Object Detection and Localization Using Local and Global Features

Autor:	Daniel Eaton, Antonio Torralba, William T. Freeman, Kevin Murphy
Rok vydání:	2006
Předmět:	business.industry Computer science Detector Computer vision Artificial intelligence business Object (computer science) Object detection Image (mathematics)
Zdroj:	Toward Category-Level Object Recognition ISBN: 9783540687948 Toward Category-Level Object Recognition
DOI:	10.1007/11957959_20
Popis:	Traditional approaches to object detection only look at local pieces of the image, whether it be within a sliding window or the regions around an interest point detector. However, such local pieces can be ambiguous, especially when the object of interest is small, or imaging conditions are otherwise unfavorable. This ambiguity can be reduced by using global features of the image — which we call the “gist” of the scene — as an additional source of evidence. We show that by combining local and global features, we get significantly improved detection rates. In addition, since the gist is much cheaper to compute than most local detectors, we can potentially gain a large increase in speed as well.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f2ed61b8e5cacc3f9f943ea33d54b6af https://doi.org/10.1007/11957959_20 Zobrazit plný text záznamu