A comparative study on multivariate mathematical morphology
By E. Aptoula, Sébastien Lefèvre. Pattern Recognition, 2007
304 citations recorded by the source corpus.
Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/4df3de520a0f18561e9d2c42ce334ec7d860f013
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Jon Atli Benediktsson
- Jocelyn Chanussot
- E. Aptoula
- Shutao Li
- Yuliya Tarabalka
- Mathieu Fauvel
- James C. Tilton
- Jun Yu Li
In Computer Vision and Pattern Recognition
- Jon Atli Benediktsson
- Jocelyn Chanussot
- Mathieu Fauvel
- E. Aptoula
- Yuliya Tarabalka
- Shutao Li
- Antonio J. Plaza
- Pedram Ghamisi
In Media Technology
- Jon Atli Benediktsson
- Jocelyn Chanussot
- Shutao Li
- E. Aptoula
- Yuliya Tarabalka
- Mathieu Fauvel
- Jun Yu Li
- Antonio J. Plaza
Citations in the corpus
- Advances in Spectral-Spatial Classification of Hyperspectral Images (2013, background): “Then, the concepts of morphological profile and morphological neighborhood are presented.”
- Remote Sensing Image Retrieval With Global Morphological Texture Descriptors (2014, background): “According to one of the reviews on multivariate morphology [44], there is currently no shortage of extension methods that can be used in order to implement multivariate morphological operators and filters, but unfortunately, most of them have been developed with color images in mind and are usually ”
- Segmentation and classification of hyperspectral images using watershed transformation (2010, background): “An extensive literature on mathematical morphology for color and multispectral images is available [31, 32, 33, 34, 35, 36].”