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Knowing a good feature when you see it: Ground truth and methodology to evaluate local features for recognition

Abstract:

While the majority of computer vision systems are based on representing images by local features, the design of the latter has been so far mostly empirical. In this Chapter we propose to tie the design of local features to their systematic evaluation on a realistic ground-truthed dataset. We propose a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affine (de)formation model. We propose novel metrics...

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Publisher copy:
10.1007/978-3-642-12848-6_2

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Journal:
Studies in Computational Intelligence
Volume:
285
Pages:
27-49
Publication date:
2010-01-01
DOI:
ISSN:
1860-949X
URN:
uuid:200d8c6c-47e4-4f61-96fe-7b4a41d28bd2
Source identifiers:
298690
Local pid:
pubs:298690
Language:
English

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