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Titlebook: Computer Vision - ECCV 2004; 8th European Confere Tomás Pajdla,Ji?í Matas Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 200

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樓主: 麻煩
41#
發(fā)表于 2025-3-28 18:25:28 | 只看該作者
42#
發(fā)表于 2025-3-28 22:03:43 | 只看該作者
https://doi.org/10.1007/978-0-230-11790-7rithms and yield a compact representation. Our experiments demonstrate robustness to wide appearance variations on non-planar surfaces, including changes in illumination, viewpoint, scale, and geometry of the scene.
43#
發(fā)表于 2025-3-28 23:45:42 | 只看該作者
44#
發(fā)表于 2025-3-29 06:31:49 | 只看該作者
45#
發(fā)表于 2025-3-29 11:02:22 | 只看該作者
46#
發(fā)表于 2025-3-29 13:14:31 | 只看該作者
A Visual Category Filter for Google Imageses may be small, the algorithm is designed to be robust and is capable of learning in either a totally unsupervised manner, or with a very limited amount of supervision..We demonstrate the method on image sets returned by Google’s image search for a number of object categories including bottles, camels, cars, horses, tigers and zebras.
47#
發(fā)表于 2025-3-29 19:06:09 | 只看該作者
48#
發(fā)表于 2025-3-29 23:16:45 | 只看該作者
49#
發(fā)表于 2025-3-30 01:38:08 | 只看該作者
Using Inter-feature-Line Consistencies for Sequence-Based Object Recognitionnuity information between features lines in a probabilistic framework. The associated recognition task is formulated as maximizing an . probability measure. The recognition problem is then further transformed to a shortest-path searching problem, and a dynamic-programming technique is used to solve it.
50#
發(fā)表于 2025-3-30 04:28:04 | 只看該作者
Texton Correlation for Recognition the second order variations across locations using Fisher linear discriminant analysis. We call it the Fisher texton model. Our two models are effective in the problem of face recognition from a single image across a wide range of illuminations, poses, and time.
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