Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers.
Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing.
There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see.
Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing.
Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Engineering Science Reference Challenges and applications for implementing machine learning in computer vision, hardcover/ramgopal kashyap
