A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images

Data Extraction from Charts via Single Deep Neural Network

Automatic data extraction from charts is challenging for two reasons: there exist many relations among objects in a chart, which is not a common consideration in general computer vision problems; and different types of charts may not be processed by the same model. To address these problems, we propose a framework of a single deep neural network, which consists of object detection, text recognition and object matching modules. The framework handles both bar and pie charts, and it may also be extended to other types of charts by slight revisions and by augmenting the training data. Our model pe…

超仿射图像的特征点提取

Feature Point Extraction for Extra-Affine Image

针对大角度仿射变换下特征提取稳定性下降且ASIFT耗时高内存大的问题，提出结合Lanczos插值与ORB的改进方法，在保持仿射不变性下实现已知最快的超仿射图像特征提取，且稳定性逼近90度极限。

Robust Detection of Non-overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds

This manuscript provides a collection of new methods for the automated detection of non-overlapping ellipses from edge points. The methods introduce new developments in: (i) robust Monte Carlo-based ellipse fitting to 2-dimensional (2D) points in the presence of outliers; (ii) detection of non-overlapping ellipse from 2D edge points; and (iii) extraction of cylinder from 3D point clouds. The proposed methods were thoroughly compared with established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. It was found that the …

基于物理的人工智能方法用于从光学数据中提取材料参数

Physics-based AI methodology for Material Parameter Extraction from Optical Data

本文提出一种融合物理知识与多尺度目标检测的神经网络方法，用于从太赫兹和红外光谱数据中自主、鲁棒且高效地提取材料参数，并验证了其优于传统方法的性能。

Towards Large-Scale Rendering of Simulated Crops for Synthetic Ground Truth Generation on Modular Supercomputers

Computer Vision problems deal with the semantic extraction of information from camera images. Especially for field crop images, the underlying problems are hard to label and even harder to learn, and the availability of high-quality training data is low. Deep neural networks do a good job of extracting the necessary models from training examples. However, they rely on an abundance of training data that is not feasible to generate or label by expert annotation. To address this challenge, we make use of the Unreal Engine to render large and complex virtual scenes. We rely on the performance of i…

Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data

In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN. The U-Net was trained to accomplish the task in a fully supervised setting. To create the training image pairs, we employed simulated X-Ray or Digitally Reconstructed Radiographs (DRR), derived from 664 CT scans belonging to the LIDC-IDRI dataset. Using HU based segmentation of bone structures in the CT domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the network. For the reconstruction loss, we utilize two loss functions…