Classification-driven stochastic watershed. Application to multispectral segmentation
Consequently, a complete method, based on a classification-driven stochastic watershed is introduced. This approach requires a unique and robust parameter: the number of classes which is the same for similar images.
Moreover, an efficient way to select factor axes, of Factor Correspondence Analysis (FCA), based on signal to noise ratio on factor pixels is presented.
Document Type: Research Article
Publication date: January 1, 2008
Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.
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