Wavefront sensing of atmospheric phase distortions at the Palomar 200-in. telescope and implications for adaptive optics

Major efforts in astronomical instrumentation are now being made to apply the techniques of adaptive optics to the correction of phase distortions induced by the turbulent atmosphere and by quasi-static aberrations in telescopes themselves. Despite decades of study, the problem of atmospheric turbulence is still only partially understood. We have obtained video-rate (30 Hz) imaging of stellar clusters and of single-star phase distortions over the pupil of the 200" Hale telescope on Palomar Mountain. These data show complex temporal and spatial behavior, with multiple components arising at a number of scale heights in the atmosphere; we hope to quantify this behavior to ensure the feasibility of adaptive optics at the Observatory. We have implemented different wavefront sensing techniques to measure aperture phase in wavefronts from single stars, including the classical Foucault test, which measures the local gradient of phase, and the recently-devised curvature sensing technique, which measures the second derivative of pupil phase and has formed the real-time wavefront sensor for some very productive astronomical adaptive optics. Our data, though not fast enough to capture all details of atmospheric phase fluctuations, provide important information regarding the capabilities that must be met by the adaptive optics system now being built for the 200" telescope by a team at the Jet Propulsion Lab. We describe our data acquisition techniques, initial results from efforts to characterize the properties of the turbulent atmosphere at Palomar Mountain, and future plans to extract additional quantitative parameters of use for adaptive optics performance predictions

Detecting Semantic Parts on Partially Occluded Objects

In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present.Comment: Accepted to BMVC 2017 (13 pages, 3 figures

Filter design for the detection of compact sources based on the Neyman-Pearson detector

This paper considers the problem of compact source detection on a Gaussian background in 1D. Two aspects of this problem are considered: the design of the detector and the filtering of the data. Our detection scheme is based on local maxima and it takes into account not only the amplitude but also the curvature of the maxima. A Neyman-Pearson test is used to define the region of acceptance, that is given by a sufficient linear detector that is independent on the amplitude distribution of the sources. We study how detection can be enhanced by means of linear filters with a scaling parameter and compare some of them (the Mexican Hat wavelet, the matched and the scale-adaptive filters). We introduce a new filter, that depends on two free parameters (biparametric scale-adaptive filter). The value of these two parameters can be determined, given the a priori pdf of the amplitudes of the sources, such that the filter optimizes the performance of the detector in the sense that it gives the maximum number of real detections once fixed the number density of spurious sources. The combination of a detection scheme that includes information on the curvature and a flexible filter that incorporates two free parameters (one of them a scaling) improves significantly the number of detections in some interesting cases. In particular, for the case of weak sources embedded in white noise the improvement with respect to the standard matched filter is of the order of 40%. Finally, an estimation of the amplitude of the source is introduced and it is proven that such an estimator is unbiased and it has maximum efficiency. We perform numerical simulations to test these theoretical ideas and conclude that the results of the simulations agree with the analytical ones.Comment: 15 pages, 13 figures, version accepted for publication in MNRAS. Corrected typos in Tab.

Probabilistic three-dimensional object tracking based on adaptive depth segmentation

Object tracking is one of the fundamental topics of computer vision with diverse applications. The arising challenges in tracking, i.e., cluttered scenes, occlusion, complex motion, and illumination variations have motivated utilization of depth information from 3D sensors. However, current 3D trackers are not applicable to unconstrained environments without a priori knowledge. As an important object detection module in tracking, segmentation subdivides an image into its constituent regions. Nevertheless, the existing range segmentation methods in literature are difficult to implement in real-time due to their slow performance. In this thesis, a 3D object tracking method based on adaptive depth segmentation and particle filtering is presented. In this approach, the segmentation method as the bottom-up process is combined with the particle filter as the top-down process to achieve efficient tracking results under challenging circumstances. The experimental results demonstrate the efficiency, as well as robustness of the tracking algorithm utilizing real-world range information

A method for detection of structure

Context. In order to understand the evolution of molecular clouds it is important to identify the departures from self-similarity associated with the scales of self-gravity and the driving of turbulence. Aims. A method is described based on structure functions for determining whether a region of gas, such as a molecular cloud, is fractal or contains structure with characteristic scale sizes. Methods. Using artificial data containing structure it is shown that derivatives of higher order structure functions provide a powerful way to detect the presence of characteristic scales should any be present and to estimate the size of such structures. The method is applied to observations of hot H2 in the Kleinman-Low nebula, north of the Trapezium stars in the Orion Molecular Cloud, including both brightness and velocity data. The method is compared with other techniques such as Fourier transform and histogram techniques. Results. It is found that the density structure, represented by H2 emission brightness in the K-band (2-2.5micron), exhibits mean characteristic sizes of 110, 550, 1700 and 2700AU. The velocity data show the presence of structure at 140, 1500 and 3500AU. Compared with other techniques such as Fourier transform or histogram, the method appears both more sensitive to characteristic scales and easier to interpret.Comment: Astronomy and Astrophysics, in pres

Morphologies in a Cluster of Extremely Red Galaxies with Old Stellar Populations at z=1.34

We have identified a clustering of red galaxies from deep optical/IR images obtained as part of the Institute for Astronomy Deep Survey. Photometric spectral-energy distributions indicate that most of these galaxies comprise nearly pure old stellar populations at a redshift near 1.4, and Keck spectroscopy of the three brightest red galaxies confirm this interpretation and give redshifts ranging from 1.335 to 1.338. Four of the galaxies are close together on the sky and less than 30" from an R=13.5 star, and we have obtained deep adaptive-optics imaging of this group. Detailed analysis and modeling of the surface-brightness profiles of these galaxies shows that two are normal ellipticals, one is an S0, and one appears to be an essentially pure disk of old stars, with no significant bulge. All four are highly relaxed, symmetric systems. While the old, bulgeless disk galaxy represents a type that is rare at the present epoch, the other three galaxies have structural parameters that are essentially indistinguishable from those of present-day galaxies and differ only in the age of their stellar populations.Comment: Accepted by ApJ. 10 pages including 9 figure