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Mathieu Bredif - One of the best experts on this subject based on the ideXlab platform.
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a classification segmentation framework for the detection of individual trees in dense mms Point cloud data acquired in urban areas
Remote Sensing, 2017Co-Authors: Martin Weinmann, Clement Mallet, Michael Weinmann, Mathieu BredifAbstract:In this paper, we present a novel framework for detecting individual trees in densely sampled 3D Point cloud data acquired in urban areas. Given a 3D Point cloud, the objective is to assign Point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a Binary Point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D Points, whereby the labels are defined as “tree Points” and “other Points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree Points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D Points among which 17.6 % are labeled as “tree Points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h).
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detection segmentation and localization of individual trees from mms Point cloud data
GEOBIA 2016 : Solutions and Synergies, 2016Co-Authors: Martin Weinmann, Clement Mallet, Mathieu BredifAbstract:In this paper, we address the extraction of objects from 3D Point clouds acquired with mobile mapping systems. More specifically, we focus on the detection of tree-like objects, a subsequent segmentation of individual trees and a localization of the respective trees. Thereby, the detection of tree-like objects is achieved via a Binary Point-wise classification based on geometric features, which categorizes each Point of the 3D Point cloud into either tree-like objects or non-tree-like objects. The subsequent segmentation and localization of individual trees is carried out by applying a 2D projection and a mean shift segmentation on a downsampled version of that part of the original 3D Point cloud which represents all tree-like objects, and it also involves a segment-based shape analysis to only retain plausible tree segments. We demonstrate the performance of our framework on a benchmark dataset which contains 10:13M 3D Points and has been acquired with a mobile mapping system in the city of Delft in the Netherlands.
Anand Sivaramakrishnan - One of the best experts on this subject based on the ideXlab platform.
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an image plane algorithm for jwst s non redundant aperture mask data
The Astrophysical Journal, 2014Co-Authors: Alexandra Z Greenbaum, Laurent Pueyo, Anand Sivaramakrishnan, Sylvestre LacourAbstract:The high angular resolution technique of non-redundant masking (NRM) or aperture masking interferometry (AMI) has yielded images of faint protoplanetary companions of nearby stars from the ground. AMI on James Webb Space Telescope (JWST)'s Near Infrared Imager and Slitless Spectrograph (NIRISS) has a lower thermal background than ground-based facilities and does not suffer from atmospheric instability. NIRISS AMI images are likely to have 90%-95% Strehl ratio between 2.77 and 4.8 μm. In this paper we quantify factors that limit the raw Point source contrast of JWST NRM. We develop an analytic model of the NRM Point spread function which includes different optical path delays (pistons) between mask holes and fit the model parameters with image plane data. It enables a straightforward way to exclude bad pixels, is suited to limited fields of view, and can incorporate effects such as intra-pixel sensitivity variations. We simulate various sources of noise to estimate their effect on the standard deviation of closure phase, σCP (a proxy for Binary Point source contrast). If σCP < 10–4 radians—a contrast ratio of 10 mag—young accreting gas giant planets (e.g., in the nearby Taurus star-forming region) could be imaged with JWST NIRISS. We show the feasibility of using NIRISS' NRM with the sub-Nyquist sampled F277W, which would enable some exoplanet chemistry characterization. In the presence of small piston errors, the dominant sources of closure phase error (depending on pixel sampling, and filter bandwidth) are flat field errors and unmodeled variations in intra-pixel sensitivity. The in-flight stability of NIRISS will determine how well these errors can be calibrated by observing a Point source. Our results help develop efficient observing strategies for space-based NRM.
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active galactic nucleus and quasar science with aperture masking interferometry on the james webb space telescope
The Astrophysical Journal, 2014Co-Authors: Anand Sivaramakrishnan, K Saavik E Ford, Barry Mckernan, A R MartelAbstract:Due to feedback from accretion onto supermassive black holes (SMBHs), active galactic nuclei (AGNs) are believed to play a key role in ΛCDM cosmology and galaxy formation. However, AGNs extreme luminosities and the small angular size of their accretion flows create a challenging imaging problem. We show that the James Webb Space Telescope's Near Infrared Imager and Slitless Spectrograph (JWST-NIRISS) Aperture Masking Interferometry (AMI) mode will enable true imaging (i.e., without any requirement of prior assumptions on source geometry) at ∼65 mas angular resolution at the centers of AGNs. This is advantageous for studying complex extended accretion flows around SMBHs and in other areas of angular-resolution-limited astrophysics. By simulating data sequences incorporating expected sources of noise, we demonstrate that JWST-NIRISS AMI mode can map extended structure at a pixel-to-pixel contrast of ∼10{sup –2} around an L = 7.5 Point source, using short exposure times (minutes). Such images will test models of AGN feedback, fueling, and structure (complementary with ALMA observations), and are not currently supported by any ground-based IR interferometer or telescope. Binary Point source contrast with NIRISS is ∼10{sup –4} (for observing Binary nuclei in merging galaxies), significantly better than current ground-based optical or IR interferometry. JWST-NIRISS's seven-hole non-redundantmore » mask has a throughput of 15%, and utilizes NIRISS's F277W (2.77 μm), F380M (3.8 μm), F430M (4.3 μm), and F480M (4.8 μm) filters. NIRISS's square pixels are 65 mas per side, with a field of view ∼2' × 2'. We also extrapolate our results to AGN science enabled by non-redundant masking on future 2.4 m and 16 m space telescopes working at long-UV to near-IR wavelengths.« less
Alexandre Le Tiec - One of the best experts on this subject based on the ideXlab platform.
