Computer Vision and Image Processing in the Deep Learning Era

The increasing interest in using machine learning and deep learning methods to solve vision problems has led to the development of systems that can solve vision problems in different fields. Computer Vision and Image Processing in the Deep Learning Era is a premier reference source for researchers and developers, covering topics such as brain tumor detection, heart disease prediction, and medical image detection.

Format: Paperback / softback
Length: 325 pages
Publication date: 31 May 2023
Publisher: IGI Global

In recent years, there has been a growing interest in leveraging machine learning and, more recently, deep learning methods in conjunction with other vision and image processing techniques to develop systems that address various vision-related problems across different fields. This trend has led to a pressing need for academicians, developers, and researchers from industry to come together and present, share, and explore traditional and emerging areas of computer vision, machine learning, deep learning, and their combinations.

Computer Vision and Image Processing in the Deep Learning Era aims to serve as a valuable resource for researchers and developers by presenting original, innovative, and state-of-the-art algorithms and architectures for applications in computer vision, image processing, biometrics, virtual and augmented reality, and other related areas. This comprehensive reference source brings together the expertise of a growing international community of researchers actively engaged in applying machine learning and deep learning methods to vision and robotics. By covering topics such as brain tumor detection, heart disease prediction, and medical image detection, this premier publication offers exceptional insights and knowledge to medical professionals, faculty and students in higher education, business leaders and managers, librarians, government officials, researchers, and academicians alike.

The field of computer vision and image processing has witnessed significant advancements in recent decades, driven by the rapid development of technology and the increasing demand for intelligent systems that can interpret and analyze visual data. Machine learning and deep learning, in particular, have emerged as powerful tools for addressing complex visual tasks, enabling computers to learn from large datasets and make accurate predictions or decisions.

One of the key advantages of machine learning and deep learning in computer vision is their ability to handle large amounts of data. These methods can analyze and process vast volumes of images, videos, and other visual data, making them suitable for applications such as object detection, facial recognition, and autonomous vehicles. By leveraging the power of neural networks, these algorithms can learn to recognize patterns and features in the data, allowing them to make predictions or decisions with high accuracy.

Another advantage of these methods is their ability to adapt to changing environments. Deep learning algorithms, in particular, are known for their ability to learn and improve over time as they are exposed to new data. This allows them to handle variations in lighting, pose, and other factors that can affect the accuracy of traditional image processing techniques.

However, the development of machine learning and deep learning algorithms also presents several challenges. One of the main challenges is the need for large amounts of high-quality data to train these algorithms. This can be a significant barrier to entry for researchers and developers who may not have access to large datasets or may not have the resources to collect and annotate data. Additionally, the training of deep learning algorithms can be computationally intensive and requires specialized hardware and software.

Despite these challenges, the use of machine learning and deep learning in computer vision is growing rapidly. Researchers and developers are continuously exploring new algorithms and architectures to improve the performance and accuracy of these methods. Additionally, there is a growing interest in combining these methods with other vision and image processing techniques, such as computer graphics, to create more sophisticated and interactive visual systems.

In conclusion, the field of computer vision and image processing in the deep learning era has the potential to revolutionize the way we interact with and analyze visual data. By leveraging the power of machine learning and deep learning, we can develop intelligent systems that can interpret and understand visual information, enabling us to solve complex problems and make informed decisions. However, there are still significant challenges to be addressed, and ongoing research and development are necessary to fully realize the potential of these methods.

Dimension: 279 x 216 (mm)
ISBN-13: 9781799888932