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Titlebook: Integrating Graphics and Vision for Object Recognition; Mark R. Stevens,J. Ross Beveridge Book 2001 Springer Science+Business Media New Yo

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書目名稱Integrating Graphics and Vision for Object Recognition
編輯Mark R. Stevens,J. Ross Beveridge
視頻videohttp://file.papertrans.cn/469/468724/468724.mp4
叢書名稱The Springer International Series in Engineering and Computer Science
圖書封面Titlebook: Integrating Graphics and Vision for Object Recognition;  Mark R. Stevens,J. Ross Beveridge Book 2001 Springer Science+Business Media New Yo
描述.Integrating Graphics and Vision for Object Recognition.serves as a reference for electrical engineers and computer scientistsresearching computer vision or computer graphics. .Computer graphics and computer vision can be viewed as different sidesof the same coin. In graphics, algorithms are given knowledge aboutthe world in the form of models, cameras, lighting, etc., and infer(or render) an image of a scene. In vision, the process is the exactopposite: algorithms are presented with an image, and infer (orinterpret) the configuration of the world. This work focuses on usingcomputer graphics to interpret camera images: using iterativerendering to predict what should be visible by the camera and thentesting and refining that hypothesis. .Features of the book include: . . Manyillustrations to supplement the text; .. A novel approach to theintegration of graphics and vision; .. Genetic algorithms forvision; .. Innovations in closed loop object recognition. ...Integrating Graphics and Vision for Object Recognition. will beof interest to research scientists and practitioners working in fieldsrelated to the topic. It may also be used as an advanced-levelgraduate text.
出版日期Book 2001
關(guān)鍵詞algorithms; cognition; computer graphics; computer vision; genetic algorithms; knowledge; object recogniti
版次1
doihttps://doi.org/10.1007/978-1-4757-5524-4
isbn_softcover978-1-4419-4860-1
isbn_ebook978-1-4757-5524-4Series ISSN 0893-3405
issn_series 0893-3405
copyrightSpringer Science+Business Media New York 2001
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Introduction, object’s relationship to the scene in which it is embedded. Even though occlusion is determined by an object’s relationship to other objects in the scene, automatic recognition algorithms seldom approach the problem in terms of multi-object interaction. Instead, algorithms focus on locating a singl
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Render: Predicting Scenes,te the prediction must be in order for the two images to be comparable depends upon the specific application domain and the comparison metric used. Intuitively, the closer the prediction mirrors reality, the higher the chance the RMR (Render-Match-Refine) algorithm will be successful. Unfortunately,
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