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Description
  • Analýza hlavních komponent (PCA) patří mezi základní statistické metody, které jsou často užívány při zpracování signálů k redukci dimenze nebo k dekorelaci dat. Článek je věnován dvěma různým aplikacím PCA ve zpracování digitálního obrazu. První použití je při redukci barev v obrze ze tří barevných složek do jediné obsahující maximum informace. Druhá aplikace spočívá v použití hodnot vlastních vektorů při detekci natočení sledovaného objektu v obraze, přičemž samotný objekt může být vymezen různými metodami. V článku je nejprve stručně zmíněna teorie a dále je věnován zpracování vybraných reálných snímků. (cs)
  • Principal component analysis (PCA) is one of the statistical techniques frequently used in signal processing to the data dimension reduction or to the data decorrelation. Presented paper deals with two distinct applications of PCA in image processing. The first application consists in the image colour reduction while the three colour components are reduced into one containing a major part of information. The second use of PCA takes advantage of eigenvectors properties for determination of selected object orientation. Various methods can be used for previous object detection. Quality of image segmentation implies to results of the following process of object orientation evaluation based on PCA as well. Presented paper briefly introduces the PCA theory at first and continues with its applications mentioned above. Results are documented for the selected real pictures.
  • Principal component analysis (PCA) is one of the statistical techniques frequently used in signal processing to the data dimension reduction or to the data decorrelation. Presented paper deals with two distinct applications of PCA in image processing. The first application consists in the image colour reduction while the three colour components are reduced into one containing a major part of information. The second use of PCA takes advantage of eigenvectors properties for determination of selected object orientation. Various methods can be used for previous object detection. Quality of image segmentation implies to results of the following process of object orientation evaluation based on PCA as well. Presented paper briefly introduces the PCA theory at first and continues with its applications mentioned above. Results are documented for the selected real pictures. (en)
Title
  • Principal Component Analysis in Image Processing
  • Principal Component Analysis in Image Processing (en)
  • Analýza hlavních komponent ve zpracování obrazu (cs)
skos:prefLabel
  • Principal Component Analysis in Image Processing
  • Principal Component Analysis in Image Processing (en)
  • Analýza hlavních komponent ve zpracování obrazu (cs)
skos:notation
  • RIV/60461373:22340/05:00014399!RIV06-MSM-22340___
http://linked.open.../vavai/riv/strany
  • MUD/1-MUD/4
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6046137306)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 538439
http://linked.open...ai/riv/idVysledku
  • RIV/60461373:22340/05:00014399
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Principal Component Analysis; Image Processing; Image Analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [C735E0254905]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Praha
http://linked.open...i/riv/nazevZdroje
  • 13th Annual Conference Proceedings Technical Computing Prague 2005
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Mudrová, Martina
  • Procházka, Aleš
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • Humusoft
https://schema.org/isbn
  • 80-7080-577-3
http://localhost/t...ganizacniJednotka
  • 22340
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