8,060 research outputs found

    A photogeologic comparison of Skylab and LANDSAT images of southwestern Nevada and southeastern California

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    There are no author-identified significant results in this report

    Sensitive VLBI Continuum and H I Absorption Observations of NGC 7674: First Scientific Observations with the Combined Array VLBA, VLA & Arecibo

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    We present phase-referenced VLBI observations of the radio continuum emission from, and the H I 21 cm absorption toward, the Luminous Infrared Galaxy NGC 7674. The observations were carried out at 1380 MHz using the VLBA, the phased VLA, and theArecibo radio telescope. These observations constitute the first scientific use of the Arecibo telescope in a VLBI observation with the VLBA. The high- and low-resolution radio continuum images reveal several new continuum structures in the nuclear region of this galaxy. At ~100 mas resolution, we distinguish six continuum structures extending over 1.4 arcsec, with a total flux density of 138 mJy. Only three of these structures were known previously. All these structures seem to be related to AGN activity. At the full resolution of the array, we only detect two of the six continuum structures. Both are composed of several compact components with brightness temperatures on the order of 10710^{7} K. While it is possible that one of these compact structures could host an AGN, they could also be shock-like features formed by the interaction of the jet with compact interstellar clouds in the nuclear region of this galaxy. Complex H I absorption is detected with our VLBI array at both high and low angular resolution. Assuming that the widest H I feature is associated with a rotating H I disk or torus feeding a central AGN, we estimate an enclosed dynamical mass of ~7 x 10^7 M_sun, comparable to the value derived from the hidden broad Hβ\beta emission in this galaxy. The narrower H I lines could represent clumpy neutral hydrogen structures in the H I torus. The detection of H I absorption toward some of the continuum components, and its absence toward others, suggest an inclined H I disk or torus in the central region of NGC 7674.Comment: 37 pages, 11 figures. ApJ accepted. To appear in the Nov. 10, 2003 issue of ApJ. Please use the PDF version if the postscript doesn't show the figure

    Spitzer Infrared Spectrograph Detection of Molecular Hydrogen Rotational Emission towards Translucent Clouds

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    Using the Infrared Spectrograph on board the Spitzer Space Telescope, we have detected emission in the S(0), S(1), and S(2) pure-rotational (v = 0-0) transitions of molecular hydrogen (H_2) toward six positions in two translucent high Galactic latitude clouds, DCld 300.2–16.9 and LDN 1780. The detection of these lines raises important questions regarding the physical conditions inside low-extinction clouds that are far from ultraviolet radiation sources. The ratio between the S(2) flux and the flux from polycyclic aromatic hydrocarbons (PAHs) at 7.9 μm averages 0.007 for these six positions. This is a factor of about four higher than the same ratio measured toward the central regions of non-active Galaxies in the Spitzer Infrared Nearby Galaxies Survey. Thus, the environment of these translucent clouds is more efficient at producing rotationally excited H_2 per PAH-exciting photon than the disks of entire galaxies. Excitation analysis finds that the S(1) and S(2) emitting regions are warm (T ≳ 300 K), but comprise no more than 2% of the gas mass. We find that UV photons cannot be the sole source of excitation in these regions and suggest mechanical heating via shocks or turbulent dissipation as the dominant cause of the emission. The clouds are located on the outskirts of the Scorpius-Centaurus OB association and may be dissipating recent bursts of mechanical energy input from supernova explosions. We suggest that pockets of warm gas in diffuse or translucent clouds, integrated over the disks of galaxies, may represent a major source of all non-active galaxy H_2 emission

    Star-forming Clumps in Local Luminous Infrared Galaxies

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    We present HST narrowband near-infrared imaging of Paα and Paβ emission of 48 local luminous infrared galaxies (LIRGs) from the Great Observatories All-Sky LIRG Survey. These data allow us to measure the properties of 810 spatially resolved star-forming regions (59 nuclei and 751 extranuclear clumps) and directly compare their properties to those found in both local and high-redshift star-forming galaxies. We find that in LIRGs the star-forming clumps have radii ranging from ~90 to 900 pc and star formation rates (SFRs) of ~1 × 10⁻³ to 10 M⊙ yr⁻¹, with median values for extranuclear clumps of 170 pc and 0.03 M⊙ yr⁻¹. The detected star-forming clumps are young, with a median stellar age of 8.7 Myr, and have a median stellar mass of 5 × 10⁵ M ⊙. The SFRs span the range of those found in normal local star-forming galaxies to those found in high-redshift star-forming galaxies at z = 1–3. The luminosity function of the LIRG clumps has a flatter slope than found in lower-luminosity, star-forming galaxies, indicating a relative excess of luminous star-forming clumps. In order to predict the possible range of star-forming histories and gas fractions, we compare the star-forming clumps to those measured in the MassiveFIRE high-resolution cosmological simulation. The star-forming clumps in MassiveFIRE cover the same range of SFRs and sizes found in the local LIRGs and have total gas fractions that extend from 10% to 90%. If local LIRGs are similar to these simulated galaxies, we expect that future observations with ALMA will find a large range of gas fractions, and corresponding star formation efficiencies, among the star-forming clumps in LIRGs

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

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    Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate

    Robust thermal face recognition using region classifiers

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    This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 different region classifiers. The region classifiers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classifier is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identification process difficult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature

    Long range facial image acquisition and quality

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    Abstract This chapter introduces issues in long range facial image acquisition and measures for image quality and their usage. Section 1, on image acquisition for face recognition discusses issues in lighting, sensor, lens, blur issues, which impact short-range biometrics, but are more pronounced in long-range biometrics. Section 2 introduces the design of controlled experiments for long range face, and why they are needed. Section 3 introduces some of the weather and atmospheric effects that occur for long-range imaging, with numerous of examples. Section 4 addresses measurements of “system quality”, including image-quality measures and their use in prediction of face recognition algorithm. That section introduces the concept of failure prediction and techniques for analyzing different “quality ” measures. The section ends with a discussion of post-recognition ”failure prediction ” and its potential role as a feedback mechanism in acquisition. Each section includes a collection of open-ended questions to challenge the reader to think about the concepts more deeply. For some of the questions we answer them after they are introduced; others are left as an exercise for the reader. 1 Image Acquisition Before any recognition can even be attempted, they system must acquire an image of the subject with sufficient quality and resolution to detect and recognize the face. The issues examined in this section are the sensor-issues in lighting, image/sensor resolution issues, the field-of view, the depth of field, and effects of motion blur
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