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DOE OSTI2022

Statistical characterization of experimental magnetized liner inertial fusion stagnation images using deep-learning-based fuel–background segmentation

Significant variety is observed in spherical crystal x-ray imager (SCXI) data for the stagnated fuel–liner system created in Magnetized Liner Inertial Fusion (MagLIF) experiments conducted at the Sandia National Laboratories Z-facility. As a result, image analysis tasks involving, e.g., region-of-interest selection (i.e. segmentation), background subtraction and image registration have generally required tedious manual treatment leading to increased risk of irreproducibility, lack of uncertainty quantification and smaller-scale studies using only a fraction of available data. We present a convolutional neural network (CNN)-based pipeline to automate much of the image processing workflow. This tool enabled batch preprocessing of an ensemble of N scans = 139 SCXI images across N exp = 67 different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. Here we also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Constructing Self-Labeled Materials Imaging Datasets from Open Access Scientific Journals with EXSCLAIM!

Due to recent improvements in image resolution and acquisition speeds, materials microscopy is experiencing an explosion in imaging data. Yet, despite the volume of images generated, the overall accessibility landscape is highly fragmented, as researchers who do release images to the public, often only do so as snapshots of their larger private dataset in context of scientific journal publications. The effort to automatically consolidate images and descriptive information from web-based platforms has garnered broad attention from the computer vision, language technologies, and chemistry/materials informatics communities. However, these methods are problematic for scientific figures because over 30% of figures are compound in nature, and it is the individual images themselves, paired with relevant context, that are necessary for construction a proper labeled dataset. To this end, we outline in this paper the design of a software pipeline for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific figures (EXSCLAIM!). Successful consolidation of materials imaging across literature sources will enhance navigation and searchability of materials microscopy images for both novice and experienced researchers, as well as establish the framework necessary for users to search by images, text, or some combination of both.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Implementing an Accurate and Rapid Sparse Sampling Approach for Low-Dose Atomic Resolution STEM Imaging

Aberration correction for scanning transmission electron microscopes (STEM) has dramatically increased spatial image resolution for beam-stable materials, but it is the sample stability rather than the microscope that often limits the practical resolution of STEM images. To extract physical information from images of beam sensitive materials it is becoming clear that there is a critical dose/dose-rate below which the images can be interpreted as representative of the pristine material, while above it the observation is dominated by beam effects. Here we describe an experimental approach for sparse sampling in the STEM and in-painting image reconstruction in order to reduce the electron dose/dose-rate to the sample during imaging. By characterizing the induction limited rise-time and hysteresis in scan coils, we show that sparse line-hopping approach to scan randomization can be implemented that optimizes both the speed of the scan and the amount of the sample that needs to be illuminated by the beam. The dose and acquisition time for the sparse sampling is shown to be effectively decreased by factor of 5x relative to conventional acquisition, permitting imaging of beam sensitive materials to be obtained without changing the microscope operating parameters. As a result, the use of sparse line-hopping scan to acquire STEM images is demonstrated with atomic resolution aberration corrected Z-contrast images of CaCO 3 , a material that is traditionally difficult to image by TEM/STEM because of dose issues.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Label-free quantum super-resolution imaging using entangled multi-mode squeezed light

Abstract In this study, we explore the theoretical application of entangled multi-mode squeezed light for label-free optical super-resolution imaging. By generating massively entangled multi-mode squeezed light through an array of balanced beam splitters, using a single-mode squeezed light input, we create a multi-mode quantum light state with exceptional entanglement and noise suppression below the shot noise level. This significantly reduces imaging measurement errors compared to classical coherent state light imaging when the same number of photons are used on the imaging sample. We demonstrate how to optimize the imaging system’s parameters to achieve the Heisenberg imaging error limit, taking into account the number of entangled modes and photons used. We also examine the effects of optical losses in the imaging system, necessitating adjustments to the optimized parameters based on the degree of optical loss. In practical applications, this new quantum imaging approach reduces the number of photons needed to achieve the same image quality by two orders of magnitude compared to classical imaging methods that use non-entangled, non-squeezed coherent state light.

