Titlebook: Visual Event Detection; Niels Haering,Niels Vitoria Lobo Book 2001 Springer-Verlag US 2001 artificial intelligence.classification.computer

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書目名稱Visual Event Detection影響因子(影響力)




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書目名稱Visual Event Detection網(wǎng)絡公開度學科排名




書目名稱Visual Event Detection被引頻次




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myocardium

我悲傷

2. - 1 can be solved in .(. + .) time and space on a unit-cost random-access machine with a word length of . bits. His algorithm works by traversing a so-called .. Two new related results are provided here. First, and most importantly, Thorup’s approach is generalized from undirected to directed net

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A Framework for the Design of Visual Event Detectors,w we obtain rich image descriptions based on color, texture, and motion measures, and how they are combined in a fat, flat hierarchy to infer object, shot, and event information. Throughout this chapter we will indicate the scope of the discussion by highlighting the relevant component within the fr

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Features and Classification Methods,ture extraction stage is the slowest of the modules, we consider classification without preprocessing in the first part of this section. Next we consider linear, quadratic, and eigen-analysis techniques for the determination of good subsets of features and classification, we will argue that the best

cataract

Results,etwork and demonstrate a method to extract powerful subsets of the features used to describe still images and video frames. Good feature sets can be found that preserve much of the robustness of the entire feature set using only about a quarter of all features. Sections 3 and 4 in this chapter show

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Summary and Discussion of Alternatives,d applied it to the detection of deciduous trees in still images, hunts in wildlife documentaries, and landings and rocket launches in general unconstrained commercial video. Before summarizing these topics we will turn to issues concerning the choice of the classifier and the feature set.

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intellect

Features and Classification Methods,ach to decide on the importance of the features and another approach to classify images. Some systematic approaches are presented which show that the best solutions involve the consideration of an exponential number of combinations of features. The results of the various classification methods are shown in Chapter 4.