In the field of digital image processing, the description of image content is one of the most crucial tasks. Indeed, it is a man- datory step for various applications, such as industrial vision, medi- cal imaging, content-based image retrieval, etc. The description of the image content is achieved through the computation of some predefined features, which can be performed at different scales. Among global features that describe the content of the whole image, the gray level histogram focuses on the distribution of gray levels within the image, while morphological features (e.g., the pattern spectrum) measure the distribution of object sizes in the image. De- spite their broad interest, such morphological size-distribution fea- tures are limited due to their monodimensional nature. Our goal is to review multidimensional extensions of these features able to deal with complementary information (such as shape, orientation, spec- tral, intensity, or spatial information). Moreover, we illustrate each multidimensional feature by an illustrative example that shows their relevance compared to the standard morphological size distribution. These features can be seen as relevant solutions when the standard monodimensional features fail to accurately represent the image
['Sébastien Lefèvre']
Beyond morphological size distribution