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The presence of undesired background areas associated with potential noise and unknown spectral characteristics degrades the performance of hyperspectral data processing. Masking out unwanted regions is key to addressing this issue.…
The Python Sky Model (PySM) is a Python package used by Cosmic Microwave Background (CMB) experiments to simulate maps, in HEALPix pixelization, of the various diffuse astrophysical components of Galactic emission relevant at CMB…
Advancements in optical metrology has enabled documentation of dense 3D point clouds of cultural heritage sites. For large scale and continuous digital documentation, processing of dense 3D point clouds becomes computationally cumbersome,…
In our former works we have made serious efforts to improve the performance of medical image analysis methods with using ensemble-based systems. In this paper, we present a novel hardware-based solution for the efficient adoption of our…
Instance segmentation on point clouds is a fundamental task in 3D scene perception. In this work, we propose a concise clustering-based framework named HAIS, which makes full use of spatial relation of points and point sets. Considering…
The WorldWide Telescope(WWT) is a scientific visualization platform which can browse deep space images, star catalogs, and planetary remote sensing data from different observation facilities in a three-dimensional virtual scene. First…
This document describes the Multi-Order Coverage map method (MOC) to specify arbitrary sky regions. The goal is to be able to provide a very fast comparison mechanism between coverage maps. The mechanism is based on the HEALPix sky…
Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer…
Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such…
In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on…
Previous harmonization methods focus on adjusting one inharmonious region in an image based on an input mask. They may face problems when dealing with different perturbations on different semantic regions without available input masks. To…
This paper describes how we have sustained the Montage image mosaic engine (http://montage.ipac.caltech.edu) first released in 2002, to support the ever-growing scale and complexity of modern data sets. The key to its longevity has been its…
Domain-specific languages that execute image processing pipelineson GPUs, such as Halide and Forma, operate by 1) dividing the image into overlapped tiles, and 2) fusing loops to improve memory locality. However, current approaches have…
The importance of hierarchical image organization has been witnessed by a wide spectrum of applications in computer vision and graphics. Different from image segmentation with the spatial whole-part consideration, this work designs a modern…
We developed a Python based framework for astronomical image processing and analysis. Astronomical image loading, normalizing, stacking, and filtering processes represent visible range images from grayscale. Besides, the blending process…
The conventional high-level sensing techniques require high-fidelity images as input to extract target features, which are produced by either complex imaging hardware or high-complexity reconstruction algorithms. In this letter, we propose…
Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multiple instance learning-based methods or transformer models is…
Efficiently analyzing maps from upcoming large-scale surveys requires gaining direct access to a high-dimensional likelihood and generating large-scale fields with high fidelity, which both represent major challenges. Using CAMELS…
Real-time processing of UAV imagery is crucial for applications requiring urgent geospatial information, such as disaster response, where rapid decision-making and accurate spatial data are essential. However, processing high-resolution…
Reliable and exact assessment of visibility is essential for safe air traffic. In order to overcome the drawbacks of the currently subjective reports from human observers, we present an approach to automatically derive visibility measures…