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first law of compact Binary mechanics with gravitational wave tails
Classical and Quantum Gravity, 2017Co-Authors: Luc Blanchet, Alexandre Le TiecAbstract:We derive the first law of Binary Point-particle mechanics for generic bound (i.e. eccentric) orbits at the fourth post-Newtonian (4PN) order, accounting for the non-locality in time of the dynamics due to the occurence of a gravitational-wave tail effect at that order. Using this first law, we show how the periastron advance of the Binary system can be related to the averaged redshift of one of the two bodies for a slightly non-circular orbit, in the limit where the eccentricity vanishes. Combining this expression with existing analytical self-force results for the averaged redshift, we recover the known 4PN expression for the circular-orbit periastron advance, to linear order in the mass ratio.
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first law of mechanics for compact binaries on eccentric orbits
Physical Review D, 2015Co-Authors: Alexandre Le TiecAbstract:Using the canonical (ADM) Hamiltonian formalism, a ``first law of mechanics'' for Binary Point masses is presented, and is explicitly verified in the weak field perturbative limit. This ``first law'' provides a means of comparing predictions using different formalisms, valid for different parameter regimes, e.g. Post-Newtonian perturbation theory, numerical relativity or self-force calculations.
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first law of Binary black hole mechanics in general relativity and post newtonian theory
Physical Review D, 2012Co-Authors: Alexandre Le Tiec, Luc Blanchet, Bernard F WhitingAbstract:First laws of black hole mechanics, or thermodynamics, come in a variety of different forms. In this paper, from a purely post-Newtonian (PN) analysis, we obtain a first law for Binary systems of Point masses moving along an exactly circular orbit. Our calculation is valid through 3PN order and includes, in addition, the contributions of logarithmic terms at 4PN and 5PN orders. This first law of Binary Point-particle mechanics is then derived from first principles in general relativity, and analogies are drawn with the single and Binary black hole cases. Some consequences of the first law are explored for PN spacetimes. As one such consequence, a simple relation between the PN binding energy of the Binary system and Detweiler's redshift observable is established. Through it, we are able to determine with high precision the numerical values of some previously unknown high order PN coefficients in the circular-orbit binding energy. Finally, we propose new gauge invariant notions for the energy and angular momentum of a particle in a Binary system.
Martin Weinmann - One of the best experts on this subject based on the ideXlab platform.
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a classification segmentation framework for the detection of individual trees in dense mms Point cloud data acquired in urban areas
Remote Sensing, 2017Co-Authors: Martin Weinmann, Clement Mallet, Michael Weinmann, Mathieu BredifAbstract:In this paper, we present a novel framework for detecting individual trees in densely sampled 3D Point cloud data acquired in urban areas. Given a 3D Point cloud, the objective is to assign Point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a Binary Point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D Points, whereby the labels are defined as “tree Points” and “other Points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree Points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D Points among which 17.6 % are labeled as “tree Points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h).
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detection segmentation and localization of individual trees from mms Point cloud data
GEOBIA 2016 : Solutions and Synergies, 2016Co-Authors: Martin Weinmann, Clement Mallet, Mathieu BredifAbstract:In this paper, we address the extraction of objects from 3D Point clouds acquired with mobile mapping systems. More specifically, we focus on the detection of tree-like objects, a subsequent segmentation of individual trees and a localization of the respective trees. Thereby, the detection of tree-like objects is achieved via a Binary Point-wise classification based on geometric features, which categorizes each Point of the 3D Point cloud into either tree-like objects or non-tree-like objects. The subsequent segmentation and localization of individual trees is carried out by applying a 2D projection and a mean shift segmentation on a downsampled version of that part of the original 3D Point cloud which represents all tree-like objects, and it also involves a segment-based shape analysis to only retain plausible tree segments. We demonstrate the performance of our framework on a benchmark dataset which contains 10:13M 3D Points and has been acquired with a mobile mapping system in the city of Delft in the Netherlands.
Clement Mallet - One of the best experts on this subject based on the ideXlab platform.
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a classification segmentation framework for the detection of individual trees in dense mms Point cloud data acquired in urban areas
Remote Sensing, 2017Co-Authors: Martin Weinmann, Clement Mallet, Michael Weinmann, Mathieu BredifAbstract:In this paper, we present a novel framework for detecting individual trees in densely sampled 3D Point cloud data acquired in urban areas. Given a 3D Point cloud, the objective is to assign Point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a Binary Point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D Points, whereby the labels are defined as “tree Points” and “other Points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree Points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D Points among which 17.6 % are labeled as “tree Points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h).
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detection segmentation and localization of individual trees from mms Point cloud data
GEOBIA 2016 : Solutions and Synergies, 2016Co-Authors: Martin Weinmann, Clement Mallet, Mathieu BredifAbstract:In this paper, we address the extraction of objects from 3D Point clouds acquired with mobile mapping systems. More specifically, we focus on the detection of tree-like objects, a subsequent segmentation of individual trees and a localization of the respective trees. Thereby, the detection of tree-like objects is achieved via a Binary Point-wise classification based on geometric features, which categorizes each Point of the 3D Point cloud into either tree-like objects or non-tree-like objects. The subsequent segmentation and localization of individual trees is carried out by applying a 2D projection and a mean shift segmentation on a downsampled version of that part of the original 3D Point cloud which represents all tree-like objects, and it also involves a segment-based shape analysis to only retain plausible tree segments. We demonstrate the performance of our framework on a benchmark dataset which contains 10:13M 3D Points and has been acquired with a mobile mapping system in the city of Delft in the Netherlands.