Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified manner. Further, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG.

Accelerometer signals play a critical role in many fields, for example navigation and vehicle safety. However, uncontrollable factors such as defective equipment and harsh environments make the signals recorded by sensors contain a large amount of noise, which poses a great challenge. Most existing methods are based on traditional signal processing and often face problems of incomplete noise reduction or signal distortion after denoising. In this paper, we propose a data-driven denoising method for accelerometer signals (ADNet) based on the Wave_U_Net network architecture, incorporating Multi-Head Attention mechanism and Spatial Attention mechanism. This enhances the network’s feature extraction capability and accelerates its convergence speed. Meanwhile, the attention mechanism focuses the model’s attention on the clean signal’s feature information, improving the network’s fitting capability to the signal distribution. Therefore, ADNet can achieve more thorough denoising while reducing signal distortion after denoising. Finally, we conduct experiments on Walking speed dataset and field experiment dataset to verify the effectiveness of ADNet. The experimental results show that ADNet outperforms other baseline models in terms of Mean Square Error, Mean Absolute Error, Root Mean Square Error, and Signal-to-Noise Ratio.

Abstract This study proposes a multi‐scale transformation method based on morphological reconstruction and membership filtering, termed as MMFuse, to fuse infrared and visible images. This method employs a fuzzy c‐means clustering algorithm for multi‐scale decomposition by introducing morphological reconstruction operations and modifying member partitions to ensure noise resistance and image detail preservation. In addition, the MMFuse utilises the image attributes of layers as their fusion weights at each scale for adaptive feature fusion, which reduces the difficulty of manual adjustment of fusion weights. Moreover, on the basis of histogram enhancement, a visible image enhancement method is proposed, which can help exploit additional texture details in low‐light visible images and transfer these details to the fused image. The experiments performed on public datasets indicates that the MMFuse can generate sharp and clean fused images with high robustness and good fusion results for the images corrupted by different noises. Moreover, the results of this method appear as high‐quality visible images with clear highlighted infrared targets.

Numerous sampling strategies have been proposed to reduce the visual clutter of large-scale geographical point data visualization, which focus on the preservation of original data features, such as randomness, spatial distribution, and associated relationship. However, user preferences and demands are not taken into account in the course of sampling, which will lead to the sampled results deviating from user requirements and impede personalized geospatial analysis in specific application scenarios. In this article, we propose a user-driven sampling model for the visual abstraction of the large-scale geographical point data based on convolutional neural networks (CNN). First, a blue noise sampling model is applied to partition the geographical space into local areas, and a set of visual interfaces are designed to present the data features of those points in the local areas, enabling users to visually select representative points according to their requirements. Then, user preferences are quantified with a CNN model based on the eigenvectors of the representative points, which are further utilized to guide the sampling courses of the other local areas. Thus, all the sampled points will retain the spatial distribution of original data points and fulfill the user preference as far as possible. In addition, we implement a visualization framework to integrate manual point selection, CNN training, automatic point sampling, and visual comparison, allowing users to easily obtain and evaluate the sampled points from the perspectives of data analysis and user requirements. Quantitative comparisons and case studies based on real-world datasets are conducted to demonstrate the effectiveness of our sampling model in the preservation of user preferences and visual exploration of large-scale geospatial point data.