Extending Morphological Signatures for Visual Pattern Recognition

Morphological signatures are powerful descriptions of the image content which are based on the framework of mathematical morphology. These signatures can be computed on a global or local scale: they are called pattern spectra (or granulometries and antigranulometries) when measured on the complete images and morphological profiles when related to single pixels. Their goal is to measure shape distribution instead of intensity distribution, thus they can be considered as a relevant alternative to classical intensity histograms, in the context of visual pattern recognition. A morphological signature (either a pattern spectrum or a morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element. Even if it can be used directly to solve various pattern recognition problems related to image data, the simple definitions given in the binary and grayscale cases limit its usefulness in many applications. In this paper, we introduce several 2-D extensions to the classical 1-D morphological signature. More precisely, we elaborate morphological signatures which try to gather more image information and do not only include a dimension related to the object size, but also consider on a second dimension a complementary information relative to size, intensity or spectral information. Each of the 2-D morphological signature proposed in this paper can be defined either on a global or local scale and for a particular kind of images among the most commonly ones (binary, grayscale or multispectral images). We also illustrate these signatures by several real-life applications related to object recognition and remote sensing.