Titlebook: Computer Analysis of Images and Patterns; 15th International C Richard Wilson,Edwin Hancock,William Smith Conference proceedings 2013 Sprin

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書(shū)目名稱(chēng)Computer Analysis of Images and Patterns影響因子(影響力)




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns被引頻次




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns被引頻次學(xué)科排名




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns年度引用




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns讀者反饋




書(shū)目名稱(chēng)Computer Analysis of Images and Patterns讀者反饋學(xué)科排名

厚臉皮

Rapid Localisation and Retrieval of Human Actions with Relevance Feedback,rapid localisation and retrieval of human actions from large video databases. We first temporally segment the database and calculate a histogram-match score for each segment against the query. High-scoring, adjacent segments are joined into candidate localised regions using a noise-robust localisati

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Intractable

從屬

Maximizing Edit Distance Accuracy with Hidden Conditional Random Fields,models such as Hidden Markov Models or Conditional Random Fields, whose quality is measured through character and word error rates while they are usually not trained to optimize such criterion. We propose an efficient method for learning Hidden Conditional Random Fields to optimize the error rate wi

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Background Recovery by Fixed-Rank Robust Principal Component Analysis,s a promising approach for solving problems such as noise removal, video background modeling, and removal of shadows and specularity. RPCA utilizes the fact that the background is common in multiple views of a scene, and attempts to decompose the data matrix constructed from input images into a low-

Trabeculoplasty

Manifold Learning and the Quantum Jensen-Shannon Divergence Kernel,l commonly used alternatives. In this paper, we study the separability properties of this kernel and we propose a way to compute a low-dimensional kernel embedding where the separation of the different classes is enhanced. The idea stems from the observation that the multidimensional scaling embeddi

低位的人或事

Spatio-temporal Manifold Embedding for Nearly-Repetitive Contents in a Video Stream,arities observed in frame sequences are captured by defining two types of correlation graphs: an intra-correlation graph in the spatial domain and an inter-correlation graph in the temporal domain. The presented work is novel in that it does not utilise any prior information such as the length and c

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