Soh, Daniel (ORCID:0000000325625675)↗

DOE OSTI2025

Image registration for accurate electrode deformation analysis in operando microscopy of battery materials

Operando imaging techniques have become increasingly valuable in both battery research and manufacturing. However, the reliability of these methods can be compromised by instabilities in the imaging setup and operando cells, particularly when utilizing high-resolution imaging systems. The acquired imaging data often include features arising from both undesirable system vibrations and drift, as well as the scientifically relevant deformations occurring in the battery sample during cell operation. For meaningful analysis, it is crucial to distinguish and separately evaluate these two factors. To address these challenges, we employ a suite of advanced image-processing techniques. These include fast Fourier transform analysis in the frequency domain, power spectrum-based assessments for image quality, as well as rigid and non-rigid image-registration methods. These techniques allow us to identify and exclude blurred images, correct for displacements caused by motor vibrations and sample holder drift and, thus, prevent unwanted image artifacts from affecting subsequent analyses and interpretations. Additionally, we apply optical flow analysis to track the dynamic deformation of battery electrode materials during electrochemical cycling. This enables us to observe and quantify the evolving mechanical responses of the electrodes, offering deeper insights into battery degradation. Together, these methods ensure more accurate image analysis and enhance our understanding of the chemomechanical interplay in battery performance and longevity.

Sun, Tianxiao↗

DOE OSTI2017

Automating High-Precision X-Ray and Neutron Imaging Applications with Robotics

Los Alamos National Laboratory and the University of Texas at Austin recently implemented a robotically controlled nondestructive testing (NDT) system for X-ray and neutron imaging. This system is intended to address the need for accurate measurements for a variety of parts and, be able to track measurement geometry at every imaging location, and is designed for high-throughput applications. This system was deployed in a beam port at a nuclear research reactor and in an operational inspection X-ray bay. The nuclear research reactor system consisted of a precision industrial seven-axis robot, 1.1-MW TRIGA research reactor, and a scintillator-mirror-camera-based imaging system. The X-ray bay system incorporated the same robot, a 225-keV microfocus X-ray source, and a custom flat panel digital detector. The robotic positioning arm is programmable and allows imaging in multiple configurations, including planar, cylindrical, as well as other user defined geometries that provide enhanced engineering evaluation capability. The imaging acquisition device is coupled with the robot for automated image acquisition. The robot can achieve target positional repeatability within 17 μm in the 3-D space. Flexible automation with nondestructive imaging saves costs, reduces dosage, adds imaging techniques, and achieves better quality results in less time. Specifics regarding the robotic system and imaging acquisition and evaluation processes are presented. In conclusion, this paper reviews the comprehensive testing and system evaluation to affirm the feasibility of robotic NDT, presents the system configuration, and reviews results for both X-ray and neutron radiography imaging applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

WE-E-18A-02: Enhancement of Lung Tumor Visibility by Dual-Energy X-Ray Imaging in An Anthropomorphic Chest Phantom Study

Purpose: Intrafractional lung tumor motion during radiotherapy can be compensated for by tracking the tumor position using x-ray imaging and adapting the treatment in real-time. However, locating the tumor with an automated template-matching algorithm is often challenging if the tumor is obscured by ribs. This study investigates the feasibility of creating dual-energy (DE) images of the chest with increased tumor visibility on an Elekta XVI system. Methods: An anthropomorphic chest phantom was imaged at two different energies. Low-energy images were obtained at 80 kVp (0.8 mAs); high-energy images at 129 kVp (0.6 mAs, additional 1.26 mm tin filter). A Geant4 Monte-Carlo framework was developed allowing simulation of the x-ray tube, flat-panel detector and phantom in order to optimize the beam energies, filtration and the weighting factor used to subtract the individual images into a synthetic DE image. The weighting factor was selected to minimize the visibility of bones while maintaining a sufficient tumor visibility. We scored the bone visibility as the contrast of tumor (with bone) to tumor (without bone), and similarly of lung tissue (with bone) to lung tissue (without bone). Tumor visibility was quantified as the contrast between tumor and lung tissue (both without bone). Results: In the experimentally obtained DE image the bone visibility was reduced by 79.2% in tumor and by 96.8% in lung tissue while the overall tumor visibility only decreased by 69.5%. The Monte-Carlo simulation yielded similar results reducing the scores by 90.0%, 85.3% and only 71.9%, respectively. Conclusion: This work demonstrates the feasibility of DE imaging to enhance lung tumor detectability. In the future, we hope to further refine the Monte-Carlo simulation to more accurately predict the weighting factors which would aid real-time implementation. Furthermore, we plan to use the Monte-Carlo framework to simulate DE images of actual lung tumors. The authors would like to thank Paul-Scherrer-Institut and Centre Suisse d'Electronique et Microtechnique for lending us the anthropomorphic phantom. We acknowledge support from Elekta AB under a research agreement. Research at The Institute of Cancer Research is also supported by Cancer Research UK under Programme C46/A10588.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

