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For the resonant grounding system, the existing single criterion is less applicable, and the data fusion method based on intelligent algorithm will be restricted by the training sample, therefore, a intelligent fault line selection method based on S transform direction and energy is proposed in this paper. Firstly, with the powerful signal extraction ability of the S transform algorithm, the characteristic band signals are extracted from the zero sequence currents of all lines. Then the phase difference and energy parameters of each line are calculated. Finally, the parameters of the energy parameters are processed with the difference of the angle difference as the weight number, which achieves the purpose of merging two criteria, and enlarges the fault boundary, and the line with the most comprehensive parameters is the fault line. The simulation results show that the method has high margin and wide application scope, and is less affected by fault resistance, fault closing angle and fault location.

We propose a novel depth maps refinement algorithm and generate multi-view video sequences from two-view video sequences for modern autostereoscopic display. In order to generate realistic contents for virtual views, high-quality depth maps are very critical to the view synthesis results. We propose an iterative depth refinement approach of a joint error detection and correction algorithm to refine the depth maps that can be estimated by an existing stereo matching method or provided by a depth capturing device. Error detection aims at two types of error: across-view color-depth-inconsistency error and local color-depth-inconsistency error. Subsequently, the detected error pixels are corrected by searching appropriate candidates under several constraints to amend the depth errors. A trilateral filter is included in the refining process that considers intensity, spatial and temporal terms into the filter weighting to enhance the consistency across frames. In the proposed view synthesis framework, it features a disparity-based view interpolation method to alleviate the translucent artifacts and a directional filter to reduce the aliasing around the object boundaries. Experimental results show that the proposed algorithm effectively fixes errors in the depth maps. In addition, we also show the refined depth maps along with the proposed view synthesis framework significantly improve the novel view synthesis on several benchmark dataseis.

Wide-angle lens provides a broad field of view which benefits some applications, such as video surveillance or endoscopic imaging. However, it also induces lens distortion, especially radial distortion, which may impede further video analysis or perceptual interpretation. For some applications, such as minimally invasive surgery and visual surveillance, real-time correction of image distortion is required. Traditional CPU-centric machines are difficult to achieve the requirement of real-time computation because of a large amount of computation. In this paper, we propose to achieve real-time correction of wide-angle lens distortion of images on several target platforms. In the GPGPU platform, we achieve real-time correction at full-HD resolution by using CUDA. For middle-end devices, an error-controllable mesh is used and the system is implemented by industry standard, OpenGL. We also implement it with OpenGL ES on embedded GPUs for mobile devices. Experiments show using our error-controllable mesh greatly outperforms those using regularly downsampled meshes.