Suppressing nonsalient patterns by smoothing the amplitude spectrum at an appropriate scale has been shown to effectively detect the visual saliency in the frequency domain. Different filter scales are required for different types of salient objects. We observe that the optimal scale for smoothing amplitude spectrum shares a specific relation with the size of the salient region. Based on this observation and the bottom-up saliency detection characterized by spectrum scale-space analysis for natural images, we propose to detect visual saliency, especially with salient objects of different sizes and locations via automatic adaptive amplitude spectrum analysis. We not only provide a new criterion for automatic optimal scale selection but also reserve the saliency maps corresponding to different salient objects with meaningful saliency information by adaptive weighted combination. The performance of quantitative and qualitative comparisons is evaluated by three different kinds of metrics on the four most widely used datasets and one up-to-date large-scale dataset. The experimental results validate that our method outperforms the existing state-of-the-art saliency models for predicting human eye fixations in terms of accuracy and robustness.
This paper proposes a novel idea for ionogram trace enhancement to obtain the “clean” ionogram with real ionospheric echo signals, which is very important for further ionogram interpretation and scaling manually or automatically. Two methods based on ionogram trace pixel connectedness are adopted: max filter and connected components labeling. The experiments show that both methods are feasible and effective, and parameter selection and time complexity of the two methods are analyzed.
IG method is an excellent salient region detection method as its good generality and well-defined boundaries. In this
paper, an improved method based on IG method is proposed to generate saliency map for phytoplankton microscopic
images. This method utilizes the characteristics of phytoplankton microscopic images, through Gaussian low-pass filter
to reduce high frequency components corresponding to water stains and dust specks. On the basis of luminance and color
used in IG method, saturation is added to determine saliency due to that the saturation of background is lower than that
of cells. The experimental results show that the proposed method can not only improve visual quality significantly, but
also obtain higher precision and better recall rates compared with IG method.