Foreground Extraction from Images and Video - Wang, Jue
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  • Broschiertes Buch

Separating foreground objects from natural images and video plays an important role in image and video editing tasks. Despite extensive study in the last two decades, this problem still remains challenging. In particular, extracting a foreground object in a static image involves determining both full and partial pixel coverage, also known as extracting a matte, which is a severely under-constrained problem. Segmenting spatio-temporal video objects from a video sequence is even harder since extracted foregrounds on adjacent frames must be both spatially and temporally coherent. Previous…mehr

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Produktbeschreibung
Separating foreground objects from natural images and video plays an important role in image and video editing tasks. Despite extensive study in the last two decades, this problem still remains challenging. In particular, extracting a foreground object in a static image involves determining both full and partial pixel coverage, also known as extracting a matte, which is a severely under-constrained problem. Segmenting spatio-temporal video objects from a video sequence is even harder since extracted foregrounds on adjacent frames must be both spatially and temporally coherent. Previous approaches usually require a large amount of user input and still suffer from inaccurate results and low computational efficiency. This book demonstrates efficient and accurate foreground extraction methods and systems by combining advanced computational algorithms with novel user interfaces. Our systems are capable of extracting high quality foreground objects from images and video with a limited amount of user input such as a few paint strokes of the mouse. We also demonstrate a variety of applications with the extracted foreground objects.
Autorenporträt
Jue Wang was born in Shanghai, China. She received the M.S. Degree in Computer Science from University of Massachusetts Boston, USA. During the time in the graduate school, she organized an association, Women in Science, to encourage the female students in science domains. Her research interests include data mining and machine learning.