Abstract: Salient image extrema detection has been a critical problem due to the importance of extrema in the description of image significant structures. However, due to image noise, traditional extrema detection methods like non-maximum suppression cannot give ideal results. Filtering techniques are commonly used to reduce the negative effect of image noise, but can hardly produce satisfied results because the filter sizes cannot be properly determined beforehand. Accordingly, in this paper,a new salient image extrema detection algorithm is proposed by applying attribute based morphological analysis. During detection,the image extrema saliency is analyzed and valuated by its morphological attributes, and the salient extrema is effectively detected without the need of noise filtering. An efficient algorithm implementation is also provided based on flooding simulations and successfully applied to both cases of visual attention selection and independent moving target detection. It is proved that the algorithm is not only noise-resistant in salient extrema detection, but also applicable to a wide range of applications.

Key words: salient image extrema detection, morphological analysis, extrema saliency, flooding simulation