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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Using AI Planning Techniques to Automatically Generate Image Processing Procedures: A Preliminary Report

This paper describes work on the Multimission VICAR Planner (MVP) system to automatically construct executable image processing procedures for custom image processing requests for the JPL Multimission Image Processing Lab (MIPL). This paper focuses on two issues. First, large search spaces caused by complex plans required the use of hand encoded control information. In order to address this in a manner similar to that used by human experts, MVP uses a decomposition-based planner to implement hierarchical/skeletal planning at the higher level and then uses a classical operator based planner to solve subproblems in contexts defined by the high-level decomposition.

Laboratory MIPL VICAR artificial intelligence AI↗
NASA NTRS2009

Processing of 3-Dimensional Flash Lidar Terrain Images Generated From an Airborne Platform

Data from the first Flight Test of the NASA Langley Flash Lidar system have been processed. Results of the analyses are presented and discussed. A digital elevation map of the test site is derived from the data, and is compared with the actual topography. The set of algorithms employed, starting from the initial data sorting, and continuing through to the final digital map classification is described. The accuracy, precision, and the spatial and angular resolution of the method are discussed.

Bulyshev, Alexander↗
NASA NTRS2017

Reconfigurable Image Generator

A RiG may simulate visual conditions of a real world environment, and generate the necessary amount of pixels in a visual simulation at rates up to 120 frames per second. RiG may also include a database generation system capable of producing visual databases suitable to drive the visual fidelity required by the RiG.

Archdeacon, John L.↗
NASA NTRS2005

Experience With Bayesian Image Based Surface Modeling

Bayesian surface modeling from images requires modeling both the surface and the image generation process, in order to optimize the models by comparing actual and generated images. Thus it differs greatly, both conceptually and in computational difficulty, from conventional stereo surface recovery techniques. But it offers the possibility of using any number of images, taken under quite different conditions, and by different instruments that provide independent and often complementary information, to generate a single surface model that fuses all available information. I describe an implemented system, with a brief introduction to the underlying mathematical models and the compromises made for computational efficiency. I describe successes and failures achieved on actual imagery, where we went wrong and what we did right, and how our approach could be improved. Lastly I discuss how the same approach can be extended to distinct types of instruments, to achieve true sensor fusion.

Stutz, John C.↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models

Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.

58 GEOSCIENCES↗
DOE OSTI2023

Deep learning methods for event verification and image re-purposing detection

Systems and methods herein describe accessing an image, generating a resized image, generating an image feature vector by applying an image classification neural network to the resized image, generating analysis of the image by processing the image feature vector using a machine-learning classifier trained to analyze the image feature vector, and based on the analysis, determining an event that is attributed to the image.

Nataraj, Lakshmanan↗
DOE OSTI2023

Homography generation for image registration in inlier-poor domains

A method for efficient image registration between two images in the presence of inlier-poor domains includes receiving a set of candidate correspondences between the two images. An approximate homography between the two images is generated based upon a first correspondence in the correspondences. The set of candidate correspondences is filtered to identify inlier correspondences based upon the approximate homography. A candidate homography is computed based upon the inlier correspondences. The candidate homography can be selected as a final homography between the two images based upon a support of the candidate homography against the set of candidate correspondences. An image registration is performed between the two images based upon the candidate homography being selected as the final homography.

Gonzales, Antonio↗
DOE OSTI2022

Apparatus and method for high dynamic range counting by pixelated detectors

The present disclosure relates to an apparatus and methods for generating a hybrid image by high-dynamic-range counting. In an embodiment, the apparatus includes a processing circuitry configured to acquire an image from a pixelated detector, obtain a sparsity map of the acquired image, the sparsity map indicating low-flux regions of the acquired image and high-flux regions of the acquired image, generate a low-flux image and a high-flux image based on the sparsity map, perform event analysis of the acquired image based on the low-flux image and the high-flux image, the event analysis including detecting, within the low-flux image, incident events by an event counting mode, multiply, by a normalization constant, resulting intensities of the high-flux image and the detected incident events of the low-flux image, and generate the hybrid image by merging the low-flux image and the high-flux image.

Bammes, Benjamin↗
NASA NTRS1989

Design considerations for a space database

Part of the information used in a real-time simulator is stored in the visual database. This information is processed by an image generator and displayed as a real-time visual image. The database must be constructed in a specific format, and it should efficiently utilize the capacities of the image generator that is was created for. A visual simulation is crucially dependent upon the success with which the database provides visual cues and recognizable scenes. For this reason, more and more attention is being paid to the art and science of creating effective real-time visual databases. Investigated here are the database design considerations required for a space-oriented real-time simulator. Space applications often require unique designs that correspond closely to the particular image-generator hardware and visual-database-management software. Specific examples from the databases constructed for NASA and its Evans and Sutherland CT6 image generator illustrate the various design strategies used in a space-simulation environment. These database design considerations are essential for all who would create a space database.

Moss, Lance M.↗