Titlebook: Machine Learning and Image Interpretation; Terry Caelli,Walter F. Bischof Book 1997 Springer Science+Business Media New York 1997 computer

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书目名称Machine Learning and Image Interpretation影响因子(影响力)




书目名称Machine Learning and Image Interpretation影响因子(影响力)学科排名




书目名称Machine Learning and Image Interpretation网络公开度




书目名称Machine Learning and Image Interpretation网络公开度学科排名




书目名称Machine Learning and Image Interpretation被引频次




书目名称Machine Learning and Image Interpretation被引频次学科排名




书目名称Machine Learning and Image Interpretation年度引用




书目名称Machine Learning and Image Interpretation年度引用学科排名




书目名称Machine Learning and Image Interpretation读者反馈




书目名称Machine Learning and Image Interpretation读者反馈学科排名

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ith respect to domain knowledge. Although the chapterseach illuminate different aspects of image interpretation, all utilizea common approach - one that asserts such interpretation mustinvolve perceptual learning in terms of automated knowledgeacquisition and application, as well as feedback and con

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,Cite—Scene Understanding and Object Recognition,d-loop system where the current interpretation state is used to drive the lower level image processing functions. The theory presented in this chapter is applied to a new object recognition and scene understanding system called . which is described in detail.

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See++: An Object Oriented Theory of Task Specific Vision,sing requirements are provided by an image query language which is controlled by feedback from the knowledge base as a function of partial image interpretation. The See. framework has application to a spectrum of problems such as teleoperation in a low bandwidth environment. This domain is discussed and used to illustrate the system.

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ABC: Biologically Motivated Image Understanding,ognition (ABC) model. The model comprises a series of recurrent self-organising topological maps, which form a general proposal for the understanding of cognition, and the visual component of this model is the subject of this chapter.

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Fuzzy Conditional Rule Generation for the Learning and Recognition of 3D Objects from 2D Images,nition performance, and the implementation of a complete object recognition system that does not rely on perfect or synthetic data. We report a recognition rate of 80% for unseen single object scenes in a database of 18 non-trivial objects.

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