Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topographic surface. Firstly, we design a local water-filling approach including a flooding and effusing process to estimate the shading map, which can be used to detect umbra and penumbra. Then, the umbra is enhanced using Retinex Theory. For penumbra, we propose a binarized water-filling strategy to correct illumination distortions. Moreover, we build up a dataset called optical shadow removal (OSR dataset), which includes hundreds of shadow images. Experiments performed on OSR dataset show that our method achieves an average ErrorRatio of 0.685 with a computation time of 0.265 s to process an image size of 960×544 pixels on a desktop. The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.

Evaluation of uncertainties associated with landslide displacement prediction is essential for improving the reliability of landslide early warning systems. An efficient probabilistic forecasting method for the construction of prediction intervals (PIs) using bootstrap and kernel-based extreme learning machine (ELM) is proposed. To overcome the drawbacks of artificial neural networks (ANNs) in predicting mutational displacement points with time lags, this paper proposes an ANNs switched prediction scheme to construct PIs with a three-stage formulation. In the first stage, K-means clustering is applied to divide the whole training dataset into two sub-training sets: the stationary points and the mutational points. In the second stage, a weighted ELM classifier is applied to construct the switched rules. In the third stage, bootstrap-and kernel-based ELMs are applied to construct candidate PIs for each sub-training set. The final PIs are constructed by switching between these two candidate PIs. The effectiveness of the proposed ANNs switched prediction method has been validated through comprehensive tests using three real-world landslide datasets from the Three Gorges region of China.

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