相关论文: MegaPipe: the MegaCam image stacking pipeline

Ecological sciences are using imagery from a variety of sources to monitor and survey populations and ecosystems. Very High Resolution (VHR) satellite imagery provide an effective dataset for large scale surveys. Convolutional Neural…

Superpixel decomposition methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while trying to respect existing contours. For all state-of-the-art…

We describe a case study to use the Montage image mosaic engine to create maps of the ALLWISE image data set in the Hierarchical Progressive Survey (HiPS) sky-tesselation scheme. Our approach demonstrates that Montage reveals the science…

We propose a principled convolutional neural pyramid (CNP) framework for general low-level vision and image processing tasks. It is based on the essential finding that many applications require large receptive fields for structure…

Recent image restoration methods have produced significant advancements using deep learning. However, existing methods tend to treat the whole image as a single entity, failing to account for the distinct objects in the image that exhibit…

The Data Handling Pipeline ("Pipeline") has been developed for the Fermi Gamma-Ray Space Telescope (Fermi) Large Area Telescope (LAT) which launched in June 2008. Since then it has been in use to completely automate the production of data…

Graphics rendering that builds on machine learning and radiance fields is gaining significant attention due to its outstanding quality and speed in generating photorealistic images from novel viewpoints. However, prior work has primarily…

This paper describes a new Heterodyne Array Receiver Programme (HARP) and Auto-Correlation Spectral Imaging System (ACSIS) that have recently been installed and commissioned on the James Clerk Maxwell Telescope (JCMT). The 16-element…

Joint camera pose and dense geometry estimation from a set of images or a monocular video remains a challenging problem due to its computational complexity and inherent visual ambiguities. Most dense incremental reconstruction systems…

We present a new automatic tool for time-domain astronomy - the Cambridge Photometric Calibration Server 2.0 - developed under OPTICON H2020 programme. It has been designed to respond to the need of automated rapid photometric data…

Magnetic Particle Imaging (MPI) is a highly sensitive imaging method that enables the visualization of magnetic tracer materials with a temporal resolution of more than 46 volumes per second. In MPI the size of the field of view scales with…

Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as…

Accurate digital twins of industrial assets, such as ships and offshore platforms, rely on the precise reconstruction of complex pipe networks. However, manual modelling of pipes from laser scan data is a time-consuming and labor-intensive…

To cope with the high requirements during the computation of semantic segmentations of earth observation imagery, current state-of-the-art pipelines divide the corresponding data into smaller images. Existing methods and benchmark datasets…

Cross spectral camera arrays, where each camera records different spectral content, are becoming increasingly popular for RGB, multispectral and hyperspectral imaging, since they are capable of a high resolution in every dimension using…

We present a versatile computational image rendering software of optically-acquired holograms. The reported software can process 4 Megapixel 8-bit raw frames from a sensor array acquired at a sustained rate of 80 Hz. Video-rate image…

Rigid registration of multi-view and multi-platform LiDAR scans is a fundamental problem in 3D mapping, robotic navigation, and large-scale urban modeling applications. Data acquisition with LiDAR sensors involves scanning multiple areas…

Mapping workflow applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline graphs. Several antagonistic criteria should be optimized, such as throughput and latency (or a…

Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision (CV) and natural language processing (NLP). However, these large-scale models are too compute- or memory-intensive for…

The spherical harmonic transform is a powerful tool in the analysis of spherical data sets, such as the cosmic microwave background data. In this work, we present a new scheme for the spherical harmonic transforms that supports both CPU and…