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| Description
| - Obrazové hrany v sobě nesou nejdůležitější část vnímané informace obsažené v obrazu. Zvýraznění hran tak výrazně přispívá ke zkvalitnění obrazu či detekci hranic objektů. Pojmem hrana rozumíme rychlou změnu v intenzitě pixelů. Hrany se skokovým profilem jsou snadno detekovatelné krátkými gradientními maskami. U rozmazaných a zašuměných obrázků je výhodnější použít např. Cannyho hranový detektor. V tomto příspěvku se zabýváme detekcí hran pomocí obou těchto technik v biomedicínských obrazech, které jsou nejprve zbaveny šumu metodou prahování wavelet koeficientů. Pro tento účel používáme diskrétní wavelet transformaci (DWT) a komplexní wavelet transformaci metodou duálního stromu (DTCWT) navrženou N. Kingsburym. DTCWT překonává DWT přibližnou invariancí k posunu a lepším prostorovým rozlišením. (cs)
- Image edges convey the most important part of the percepted information in an image. Edge detection contributes extensively to visual enhancement and objects boundaries detection. The term edge denotes an abrupt change in the image intensity. Sharp edges of a step-function profile may by easily detected by short gradient masks. In blurred and noisy images, multi-scale methods, such as the Canny detector, prove more convenient. In this paper, all the above edge detection techniques are applied to biomedical images prepocessed by wavelet coefficients shrinkage for noise reduction. For this purpose, we use either the Discrete Wavelet Transform (DWT) or the Dual-Tree Complex Wavelet Transform (DTCWT) designed by N. Kingsbury. The latter outperforms the DWT by its approximate shift invariance and better directional selectivity. Furthermore, owing to their sparsity and persistence, the DTCWT coefficients may be modelled by Hidden Markov models and utilized for edge detection.
- Image edges convey the most important part of the percepted information in an image. Edge detection contributes extensively to visual enhancement and objects boundaries detection. The term edge denotes an abrupt change in the image intensity. Sharp edges of a step-function profile may by easily detected by short gradient masks. In blurred and noisy images, multi-scale methods, such as the Canny detector, prove more convenient. In this paper, all the above edge detection techniques are applied to biomedical images prepocessed by wavelet coefficients shrinkage for noise reduction. For this purpose, we use either the Discrete Wavelet Transform (DWT) or the Dual-Tree Complex Wavelet Transform (DTCWT) designed by N. Kingsbury. The latter outperforms the DWT by its approximate shift invariance and better directional selectivity. Furthermore, owing to their sparsity and persistence, the DTCWT coefficients may be modelled by Hidden Markov models and utilized for edge detection. (en)
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| Title
| - Edge Detection in Biomedical Images
- Detekce hran v biomedicínských obrazech (cs)
- Edge Detection in Biomedical Images (en)
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| skos:prefLabel
| - Edge Detection in Biomedical Images
- Detekce hran v biomedicínských obrazech (cs)
- Edge Detection in Biomedical Images (en)
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| skos:notation
| - RIV/60461373:22340/08:00020857!RIV09-MSM-22340___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/60461373:22340/08:00020857
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - edge detection; wavelet transform; wavelet shrinkage; gradient masks; Canny detector; dual-tree complex wavelet transform; hidden Markov models (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - Proceedings the 8th International Scientific - Technological Conference Process Control 2008
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Procházka, Aleš
- Hošťálková, Eva
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
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| http://linked.open...n/vavai/riv/zamer
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| number of pages
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| http://purl.org/ne...btex#hasPublisher
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| https://schema.org/isbn
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| http://localhost/t...ganizacniJednotka
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| is http://linked.open...avai/riv/vysledek
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