
doi: 10.1007/11552499_51
Many applications such as image compression, pre-processing or segmentation require some information from the regions composing an image. The main objective of this paper is to define a methodology to extract some local information from an image. Each region is characterized in terms of homogeneity (region composed with the same grey-level or a single texture) and its type (textured or uniform). The decision criterion is based on the use of classical texture attributes (cooccurrence matrix and grey-levels moments) and a support vector machine in order to realize the fusion of the different attributes. We then characterize each region considering its type by appropriate features.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
