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

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樓主: bradycardia
51#
發(fā)表于 2025-3-30 11:05:04 | 只看該作者
Rapid Localisation and Retrieval of Human Actions with Relevance Feedback,pts to perform similar action searches with localisation. We demonstrate how results can be enhanced using relevance feedback, considering how relevance feedback can be effectively applied in the context of localisation.
52#
發(fā)表于 2025-3-30 14:57:46 | 只看該作者
53#
發(fā)表于 2025-3-30 20:13:58 | 只看該作者
Manifold Learning and the Quantum Jensen-Shannon Divergence Kernel,y. Here we propose to use Isomap to embed the graphs using only local distance information onto a new vectorial space with a higher class separability. The experimental evaluation shows the effectiveness of the proposed approach.
54#
發(fā)表于 2025-3-30 20:51:30 | 只看該作者
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 within the large margin framework.
55#
發(fā)表于 2025-3-31 03:56:25 | 只看該作者
Richard Wilson,Edwin Hancock,William SmithProceedings of the 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013
56#
發(fā)表于 2025-3-31 06:13:58 | 只看該作者
57#
發(fā)表于 2025-3-31 11:03:17 | 只看該作者
https://doi.org/10.1007/978-3-7643-8675-7far beyond being only an application field. Indeed, it is a wide field with huge potential for developing novel concepts and algorithms and can be seen as a driving force for computer vision research. To emphasize this view of biomedical computer vision we consider a variety of important topics of b
58#
發(fā)表于 2025-3-31 13:37:51 | 只看該作者
59#
發(fā)表于 2025-3-31 20:50:47 | 只看該作者
60#
發(fā)表于 2025-4-1 01:34:41 | 只看該作者
https://doi.org/10.1007/978-3-7643-8675-7ecently proposed methods try to comprehensively use both image- and region-level features for more satisfactory performance, but they either cannot well explore the relationship between the two kinds of features or lead to heavy computational load. In this paper, by adopting support vector machine (
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