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Circular histogram thresholding is a new threshold selection method in color image segmentation. However, the method of the existing circular histogram thresholding based on the Otsu Criteria lacks the generality of using the circular histogram. In order to improve the effectiveness and reduce the complexity of thresholding on circular histogram, this paper firstly introduces the Lorenz curve into circular histogram. Then the circular histogram is expanded into the linearized histogram in clockwise or anticlockwise direction by the optimal index of the Lorenz curve. In the end, the entropy thresholding of the linearized circular histogram is adopted to choose the optimal threshold to obtain the object of color images. Many experimental results show that the proposed method has better effectiveness and adaptability than the existing circular thresholding utilizing Otsu Criteria. INDEX TERMS Color image segmentation, circular histogram thresholding, entropy method, Lorenz-curve type technique.

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When the dynamic characteristics of a bridge structure are analyzed though Hilbert–Huang transform (HHT), the noise contained in the bridge dynamic monitoring data may seriously affect the performance of the first natural frequency identification. A time-frequency analysis method that integrates wavelet threshold denoising and HHT is proposed to overcome this deficiency. The denoising effect of the experimental analysis on the simulated noisy signals proves the effectiveness of the proposed method. This method is used to perform denoising pre-processing on the dynamic monitoring data of Sutong Bridge, and the denoised results of different methods are compared and analyzed. Then, the best denoising data are selected as the input data of Hilbert spectrum analysis to identify the structural first natural frequency of the bridge. The results indicate that the wavelet-empirical mode decomposition (EMD) method effectively reduces the interference of random noise and eliminates useless intrinsic modal function (IMF) components, and the excellent properties of the signal evaluation index after denoising make the method suitable for processing non-stationary signals with noise. When Hilbert spectrum analysis is applied to the denoised data, the first natural frequency of the bridge structure can be identified clearly and is consistent with the theoretical calculation. The proposed method can effectively determine the natural vibration characteristics of the bridge structure.

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