Learning an efficient projection to map high-dimensional data into a lower dimensional space is a rather challenging task in the community of pattern recognition and computer vision. Manifold learning is widely applied because it can disclose the intrinsic geometric structure of data. However, it only concerns the geometric structure and may lose its effectiveness in case of corrupted data. To address this challenge, we propose a novel dimensionality reduction method by combining the manifold learning and low-rank sparse representation, termed low-rank sparse preserving projections (LSPP), which can simultaneously preserve the intrinsic geometric structure and learn a robust representation to reduce the negative effects of corruptions. Therefore, LSPP is advantageous to extract robust features. Because the formulated LSPP problem has no closed-form solution, we use the linearized alternating direction method with adaptive penalty and eigen-decomposition to obtain the optimal projection. The convergence of LSPP is proven, and we also analyze its complexity. To validate the effectiveness and robustness of LSPP in feature extraction and dimensionality reduction, we make a critical comparison between LSPP and a series of related dimensionality reduction methods. The experimental results demonstrate the effectiveness of LSPP.
In the development of modern intelligent Ubiquitous Electric Internet of Things (UE-IoT), infrared thermal imaging always plays an important role in automated early-warning detection of developing failures of critical assets such as transformers, disconnects and capacity banks in electrical power substation non-intrusively. However, the low resolution and contrast of infrared images hinder the subsequent analysis and recognition of fault points. In contrast, visible images present abundant texture details of the equipment without thermal information. In order to assist the detection of fault points, this paper proposes a Generative adversarial networks (GAN) based infrared and visible image fusion method to produce a composite image with enhanced edges and better quality. The edge loss function is added to represent the perceptual edges. In the discriminator, the proposed method improves the texture similarity between fusion image and visible image by minimizing the Wasserstein distance in VGG (Visual Geometry Group network) feature space. The experimental results show that the fault regions become more salient and the details are enhanced. In this way, it can facilitate the detection of fault points both reliably and accurately. INDEX TERMS Image fusion, VGG, generative adversarial networks, ubiquitous electric Internet of Things. I. INTRODUCTION
This paper studies a registration method that is more suitable for using Microsoft HoloLens as a surgical navigation instrument. The most important step in the surgical navigation process is to register the virtual model with the corresponding lesion. Our registration method is mainly as follows: first use the 3D model recognition in vuforia for coarse registration; then extract the point cloud data of model and real scene scanned by HoloLens; finally use the normal vector geometric features to extract the feature points of the point cloud data, and then use the ICP algorithm Perform fine registration. Our registration method can solve the problem that the traditional ICP algorithm is easy to fall into a local optimum, and it can also improve the registration accuracy to a certain extent.
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