptual evaluation
- Object detection, tracking and recognition
- Deep learning for specialized computer vision tasks such as medical image processing, remote sensing, hyperspectral imaging, and thermal imaging
- Multimedia affective computing
- Interpretable deep learning models
- Pattern recognition for IoT and medical applications.
The field of computer vision has experienced remarkable progress in recent years, largely attributed to the unprecedented advancements in deep learning models and their practical applications across diverse domains. This research topic is dedicated to presenting and exploring the latest developments in computer vision, with a particular emphasis on the transition from theoretical deep learning models to their real-world applications.
This research topic focuses on the practical application of deep learning models in computer vision, translating theoretical advancements into real-world solutions. It offers a platform to share success stories and case studies illustrating the effective deployment of such models in areas like medical imaging, remote sensing, and multimedia affective computing. Furthermore, with the importance of interpretability and transparency in deep learning models emphasized, these models become more complex and understanding their decision-making processes is crucial. The articles in this collection explore techniques to enhance interpretability, providing insights into the inner workings of these systems. Additionally, computer vision plays a pivotal role in addressing challenges in medical applications and Internet of Things (IoT) technologies, which revolutionizes medical imaging, diagnosis, and treatment, and enables visual perception in smart and connected IoT devices.
This research topic covers a wide range of topics, including but not limited to:
- Image classification and segmentation
- Image understanding and scene analysis
- Image denoising and reconstruction
- Psychophysical analysis of visual perception
- Image generation and super-resolution
- Visual perceptual evaluation
- Object detection, tracking and recognition
- Deep learning for specialized computer vision tasks such as medical image processing, remote sensing, hyperspectral imaging, and thermal imaging
- Multimedia affective computing
- Interpretable deep learning models
- Pattern recognition for IoT and medical applications.