Motion from Fixation

We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. The motivation comes from the mechanics of the buman eye, which either pursuits smoothly some fixation point in the scene, or "saccades" between different fixation points. In particular, we are interested in understanding whether fixation helps the process of estimating motion in the sense that it makes it more robust, better conditioned or simpler to solve. We cast the problem in the framework of "dynamic epipolar geometry", and propose an implicit dynamical model for recursively estimating motion from fixation. This allows us to compare directly the quality of the estimates of motion obtained by imposing the fixation constraint, or by assuming a general rigid motion, simply by changing the geometry of the parameter space while maintaining the same structure of the recursive estimator. We also present a closed-form static solution from two views, and a recursive estimator of the absolute attitude between the viewer and the scene. One important issue is how do the estimates degrade in presence of disturbances in the tracking procedure. We describe a simple fixation control that converges exponentially, which is complemented by a image shift-registration for achieving sub-pixel accuracy, and assess how small deviations from perfect tracking affect the estimates of motion

Reducing "Structure From Motion": a General Framework for Dynamic Vision - Part 2: Experimental Evaluation

A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Although all methods may be derived from a "natural" dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the specific applications and the goals one is targeting. Which one is the winning strategy? In this paper we analyze the properties of the dynamical models that originate from each strategy under a variety of experimental conditions. For each model we assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate

Reducing “Structure from Motion”: a general framework for dynamic vision. 2. Implementation and experimental assessment

For pt.1 see ibid., p.933-42 (1998). A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Despite the fact that all methods may be derived from a “natural” dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the applications and the goals one is targeting. We want to characterize and compare the properties of each model such that the engineer may choose the one best suited to the specific application. We analyze the properties of filters derived from each dynamical model under a variety of experimental conditions, assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate

Cloud Atlas: High-precision HST/WFC3/IR Time-resolved Observations of Directly Imaged Exoplanet HD 106906b

HD 106906b is an ~11M_(Jup), ~15 Myr old directly imaged exoplanet orbiting at an extremely large distance from its host star. The wide separation (7 11) between HD 106906b and its host star greatly reduces the difficulty in direct-imaging observations, making it one of the most favorable directly imaged exoplanets for detailed characterization. In this paper, we present HST/WFC3/IR time-resolved observations of HD 106906b in the F127M, F139M, and F153M bands. We have achieved ~1% precision in the lightcurves in all three bands. The F127M lightcurve demonstrates marginally detectable (2.7σ significance) variability with a best-fitting period of 4 hr, while the lightcurves in the other two bands are consistent with flat lines. We construct primary-subtracted deep images and use these images to exclude additional companions to HD 106906 that are more massive than 4 M_(Jup) and locate at projected distances of more than ~500 au. We measure the astrometry of HD 106906b in two HST/WFC3 epochs and achieve precisions better than 2.5 mas. The position angle and separation measurements do not deviate from those in the 2004 HST/ACS/HRC images for more than 1σ uncertainty. We provide the HST/WFC3 astrometric results for 25 background stars that can be used as reference sources in future precision astrometry studies. Our observations also provide the first 1.4 μm water band photometric measurement for HD 106906b. HD 106906b's spectral energy distribution and the best-fitting BT-Settl model have an inconsistency in the 1.4 μm water absorption band, which highlights the challenges in modeling atmospheres of young planetary-mass objects

Quantum Ontologies and Mind-Matter Synthesis

Aspects of a quantum mechanical theory of a world containing efficacious mental aspects that are closely tied to brains, but that are not identical to brains.Comment: 69 pages. Invited contribution to Xth Max Born Symposium: "Quantum Future". Published in "Quantum Future", eds. P. Blanchard and A. Jadczyk, Springer-Verlag, 1999, ISBN 3-540-65218-3. LBNL 4072

Dynamical Analysis of Nearby ClustErs. Automated astrometry from the ground: precision proper motions over wide field

The kinematic properties of the different classes of objects in a given association hold important clues about its member's history, and offer a unique opportunity to test the predictions of the various models of stellar formation and evolution. DANCe (standing for Dynamical Analysis of Nearby ClustErs) is a survey program aimed at deriving a comprehensive and homogeneous census of the stellar and substellar content of a number of nearby (<1kpc) young (<500Myr) associations. Whenever possible, members will be identified based on their kinematics properties, ensuring little contamination from background and foreground sources. Otherwise, the dynamics of previously confirmed members will be studied using the proper motion measurements. We present here the method used to derive precise proper motion measurements, using the Pleiades cluster as a test bench. Combining deep wide field multi-epoch panchromatic images obtained at various obervatories over up to 14 years, we derive accurate proper motions for the sources present in the field of the survey. The datasets cover ~80 square degrees, centered around the Seven Sisters. Using new tools, we have computed a catalog of 6116907 unique sources, including proper motion measurements for 3577478 of them. The catalogue covers the magnitude range between i=12~24mag, achieving a proper motion accuracy <1mas/yr for sources as faint as i=22.5mag. We estimate that our final accuracy reaches 0.3mas/yr in the best cases, depending on magnitude, observing history, and the presence of reference extragalactic sources for the anchoring onto the ICRS.Comment: Accepted for publication in A&

NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration

Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a novel, generic, and accurate diffeomorphic image registration framework that utilizes neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Our method leverages deep neural networks for their expressive power in modeling dynamical systems, and simultaneously optimizes for a dynamical system between the image pairs and the corresponding transformation. Our formulation allows various constraints to be imposed along the transformation to maintain desired regularities. Our experiment results show that our method outperforms the benchmarks under various metrics. Additionally, we demonstrate the feasibility to expand our framework to register multiple image sets using a unified form of transformation,which could possibly serve a wider range of applications