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Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications.
This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI.
Computer Vision was a curiosity research field. Today, after five decades of research and development that include periods with both good and struggling progress, Computer Vision has found its applications everywhere from everyday cell phones to securities, entertainment, robotics, industrial, medical, and military domains. Advanced imaging sensors produce high-resolution images and videos at a high frame rate, even in 3D. High-performance low-power computers execute computation-hungry vision algorithms even in handheld devices. And, of course, Deep Neural Net Learning played the most critical role to turn Computer Vision into an amazingly impactful technology.
Owing to the availability of massive amounts of multimodal data and the reemergence of neural network-based methods, data-driven artificial intelligence (AI) appears to be on the verge of becoming the dominant technology of the future. While this development has basically shaken many established fields such as computer vision, defense, medicine, natural language processing (NLP), robotics, and smart transportation, data-driven AI has also raised many concerns about robustness, vulnerability to adversarial attacks, and bias.
In recent years, data-driven artificial intelligence (AI) systems have been utilized extensively in a variety of applications. With their exceptional performance over the past decade, Deep Learning (DL) models have revolutionized the Machine Learning and AI fields. DL models have out-performed nearly all classical Machine Learning models, such as SVMs, naive Bayes classifier, k-means clustering, and nearest neighbor, in many applications, including computer vision, natural language processing, and speech processing, by a significant margin. In particular, with their outstanding capacity for feature extraction and generalizations, DL models have made significant advances in a wide range of computer vision problems, such as object detection, object tracking, image classification, action recognition, image captioning, human pose estimation, face recognition, and semantic segmentation.
Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners