The brightness clustering transform and locally contrasting keypoints

In recent years a new wave of feature descriptors has been presented to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages of tasks such as image matching or visual odometry, enabling real time applications. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. We present a new blob- detector which can be implemented in real time and is faster than most of the currently used feature-detectors. The detection is achieved with an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT). The BCT can be thought as a coarse-to- fine search through scale spaces for the true derivative of the image; it also mimics trans-saccadic perception of human vision. We call the new algorithm Locally Contrasting Keypoints detector or LOCKY. Showing good repeatability and robustness to image transformations included in the Oxford dataset, LOCKY is amongst the fastest affine-covariant feature detectors

η\eta Carinae's Dusty Homunculus Nebula from Near-Infrared to Submillimeter Wavelengths: Mass, Composition, and Evidence for Fading Opacity

Infrared observations of the dusty, massive Homunculus Nebula around the luminous blue variable $\eta$ Carinae are crucial to characterize the mass-loss history and help constrain the mechanisms leading to the Great Eruption. We present the 2.4 - 670 $\mu$m spectral energy distribution, constructed from legacy ISO observations and new spectroscopy obtained with the {\em{Herschel Space Observatory}}. Using radiative transfer modeling, we find that the two best-fit dust models yield compositions which are consistent with CNO-processed material, with iron, pyroxene and other metal-rich silicates, corundum, and magnesium-iron sulfide in common. Spherical corundum grains are supported by the good match to a narrow 20.2 $\mu$m feature. Our preferred model contains nitrides AlN and Si$_3$N$_4$ in low abundances. Dust masses range from 0.25 to 0.44 $M_\odot$ but $M_{\rm{tot}} \ge$ 45 $M_\odot$ in both cases due to an expected high Fe gas-to-dust ratio. The bulk of dust is within a 5$"$ $\times$ 7$"$ central region. An additional compact feature is detected at 390 $\mu$m. We obtain $L_{\rm{IR}}$ = 2.96 $\times$ 10$^6$ $L_\odot$, a 25\% decline from an average of mid-IR photometric levels observed in 1971-1977. This indicates a reduction in circumstellar extinction in conjunction with an increase in visual brightness, allowing 25-40\% of optical and UV radiation to escape from the central source. We also present an analysis of $^{12}$CO and $^{13}$CO $J = 5-4$ through $9-8$ lines, showing that the abundances are consistent with expectations for CNO-processed material. The [$^{12}$C~{\sc{ii}}] line is detected in absorption, which we suspect originates in foreground material at very low excitation temperatures.Comment: Accepted in Ap

Holography in the Flat Space Limit

Matrix theory and the AdS/CFT correspondence provide nonperturbative holographic formulations of string theory. In both cases the finite N theories can be thought of as infrared regulated versions of flat space string theory in which removing the cutoff is equivalent to letting N go to infinity. In this paper we consider the nature of this limit. In both cases the holographic mapping becomes completely nonlocal. In matrix theory this corresponds to the growth of D0-brane bound states with N. For the AdS/CFT correspondence there is a similar delocalization of the holographic image of a system as N increases. In this case the limiting theory seems to require a number of degrees of freedom comparable to large N matrix quantum mechanics

The Infrared Array Camera (IRAC) for the Spitzer Space Telescope

The Infrared Array Camera (IRAC) is one of three focal plane instruments in the Spitzer Space Telescope. IRAC is a four-channel camera that obtains simultaneous broad-band images at 3.6, 4.5, 5.8, and 8.0 microns. Two nearly adjacent 5.2x5.2 arcmin fields of view in the focal plane are viewed by the four channels in pairs (3.6 and 5.8 microns; 4.5 and 8 microns). All four detector arrays in the camera are 256x256 pixels in size, with the two shorter wavelength channels using InSb and the two longer wavelength channels using Si:As IBC detectors. IRAC is a powerful survey instrument because of its high sensitivity, large field of view, and four-color imaging. This paper summarizes the in-flight scientific, technical, and operational performance of IRAC.Comment: 7 pages, 3 figures. Accepted for publication in the ApJS. A higher resolution version is at http://cfa-www.harvard.edu/irac/publication

Direct occlusion handling for high level image processing algorithms

Many high-level computer vision algorithms suffer in the presence of occlusions caused by multiple objects overlapping in a view. Occlusions remove the direct correspondence between visible areas of objects and the objects themselves by introducing ambiguity in the interpretation of the shape of the occluded object. Ignoring this ambiguity allows the perceived geometry of overlapping objects to be deformed or even fractured. Supplementing the raw image data with a vectorized structural representation which predicts object completions could stabilize high-level algorithms which currently disregard occlusions. Studies in the neuroscience community indicate that the feature points located at the intersection of junctions may be used by the human visual system to produce these completions. Geiger, Pao, and Rubin have successfully used these features in a purely rasterized setting to complete objects in a fashion similar to what is demonstrated by human perception. This work proposes using these features in a vectorized approach to solving the mid-level computer vision problem of object stitching. A system has been implemented which is able extract L and T-junctions directly from the edges of an image using scale-space and robust statistical techniques. The system is sensitive enough to be able to isolate the corners on polygons with 24 sides or more, provided sufficient image resolution is available. Areas of promising development have been identified and several directions for further research are proposed