The ANACONDA algorithm for deformable image registration in radiotherapy

Purpose: The purpose of this work was to describe a versatile algorithm for deformable image registration with applications in radiotherapy and to validate it on thoracic 4DCT data as well as CT/cone beam CT (CBCT) data. Methods: ANAtomically CONstrained Deformation Algorithm (ANACONDA) combines image information (i.e., intensities) with anatomical information as provided by contoured image sets. The registration problem is formulated as a nonlinear optimization problem and solved with an in-house developed solver, tailored to this problem. The objective function, which is minimized during optimization, is a linear combination of four nonlinear terms: 1. image similarity term; 2. grid regularization term, which aims at keeping the deformed image grid smooth and invertible; 3. a shape based regularization term which works to keep the deformation anatomically reasonable when regions of interest are present in the reference image; and 4. a penalty term which is added to the optimization problem when controlling structures are used, aimed at deforming the selected structure in the reference image to the corresponding structure in the target image. Results: To validate ANACONDA, the authors have used 16 publically available thoracic 4DCT data sets for which target registration errors from several algorithms have been reported in the literature. On average for the 16 data sets, the target registration error is 1.17 ± 0.87 mm, Dice similarity coefficient is 0.98 for the two lungs, and image similarity, measured by the correlation coefficient, is 0.95. The authors have also validated ANACONDA using two pelvic cases and one head and neck case with planning CT and daily acquired CBCT. Each image has been contoured by a physician (radiation oncologist) or experienced radiation therapist. The results are an improvement with respect to rigid registration. However, for the head and neck case, the sample set is too small to show statistical significance. Conclusions: ANACONDA performs well in comparison with other algorithms. By including CT/CBCT data in the validation, the various aspects of the algorithm such as its ability to handle different modalities, large deformations, and air pockets are shown.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Methods—Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition

The mesostructure of porous electrodes used in lithium-ion batteries strongly influences cell performance. Accurate imaging of the distribution of phases in these electrodes would allow this relationship to be better understood through simulation. However, imaging the nanoscale features in these components is challenging. While scanning electron microscopy is able to achieve the required resolution, it has well established difficulties imaging porous media. This is because the flat imaging planes prepared using focused ion beam milling will intersect with the pores, which makes the images hard to interpret as the inside walls of the pores are observed. It is common to infiltrate porous media with resin prior to imaging to help resolve this issue, but both the nanoscale porosity and the chemical similarity of the resins to the battery materials undermine the utility of this approach for most electrodes. In this study, a technique is demonstrated which uses in situ infiltration of platinum to fill the pores and thus enhance their contrast during imaging. Reminiscent of the Japanese art of repairing cracked ceramics with precious metals, this technique is referred to as the kintsugi method. The images resulting from applying this technique to a conventional porous cathode are presented and then segmented using a multi-channel convolutional method. We show that while some cracks in active material particles were empty, others appear to be filled (perhaps with the carbon binder phase), which will have implications for the rate performance of the cell. Energy dispersive X-ray spectroscopy was used to validate the distribution of phases resulting from image analysis, which also suggested a graded distribution of the binder relative to the carbon additive. The equipment required to use the kintsugi method is commonly available in major research facilities and so we hope that this method will be rapidly adopted to improve the imaging of electrode materials and porous media in general.

25 ENERGY STORAGE↗

DOE OSTI2021

Imaging Electrons in Two Dimensional Materials (Final report)

