About: Principal Component Analysis in Image Processing     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
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/60461373:22340/05:00014398!RIV06-MSM-22340___
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
  • 538440
http://linked.open...ai/riv/idVysledku
  • RIV/60461373:22340/05:00014398
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
  • [277F68E4889D]
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...n/vavai/riv/zamer
http://localhost/t...ganizacniJednotka
  • 22340
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software