Difference between revisions of "Publications/lazzara.14.das"
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(Created page with "{{Publication | published = true | date = 2013-12-10 | authors = Guillaume Lazzara, Thierry Géraud, Roland Levillain | title = Planting, Growing and Pruning Trees: Connected ...") |
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| + | #REDIRECT [[Publications/lazzara.14.das]] |
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| − | {{Publication |
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| + | [[Category:PublicationRedirected]] |
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| − | | published = true |
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| − | | date = 2013-12-10 |
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| − | | authors = Guillaume Lazzara, Thierry Géraud, Roland Levillain |
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| − | | title = Planting, Growing and Pruning Trees: Connected Filters Applied to Document Image Analysis |
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| − | | booktitle = Proceedings of the 11th IAPR International Workshop on Document Analysis Systems (DAS) |
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| − | | address = Tours, France |
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| − | | organization = IAPR |
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| − | | note = Accepted |
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| − | | project = Image |
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| − | | urllrde = lazzara.14.das |
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| − | | abstract = Mathematical morphology, when used in the field of document image analysis and processing, is often limited to some classical yet basic tools. The domain however features a lesser-known class of powerful operators, called connected filters. These operators present an important property: they do not shift nor create contours. Most connected filters are linked to a tree-based representation of an image's contents, where nodes represent connected components while edges express an inclusion relation. By computing attributes for each node of the tree from the corresponding connected component, then selecting nodes according to an attribute-based criterion, one can either filter or recognize objects in an image. This strategy is very intuitive, efficient, easy to implement, and actually well-suited to processing images of magazines. Examples of applications include image simplification, smart binarization, and object identification. |
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| − | | lrdeprojects = Olena |
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| − | | lrdenewsdate = 2013-12-10 |
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| − | | type = inproceedings |
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| − | | id = lazzara.14.das |
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| − | | bibtex = |
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| − | @InProceedings<nowiki>{</nowiki> lazzara.14.das, |
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| − | author = <nowiki>{</nowiki>Guillaume Lazzara and Thierry G\'eraud and Roland |
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| − | Levillain<nowiki>}</nowiki>, |
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| − | title = <nowiki>{</nowiki>Planting, Growing and Pruning Trees: Connected Filters |
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| − | Applied to Document Image Analysis<nowiki>}</nowiki>, |
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| − | booktitle = <nowiki>{</nowiki>Proceedings of the 11th IAPR International Workshop on |
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| − | Document Analysis Systems (DAS)<nowiki>}</nowiki>, |
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| − | year = <nowiki>{</nowiki>2014<nowiki>}</nowiki>, |
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| − | address = <nowiki>{</nowiki>Tours, France<nowiki>}</nowiki>, |
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| − | month = apr, |
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| − | organization = <nowiki>{</nowiki>IAPR<nowiki>}</nowiki>, |
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| − | note = <nowiki>{</nowiki>Accepted<nowiki>}</nowiki>, |
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| − | project = <nowiki>{</nowiki>Image<nowiki>}</nowiki>, |
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| − | abstract = <nowiki>{</nowiki>Mathematical morphology, when used in the field of |
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| − | document image analysis and processing, is often limited to |
||
| − | some classical yet basic tools. The domain however features |
||
| − | a lesser-known class of powerful operators, called |
||
| − | connected filters. These operators present an important |
||
| − | property: they do not shift nor create contours. Most |
||
| − | connected filters are linked to a tree-based representation |
||
| − | of an image's contents, where nodes represent connected |
||
| − | components while edges express an inclusion relation. By |
||
| − | computing attributes for each node of the tree from the |
||
| − | corresponding connected component, then selecting nodes |
||
| − | according to an attribute-based criterion, one can either |
||
| − | filter or recognize objects in an image. This strategy is |
||
| − | very intuitive, efficient, easy to implement, and actually |
||
| − | well-suited to processing images of magazines. Examples of |
||
| − | applications include image simplification, smart |
||
| − | binarization, and object identification. <nowiki>}</nowiki> |
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| − | <nowiki>}</nowiki> |
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| − | |||
| − | }} |
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Revision as of 13:00, 3 January 2014
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