APEX-Net:自动地块提取网络

Aalok Gangopadhyay, Prajwal Singh, S. Raman
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引用次数: 1

摘要

自动图提取包括理解和推断数据分布,从而从包含多个二维线图的图像中提取单个线图。这是一个有许多实际应用程序的重要问题。解决这个问题的现有方法涉及大量的人为干预。为了最大限度地减少这种干预,我们提出了APEX-Net,这是一个基于深度学习的框架,具有新颖的损失函数来解决图提取问题。此外,我们还介绍了APEX-1M——一个新的大型数据集,它同时包含了绘图图像和原始数据。我们在APEX-1M测试集上演示了APEX-Net的性能,并表明它获得了令人印象深刻的准确性。我们还展示了我们的网络在未见过的地块图像上的视觉结果,并证明它在很大程度上提取了地块的形状。最后,我们开发了一个图形用户界面用于绘图提取,可以使整个社区受益。数据集和代码将向公众开放。
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APEX-Net: Automatic Plot Extraction Network
Automatic plot extraction involves understanding and inferring the data distribution and therefore, extracting individual line plots from an image containing multiple 2D line plots. It is an important problem having many real-world applications. The existing methods for addressing this problem involve a significant amount of human intervention. To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. Further, we introduce APEX-1M - a new large scale dataset that contains both the plot images and the raw data. We demonstrate the performance of APEX-Net on the APEX-1M test set and show that it obtains impressive accuracy. We also show visual results of our network on unseen plot images and demonstrate that it extracts the shape of the plots to a great extent. Finally, we develop a GUI for plot extraction that can benefit the community at large. The dataset and code will be made publicly available.
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