2017/05/09 by Omer Faruk Gulban, Ömer Faruk Gülban, Gulban, Omer Faruk
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · #Advanced Image Fusion Techniques #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Geochemistry and Geologic Mapping #Medical Image Segmentation Techniques #Quantitative Methods (q-bio.QM) #Remote-Sensing Image Classification #q-bio.QM #stat.AP
paper · pdf · doi:10.48550/arxiv.1705.03457
13 pages, 3 figures, short paper, submitted to Austrian Journal of Statistics compositional data analysis special issue, first revision, fix rendering error in fig2
openalex publication_date 2017/05/09 · arxiv created 2018/06/11 · arxiv updated 2018/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a novel application of compositional data analysis methods in the context of color image processing. A vector decomposition method is proposed to reveal compositional components of any vector with positive components followed by compositional data analysis to demonstrate the relation between color space concepts such as hue and saturation to their compositional counterparts. The proposed methods are applied to a magnetic resonance imaging dataset acquired from a living human brain and a digital color photograph to perform image fusion. Potential future applications in magnetic resonance imaging are mentioned and the benefits/disadvantages of the proposed methods are discussed in terms of color image processing.