Two-dimensional (2D) materials have extraordinary characteristics that offer promising new approaches for science and technology. Electrons in graphene can move ballistically through a sheet, even though it is only a single atom thick. And transition metal dichalcogenide materials can be cleaved into 2D flakes of all types – metals, semiconductors, insulators, magnetic materials, and superconductors. To benefit from these discoveries, the science needs to be understood to transform 2D materials into useful new devices and systems. The evolution in time of atomic scale graphene structures has been studied in time with a transmission electron microscope (TEM), and ballistic transport of electrons in graphene was been imaged using a cooled scanning probe microscope (SPM), as well as electron motion in MoS 2 . Graphene is only one atom thick but is extremely strong, creating the opportunity to fabricate atomic scale structures in the lateral direction. Using an atomic resolution TEM, a suspended sheet of graphene can be shaped by a Si impurity atom on its surface that acts like a chisel to open up apertures, one atom at a time. The electrons in the TEM provide both the chiseling energy and the ability to image the results. Electrons and holes in graphene form a new type of electronic system with conical conduction and valence bands that meet at the Dirac point, with no energy gap. For moderate densities, the carriers form a Fermi liquid with ballistic transport over micron-scale distances. Using a cooled SPM the cyclotron orbit of electrons in graphene has been imaged. In the magnetic focusing regime, electrons leaving a point contact circle around and leave from a second point contact when the cyclotron diameter is equal to the contact spacing. The paths of electrons are focused - they enter at different angles but converge again at the exiting contact. When the SPM tip knocks an electron out of its orbit, an imaging signal is created. The ballistic motion of carriers in graphene opens the way for ballistic devices that manipulate beams of electrons and holes. Our collaborator Gil-Ho Lee, in Philip Kim's group, created a collimating contact by placing zig-zag absorbers on either side of entering electrons. Our cooled SPM was used to image the electron beam and determine its 9-degree halfwidth. By changing the gate voltage, a collimated beam of holes was also created, opening the way for colliding beam experiments. Coherent beams of carriers are desirable for quantum information processing. Via Andreev reflection, superconducting contacts can covert Cooper pairs in a superconductor to an ingoing and outgoing electrons and holes. Using our cooled SPM, Andreev reflection was imaged from a superconducting contact on a graphene device. Magnetic focusing was used to create an incoming beam of electrons and an outgoing beam of holes, detected by a third point contact. The images show a clear transition from normal reflection above the superconducting transition temperature to Andreev reflection as the device is cooled. Transition metal dichalcogenides offer a wide array of materials that can be exfoliated into ultrathin 2D sheets. A cooled SPM can be used to detect quantum dots as well as to image electron flow. The tip charge capacitively couples to electrons on the dot, acting as a gate to tune the dot conductance. Coulomb blockade peaks appear as a bullseye pattern in an SPM image as the tip is raster scanned above the dot. This approach was used to detect quantum dots in a MoS 2 channel as the carrier density was reduced and electrons pooled in low energy points. Through our DOE supported research, cooled SPM imaging has proven to be a very useful tool to uncover the motion of electrons and holes in the new quantum materials graphene and MoS 2 .

36 MATERIALS SCIENCE↗

DOE OSTI2016

“Refsdal” meets Popper: Comparing predictions of the re-appearance of the multiply imaged supernova behind MACSJ1149.5+2223

Supernova “Refsdal,” multiply imaged by cluster MACS1149.5+2223, represents a rare opportunity to make a true blind test of model predictions in extragalactic astronomy, on a timescale that is short compared to a human lifetime. In order to take advantage of this event, we produced seven gravitational lens models with five independent methods, based on Hubble Space Telescope (HST) Hubble Frontier Field images, along with extensive spectroscopic follow-up observations by HST, the Very Large and the Keck Telescopes. We compare the model predictions and show that they agree reasonably well with the measured time delays and magnification ratios between the known images, even though these quantities were not used as input. This agreement is encouraging, considering that the models only provide statistical uncertainties, and do not include additional sources of uncertainties such as structure along the line of sight, cosmology, and the mass sheet degeneracy. We then present the model predictions for the other appearances of supernova “Refsdal.” A future image will reach its peak in the first half of 2016, while another image appeared between 1994 and 2004. The past image would have been too faint to be detected in existing archival images. The future image should be approximately one-third as bright as the brightest known image (i.e., H{sub AB}≈25.7 mag at peak and H{sub AB}≈26.7 mag six months before peak), and thus detectable in single-orbit HST images. We will find out soon whether our predictions are correct.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1986

Automated matching of pairs of SIR-B images for elevation mapping

During the SIR-B mission in October 1984, a significant number of overlapping synthetic aperture radar (SAR) images of various ground areas was collected. This has offered the first opportunity to perform stereo analyses on images from space that cover large ground areas to determine elevation information. This paper presents the preliminary results of an investigation to obtain elevation data from stereo pairs of SIR-B images. First, the accuracy with which elevation information can be derived from SIR-B image pairs is evaluated theoretically. It is shown that elevation accuracy is a function of the slant range resolution, the incidence angles with which the stereo pair is obtained, the accuracies in spacecraft state estimation, and determination of corresponding pixels in the stereo pair. Next, a hierarchical method is developed to match the corresponding pixels. This method involves iterative removal of local distortions and correlations of pairs of local neighborhoods in the two images. Since it is necessary to perform the matching at every pixel in the image, it is very computationally intensive. Therefore, it has been implemented on the Massively Parallel Processor (MPP) at the Goddard Space Flight Center (GSFC). The MPP's speed permits two iterations of this technique to operate on a pair of 512 x 512 images within 7 s. Results of applying this algorithm of SIR-B images of Mount Shasta, CA, are shown. The matching algorithm performs well in regions of the image with significant features. An approximate elevation image derived from the matching process corresponds to published topographic map data, except for certain obvious discontinuities.

