Matches in UGent Biblio for { <https://biblio.ugent.be/publication/978290#aggregation> ?p ?o. }
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- aggregation classification "A1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2010".
- aggregation format "application/pdf".
- aggregation hasFormat 978290.bibtex.
- aggregation hasFormat 978290.csv.
- aggregation hasFormat 978290.dc.
- aggregation hasFormat 978290.didl.
- aggregation hasFormat 978290.doc.
- aggregation hasFormat 978290.json.
- aggregation hasFormat 978290.mets.
- aggregation hasFormat 978290.mods.
- aggregation hasFormat 978290.rdf.
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- aggregation hasFormat 978290.txt.
- aggregation hasFormat 978290.xls.
- aggregation hasFormat 978290.yaml.
- aggregation isPartOf urn:issn:0178-2789.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Noise- and compression-robust biological features for texture classification".
- aggregation abstract "Texture classification is an important aspect of many digital image processing applications such as surface inspection, content-based image retrieval, and biomedical image analysis. However, noise and compression artifacts in images cause problems for most texture analysis methods. This paper proposes the use of features based on the human visual system for texture classification using a semisupervised, hierarchical approach. The texture feature consists of responses of cells which are found in the visual cortex of higher primates. Classification experiments on different texture libraries indicate that the proposed features obtain a very high classification near 97%. In contrast to other well-established texture analysis methods, the experiments indicate that the proposed features are more robust to various levels of speckle and Gaussian noise. Furthermore, we show that the classification rate of the textures using the presented biologically inspired features is hardly affected by image compression techniques.".
- aggregation authorList BK692404.
- aggregation endPage "922".
- aggregation issue "6-8".
- aggregation startPage "915".
- aggregation volume "26".
- aggregation aggregates 1009829.
- aggregation aggregates 978296.
- aggregation isDescribedBy 978290.
- aggregation similarTo s00371-010-0455-9.
- aggregation similarTo LU-978290.