Global Image Feature Extraction Using Slope Pattern Spectra

A novel algorithm inspired by the integral image representation to derive an increasing slope segment pattern spectrum (called the Slope Pattern Spectrum for convenience), is proposed. Although many pattern spectra algorithms have their roots in mathematical morphology, this is not the case for the proposed algorithm. Granulometries and their resulting pattern spectra are useful tools for texture or shape analysis in images since they characterize size distributions. Many applications such as texture classification and segmentation have demonstrated the importance of pattern spectra for image analysis. The Slope Pattern Spectra algorithm extracts a global image signature from an image based on increasing slope segments. High Steel Low Alloy (HSLA) steel and satellite images are used to demonstrate that the proposed algorithm is a fast and robust alternative to granulometric methods. The experimental results show that the proposed algorithm is efficient and has a faster execution time than Vincent’s linear granulometric technique.