Preliminary Parallaxes of 40 L and T Dwarfs from the U.S. Naval Observatory Infrared Astrometry Program

We present preliminary trigonometric parallaxes and proper motions for 22 L dwarfs and 18 T dwarfs measured using the ASTROCAM infrared imager. Relative to absolute parallax corrections are made by employing 2MASS and/or SDSS photometry for reference frame stars. We combine USNO infrared and optical parallaxes with the best available CIT system photometry to determine M_J, M_H, and M_K values for 37 L dwarfs between spectral types L0 to L8 and 19 T dwarfs between spectral types T0.5 and T8 and present selected absolute magnitude versus spectral type and color diagrams, based on these results. Luminosities and temperatures are estimated for these objects. Of special interest are the distances of several objects which are at or near the L-T dwarf boundary so that this important transition can be better understood. The previously reported early-mid T dwarf luminosity excess is clearly confirmed and found to be present at J, H, and K. The large number of objects that populate this luminosity excess region indicates that it cannot be due entirely to selection effects. The T dwarf sequence is extended to M_J~16.9 by 2MASS J041519-0935 which, at d = 5.74 pc, is found to be the least luminous [log(L/L_sun)=-5.58] and coldest (T_eff~760 K) brown dwarf known. Combining results from this paper with earlier USNO CCD results we find that, in contrast to the L dwarfs, there are no examples of low velocity (V_tan < 20 km/s) T dwarfs. We briefly discuss future directions for the USNO infrared astrometry program.Comment: 73 pages, 9 figures, 9 tables, accepted for publication in The Astronomical Journa

Spitzer, Near-Infrared, and Submillimeter Imaging of the Relatively Sparse Young Cluster, Lynds 988e

We present {\it Spitzer} images of the relatively sparse, low luminosity young cluster L988e, as well as complementary near-infrared (NIR) and submillimeter images of the region. The cluster is asymmetric, with the western region of the cluster embedded within the molecular cloud, and the slightly less dense eastern region to the east of, and on the edge of, the molecular cloud. With these data, as well as with extant H$\alpha$ data of stars primarily found in the eastern region of the cluster, and a molecular $^{13}$CO gas emission map of the entire region, we investigate the distribution of forming young stars with respect to the cloud material, concentrating particularly on the differences and similarities between the exposed and embedded regions of the cluster. We also compare star formation in this region to that in denser, more luminous and more massive clusters already investigated in our comprehensive multi-wavelength study of young clusters within 1 kpc of the Sun.Comment: 21 pages, 6 tables, 13 figures. Full resolution figures at: http://astro.pas.rochester.edu/~tom/Preprints/L988e.pd

The Spectroscopically Determined Substellar Mass Function of the Orion Nebula Cluster

We present a spectroscopic study of candidate brown dwarf members of the Orion Nebula Cluster (ONC). We obtained new J- and/or K-band spectra of ~100 objects within the ONC which are expected to be substellar based on their K,(H-K) magnitudes and colors. Spectral classification in the near-infrared of young low mass objects is described, including the effects of surface gravity, veiling due to circumstellar material, and reddening. From our derived spectral types and existing near-infrared photometry we construct an HR diagram for the cluster. Masses are inferred for each object and used to derive the brown dwarf fraction and assess the mass function for the inner 5.'1 x 5.'1 of the ONC, down to ~0.02 solar masses. The derived logarithmic mass function rises to a peak at ~0.2 solar masses, similar to previous IMF determinations derived from purely photometric methods, but falls off more sharply at the hydrogen-burning limit before leveling through the substellar regime. We compare the mass function derived here for the inner ONC to those presented in recent literature for the sparsely populated Taurus cloud members and the rich cluster IC 348. We find good agreement between the shapes and peak values of the ONC and IC 348 mass distributions, but little similarity between the ONC and Taurus results.Comment: Accepted for Publication in Apj. Added Erratu

The Aromatic Features in Very Faint Dwarf Galaxies

We present optical and mid-infrared photometry of a statistically complete sample of 29 very faint dwarf galaxies (M_r > -15 mag) selected from the SDSS spectroscopic sample and observed in the mid-infrared with Spitzer IRAC. This sample contains nearby (redshift z<0.005) galaxies three magnitudes fainter than previously studied samples. We compare our sample with other star-forming galaxies that have been observed with both IRAC and SDSS. We examine the relationship of the infrared color, sensitive to PAH abundance, with star-formation rates, gas-phase metallicities and radiation hardness, all estimated from optical emission lines. Consistent with studies of more luminous dwarfs, we find that the very faint dwarf galaxies show much weaker PAH emission than more luminous galaxies with similar specific star-formation rates. Unlike more luminous galaxies, we find that the very faint dwarf galaxies show no significant dependence at all of PAH emission on star-formation rate, metallicity, or radiation hardness, despite the fact that the sample spans a significant range in all of these quantities. When the very faint dwarfs in our sample are compared with more luminous (M_r ~ -18 mag) dwarfs, we find that PAH emission depends on metallicity and radiation hardness. These two parameters are correlated; we look at the PAH-metallicity relation at fixed radiation hardness and the PAH-hardness relation at fixed metallicity. This test shows that the PAH emission in dwarf galaxies depends most directly on metallicity.Comment: submitted to Ap

Surface analysis and visualization from multi-light image collections

Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation

Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo

In this paper, we build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations based on an arbitrary number of images captured under different light directions, performing accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules for the photometric stereo problem. Moreover, we propose to utilize the easily obtained object mask for eliminating adverse interference from invalid background regions in intra-frame spatial convolutions, thus effectively improve the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.Comment: 9 pages,8 figure

PS-FCN: A Flexible Learning Framework for Photometric Stereo

This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo.Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.Comment: ECCV 2018: https://guanyingc.github.io/PS-FC