Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new set of fast algorithms for mathematical morphology I: idempotent geodesic transforms
CVGIP: Image Understanding
Automatic Plankton Image Recognition
Artificial Intelligence Review
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Robust Real-Time Face Detection
International Journal of Computer Vision
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
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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.