HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

NEW GENERAL FEATURES BASED ON SUPERPIXELS FOR IMAGE SEGMENTATION LEARNING

Abstract : Segmenting an image is usually one of the major and most challenging steps in the pipeline of biomedical image analysis. One classical and promising approach is to consider seg-mentation as a classification task, where the aim is to assign to each pixel the label of the objects it belongs to. Pixels are therefore described by a vector of features, where each feature is calculated on the pixel itself or, more frequently, on a sliding window centered on the pixel. In this work, we propose to replace the sliding window by superpixels, i.e. regions which adapt to the image content. We call the resulting features SAF (Superpixel Adaptive Feature). Their contribution is highlighted on a biomedical database of melanocytes images. Qualitative and quantitative analyses show that they are better suited for segmentation purposes than the sliding window approach.
Complete list of metadata

Cited literature [8 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01276132
Contributor : Vaïa Machairas Connect in order to contact the contributor
Submitted on : Thursday, February 18, 2016 - 6:19:10 PM
Last modification on : Wednesday, November 17, 2021 - 12:31:24 PM
Long-term archiving on: : Thursday, May 19, 2016 - 11:16:56 AM

File

Machairas_HAL_ISBI.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01276132, version 1

Citation

V Machairas, T Baldeweck, T Walter, Etienne Decencière. NEW GENERAL FEATURES BASED ON SUPERPIXELS FOR IMAGE SEGMENTATION LEARNING. International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republic. ⟨hal-01276132⟩

Share

Metrics

Record views

2901

Files downloads

605