An Improved Learning Framework for Covariant Local Feature Detection Article Swipe

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Nehal Doiphode , Rahul Mitra , Shuaib Ahmed , Arjun Jain ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1007/978-3-030-20876-9_17

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Affine Transformation

Artificial Intelligence

Concepts

Covariant transformation Constraint (computer-aided design) Feature (linguistics) Affine transformation Stability (learning theory) Detector Artificial intelligence Computer science Pattern recognition (psychology) Function (biology) Mathematics Machine learning Pure mathematics Geometry Telecommunications Biology Evolutionary biology Philosophy Linguistics

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1007/978-3-030-20876-9_17
OA Status: green
Cited By: 1
References: 25
Related Works: 20
OpenAlex ID: https://openalex.org/W2898861608

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OpenAlex ID: https://openalex.org/W2898861608

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DOI: https://doi.org/10.1007/978-3-030-20876-9_17

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Title: An Improved Learning Framework for Covariant Local Feature Detection

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-01-01

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Authors: Nehal Doiphode, Rahul Mitra, Shuaib Ahmed, Arjun Jain

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Landing page: https://doi.org/10.1007/978-3-030-20876-9_17

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OA status: green

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OA URL: https://arxiv.org/pdf/1811.00438

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Concepts: Covariant transformation, Constraint (computer-aided design), Feature (linguistics), Affine transformation, Stability (learning theory), Detector, Artificial intelligence, Computer science, Pattern recognition (psychology), Function (biology), Mathematics, Machine learning, Pure mathematics, Geometry, Telecommunications, Biology, Evolutionary biology, Philosophy, Linguistics

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Cited by: 1

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References (count): 25

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Related works (count): 20

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referenced_works_count	25
abstract_inverted_index
cited_by_percentile_year.max	94
cited_by_percentile_year.min	90
countries_distinct_count	2
institutions_distinct_count	4
citation_normalized_percentile.value	0.39093484
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False