6533b85afe1ef96bd12b8cbf
RESEARCH PRODUCT
Archetypal analysis: an alternative to clustering for unsupervised texture segmentation
Irene EpifanioIsmael Caberosubject
Acoustics and UltrasonicsComputer scienceMaterials Science (miscellaneous)General MathematicsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologylocal granulometriesMathematical morphology01 natural sciencesTexture (geology)archetypeImage (mathematics)010104 statistics & probability0202 electrical engineering electronic engineering information engineeringRadiology Nuclear Medicine and imagingSegmentationmathematical morphology0101 mathematicsCluster analysisInstrumentationimage segmentationtexture analysislcsh:R5-920business.industrylcsh:MathematicsPattern recognitionImage segmentationlcsh:QA1-939DiscriminantSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceFocus (optics)businesslcsh:Medicine (General)Biotechnologydescription
Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The proposed procedure has been tested on a remote sensing image application with local granulometries, providing promising results.
year | journal | country | edition | language |
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2019-07-01 |