Inferring the photometric and size evolution of galaxies from image simulations

Current constraints on models of galaxy evolution rely on morphometric catalogs extracted from multi-band photometric surveys. However, these catalogs are altered by selection effects that are difficult to model, that correlate in non trivial ways, and that can lead to contradictory predictions if not taken into account carefully. To address this issue, we have developed a new approach combining parametric Bayesian indirect likelihood (pBIL) techniques and empirical modeling with realistic image simulations that reproduce a large fraction of these selection effects. This allows us to perform a direct comparison between observed and simulated images and to infer robust constraints on model parameters. We use a semi-empirical forward model to generate a distribution of mock galaxies from a set of physical parameters. These galaxies are passed through an image simulator reproducing the instrumental characteristics of any survey and are then extracted in the same way as the observed data. The discrepancy between the simulated and observed data is quantified, and minimized with a custom sampling process based on adaptive Monte Carlo Markov Chain methods. Using synthetic data matching most of the properties of a CFHTLS Deep field, we demonstrate the robustness and internal consistency of our approach by inferring the parameters governing the size and luminosity functions and their evolutions for different realistic populations of galaxies. We also compare the results of our approach with those obtained from the classical spectral energy distribution fitting and photometric redshift approach.Our pipeline infers efficiently the luminosity and size distribution and evolution parameters with a very limited number of observables (3 photometric bands). When compared to SED fitting based on the same set of observables, our method yields results that are more accurate and free from systematic biases.Comment: 24 pages, 12 figures, accepted for publication in A&

Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure

Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially challenging in highly dynamic environments. Such environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Previous work in object tracking focuses on maintaining the integrity of object tracks but usually relies on specific appearance-based descriptors or constrained motion models. These approaches are very effective in specific applications but do not generalize to the full multimotion estimation problem. This paper presents a pipeline for estimating multiple motions, including the camera egomotion, in the presence of occlusions. This approach uses an expressive motion prior to estimate the SE (3) trajectory of every motion in the scene, even during temporary occlusions, and identify the reappearance of motions through motion closure. The performance of this occlusion-robust multimotion visual odometry (MVO) pipeline is evaluated on real-world data and the Oxford Multimotion Dataset.Comment: To appear at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). An earlier version of this work first appeared at the Long-term Human Motion Planning Workshop (ICRA 2019). 8 pages, 5 figures. Video available at https://www.youtube.com/watch?v=o_N71AA6FR

The Luminosity Function at z~8 from 97 Y-band dropouts: Inferences About Reionization

[Abbreviated] We present the largest search to date for $z\sim8$ Lyman break galaxies (LBGs) based on 350 arcmin$^2$ of HST observations in the V-, Y-, J- and H-bands from the Brightest of Reionizing Galaxies (BoRG) survey. The BoRG dataset includes $\sim$50 arcmin$^2$ of new data and deeper observations of two previous BoRG pointings, from which we present 9 new $z\sim8$ LBG candidates, bringing the total number of BoRG LBGs to 38 with $25.5\leqslant m_{J} \leqslant 27.6$ (AB system). We introduce a new Bayesian formalism for estimating the galaxy luminosity function (LF), which does not require binning (and thus smearing) of the data and includes a likelihood based on the formally correct binomial distribution as opposed to the often used approximate Poisson distribution. We demonstrate the utility of the new method on a sample of $97$ LBGs that combines the bright BoRG galaxies with the fainter sources published in Bouwens et al. (2012) from the HUDF and ERS programs. We show that the $z\sim8$ LF is well described by a Schechter function with a characteristic magnitude $M^\star = -20.15^{+0.29}_{-0.38}$, a faint-end slope of $\alpha = -1.87^{+0.26}_{-0.26}$, and a number density of $\log_{10} \phi^\star [\textrm{Mpc}^{-3}] = -3.24^{+0.25}_{-0.24}$. Integrated down to $M=-17.7$ this LF yields a luminosity density, $\log_{10} \epsilon [\textrm{erg}/\textrm{s/Hz/Mpc}^{3}] = 25.52^{+0.05}_{-0.05}$. Our LF analysis is consistent with previously published determinations within 1$\sigma$. We discuss the implication of our study for the physics of reionization. By assuming theoretically motivated priors on the clumping factor and the photon escape fraction we show that the UV LF from galaxy samples down to $M=-17.7$ can ionize only 10-50% of the neutral hydrogen at $z\sim8$. Full reionization would require extending the LF down to $M=-15$.Comment: Accepted for publication in ApJ, 22 pages, 15 figure