Ramapriyan, H. K.↗

NASA NTRS1987

Calibrating AIS images using the surface as a reference

A method of evaluating the initial assumptions and uncertainties of the physical connection between Airborne Imaging Spectrometer (AIS) image data and laboratory/field spectrometer data was tested. The Tuscon AIS-2 image connects to lab reference spectra by an alignment to the image spectral endmembers through a system gain and offset for each band. Images were calibrated to reflectance so as to transform the image into a measure that is independent of the solar radiant flux. This transformation also makes the image spectra directly comparable to data from lab and field spectrometers. A method was tested for calibrating AIS images using the surface as a reference. The surface heterogeneity is defined by lab/field spectral measurements. It was found that the Tuscon AIS-2 image is consistent with each of the initial hypotheses: (1) that the AIS-2 instrument calibration is nearly linear; (2) the spectral variance is caused by sub-pixel mixtures of spectrally distinct materials and shade, and (3) that sub-pixel mixtures can be treated as linear mixtures of pure endmembers. It was also found that the image can be characterized by relatively few endmembers using the AIS-2 spectra.

Smith, M. O.↗

NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗

NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗

NASA NTRS1990

Kalman filter based range estimation for autonomous navigation using imaging sensors

Rotorcraft operating in high-threat environments fly close to the surface of the earth to utilize surrounding terrain, vegetation, or man-made objects to minimize the risk of being detected by the enemy. Two basic requirements for obstacle avoidance are detection and range estimation of the object from the current rotorcraft position. There are many approaches to the estimation of range using a sequence of images. The approach used in this analysis differes from previous methods in two significant ways: an attempt is not made to estimate the rotorcraft's motion from the images; and the interest lies in recursive algorithms. The rotorcraft parameters are assumed to be computed using an onboard inertial navigation system. Given a sequence of images, using image-object differential equations, a Kalman filter (Sridhar and Phatak, 1988) can be used to estimate both the relative coordinates and the earth coordinates of the objects on the ground. The Kalman filter can also be used in a predictive mode to track features in the images, leading to a significant reduction of search effort in the feature extraction step of the algorithm. The purpose is to summarize early results obtained in extending the Kalman filter for use with actual image sequences. The experience gained from the application of this algorithm to real images is very valuable and is a necessary step before proceeding to the estimation of range during low-altitude curvilinear flight. A simple recursive method is presented to estimate range to objects using a sequence of images. The method produces good range estimates using real images in a laboratory set up and needs to be evaluated further using several different image sequences to test its robustness. The feature generation part of the algorithm requires further refinement on the strategies to limit the number of features (Sridhar and Phatak, 1989). The extension of the work reported here to curvilinear flight may require the use of the extended Kalman filter.

Sridhar, Banavar↗

NASA NTRS1990

Infrared images of merging galaxies

Infrared imaging of interacting galaxies is especially interesting because their optical appearance is often so chaotic due to extinction by dust and emission from star formation regions, that it is impossible to locate the nuclei or determine the true stellar distribution. However, at near-infrared wavelengths extinction is considerably reduced, and most of the flux from galaxies originates from red giant stars that comprise the dominant stellar component by mass. Thus near infrared images offer the opportunity to study directly components of galactic structure which are otherwise inaccessible. Such images may ultimately provide the framework in which to understand the activity taking place in many of the mergers with high Infrared Astronomy Satellite (IRAS) luminosities. Infrared images have been useful in identifying double structures in the nuclei of interacting galaxies which have not even been hinted at by optical observations. A striking example of this is given by the K images of Arp 220. Graham et al. (1990) have used high resolution imaging to show that it has a double nucleus coincident with the radio sources in the middle of the dust lane. The results suggest that caution should be applied in the identification of optical bright spots as multiple nuclei in the absence of other evidence. They also illustrate the advantages of using infrared imaging to study the underlying structure in merging galaxies. The authors have begun a program to take near infrared images of galaxies which are believed to be mergers of disk galaxies because they have tidal tails and filaments. In many of these the merger is thought to have induced exceptionally luminous infrared emission (cf. Joseph and Wright 1985, Sanders et al. 1988). Although the optical images of the galaxies show spectacular dust lanes and filaments, the K images all have a very smooth distribution of light with an apparently single nucleus.

Wright, G. S.↗

NASA NTRS1991

Analysis of objects in binary images

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Leonard, Desiree M.↗