Hillebrand et al 2012a - Revision history

Scipediacontent at 14:01, 21 January 2021

2021-01-21T14:01:06Z

Scipediacontent: Scipediacontent moved page Draft Content 821471847 to Hillebrand et al 2012a

2020-10-14T13:52:44Z

Scipediacontent moved page Draft Content 821471847 to Hillebrand et al 2012a

Scipediacontent: Created page with " == Abstract == The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs..."

2020-10-14T13:52:40Z

Created page with " == Abstract == The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs..."

New page

== Abstract ==

The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_821471847-beopen885-6310-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf]

← Older revision		Revision as of 14:01, 21 January 2021
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	The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.		The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.
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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_821471847-beopen885-6310-document.pdf</pdf>~~


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	* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf]		* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf]
		+
		+	* [http://www.springerlink.com/index/pdf/10.1007/978-3-642-33212-8_18 http://www.springerlink.com/index/pdf/10.1007/978-3-642-33212-8_18],
		+	: [http://dx.doi.org/10.1007/978-3-642-33212-8_18 http://dx.doi.org/10.1007/978-3-642-33212-8_18]
		+
		+	* [https://link.springer.com/chapter/10.1007%2F978-3-642-33212-8_18 https://link.springer.com/chapter/10.1007%2F978-3-642-33212-8_18],
		+	: [https://www.scipedia.com/public/Hillebrand_et_al_2012a https://www.scipedia.com/public/Hillebrand_et_al_2012a],
		+	: [https://dblp.uni-trier.de/db/conf/annpr/annpr2012.html#HillebrandKWK12 https://dblp.uni-trier.de/db/conf/annpr/annpr2012.html#HillebrandKWK12],
		+	: [https://doi.org/10.1007/978-3-642-33212-8_18 https://doi.org/10.1007/978-3-642-33212-8_18],
		+	: [https://rd.springer.com/chapter/10.1007/978-3-642-33212-8_18 https://rd.springer.com/chapter/10.1007/978-3-642-33212-8_18],
		+	: [https://academic.microsoft.com/#/detail/200726266 https://academic.microsoft.com/#/detail/200726266]

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