Continuous Modeling of 3D Building Rooftops From Airborne LIDAR and Imagery

In recent years, a number of mega-cities have provided 3D photorealistic virtual models to support the decisions making process for maintaining the cities' infrastructure and environment more effectively. 3D virtual city models are static snap-shots of the environment and represent the status quo at the time of their data acquisition. However, cities are dynamic system that continuously change over time. Accordingly, their virtual representation need to be regularly updated in a timely manner to allow for accurate analysis and simulated results that decisions are based upon. The concept of "continuous city modeling" is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. However, developing a universal intelligent machine enabling continuous modeling still remains a challenging task. Therefore, this thesis proposes a novel research framework for continuously reconstructing 3D building rooftops using multi-sensor data. For achieving this goal, we first proposes a 3D building rooftop modeling method using airborne LiDAR data. The main focus is on the implementation of an implicit regularization method which impose a data-driven building regularity to noisy boundaries of roof planes for reconstructing 3D building rooftop models. The implicit regularization process is implemented in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). Secondly, we propose a context-based geometric hashing method to align newly acquired image data with existing building models. The novelty is the use of context features to achieve robust and accurate matching results. Thirdly, the existing building models are refined by newly proposed sequential fusion method. The main advantage of the proposed method is its ability to progressively refine modeling errors frequently observed in LiDAR-driven building models. The refinement process is conducted in the framework of MDL combined with HAT. Markov Chain Monte Carlo (MDMC) coupled with Simulated Annealing (SA) is employed to perform a global optimization. The results demonstrates that the proposed continuous rooftop modeling methods show a promising aspects to support various critical decisions by not only reconstructing 3D rooftop models accurately, but also by updating the models using multi-sensor data

Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments

Optical microscopy provides rich spatio-temporal information characterizing in vivo molecular motion. However, effective forces and other parameters used to summarize molecular motion change over time in live cells due to latent state changes, e.g., changes induced by dynamic micro-environments, photobleaching, and other heterogeneity inherent in biological processes. This study focuses on techniques for analyzing Single Particle Tracking (SPT) data experiencing abrupt state changes. We demonstrate the approach on GFP tagged chromatids experiencing metaphase in yeast cells and probe the effective forces resulting from dynamic interactions that reflect the sum of a number of physical phenomena. State changes are induced by factors such as microtubule dynamics exerting force through the centromere, thermal polymer fluctuations, etc. Simulations are used to demonstrate the relevance of the approach in more general SPT data analyses. Refined force estimates are obtained by adopting and modifying a nonparametric Bayesian modeling technique, the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT applications. The HDP-SLDS method shows promise in systematically identifying dynamical regime changes induced by unobserved state changes when the number of underlying states is unknown in advance (a common problem in SPT applications). We expand on the relevance of the HDP-SLDS approach, review the relevant background of Hierarchical Dirichlet Processes, show how to map discrete time HDP-SLDS models to classic SPT models, and discuss limitations of the approach. In addition, we demonstrate new computational techniques for tuning hyperparameters and for checking the statistical consistency of model assumptions directly against individual experimental trajectories; the techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One publication available freely as an open-access article at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763

Improved image analysis by maximised statistical use of geometry-shape constraints

Identifying the underlying models in a set of data points contaminated by noise and outliers, leads to a highly complex multi-model fitting problem. This problem can be posed as a clustering problem by the construction of higher order affinities between data points into a hypergraph, which can then be partitioned using spectral clustering. Calculating the weights of all hyperedges is computationally expensive. Hence an approximation is required. In this thesis, the aim is to find an efficient and effective approximation that produces an excellent segmentation outcome. Firstly, the effect of hyperedge sizes on the speed and accuracy of the clustering is investigated. Almost all previous work on hypergraph clustering in computer vision, has considered the smallest possible hyperedge size, due to the lack of research into the potential benefits of large hyperedges and effective algorithms to generate them. In this thesis, it is shown that large hyperedges are better from both theoretical and empirical standpoints. The efficiency of this technique on various higher-order grouping problems is investigated. In particular, we show that our approach improves the accuracy and efficiency of motion segmentation from dense, long-term, trajectories. A shortcoming of the above approach is that the probability of a generated sample being impure increases as the size of the sample increases. To address this issue, a novel guided sampling strategy for large hyperedges, based on the concept of minimizing the largest residual, is also included. It is proposed to guide each sample by optimizing over a $k$\textsuperscript{th} order statistics based cost function. Samples are generated using a greedy algorithm coupled with a data sub-sampling strategy. The experimental analysis shows that this proposed step is both accurate and computationally efficient compared to state-of-the-art robust multi-model fitting techniques. However, the optimization method for guiding samples involves hard-to-tune parameters. Thus a sampling method is eventually developed that significantly facilitates solving the segmentation problem using a new form of the Markov-Chain-Monte-Carlo (MCMC) method to efficiently sample from hyperedge distribution. To sample from the above distribution effectively, the proposed Markov Chain includes new types of long and short jumps to perform exploration and exploitation of all structures. Unlike common sampling methods, this method does not require any specific prior knowledge about the distribution of models. The output set of samples leads to a clustering solution by which the final model parameters for each segment are obtained. The overall method competes favorably with the state-of-the-art both in terms of computation power and segmentation accuracy