27,149 research outputs found
Aqueye+: a new ultrafast single photon counter for optical high time resolution astrophysics
Aqueye+ is a new ultrafast optical single photon counter, based on single
photon avalanche photodiodes (SPAD) and a 4-fold split-pupil concept. It is a
completely revisited version of its predecessor, Aqueye, successfully mounted
at the 182 cm Copernicus telescope in Asiago. Here we will present the new
technological features implemented on Aqueye+, namely a state of the art timing
system, a dedicated and optimized optical train, a high sensitivity and high
frame rate field camera and remote control, which will give Aqueye plus much
superior performances with respect to its predecessor, unparalleled by any
other existing fast photometer. The instrument will host also an optical
vorticity module to achieve high performance astronomical coronography and a
real time acquisition of atmospheric seeing unit. The present paper describes
the instrument and its first performances.Comment: Proceedings of the SPIE, Volume 9504, id. 95040C 14 pp. (2015
Development of robots and application to industrial processes
An algorithm is presented for using a robot system with a single camera to position in three-dimensional space a slender object for insertion into a hole; for example, an electrical pin-type termination into a connector hole. The algorithm relies on a control-configured end effector to achieve the required horizontal translations and rotational motion, and it does not require camera calibration. A force sensor in each fingertip is integrated with the vision system to allow the robot to teach itself new reference points when different connectors and pins are used. Variability in the grasped orientation and position of the pin can be accomodated with the sensor system. Performance tests show that the system is feasible. More work is needed to determine more precisely the effects of lighting levels and lighting direction
Fast Mojette Transform for Discrete Tomography
A new algorithm for reconstructing a two dimensional object from a set of one
dimensional projected views is presented that is both computationally exact and
experimentally practical. The algorithm has a computational complexity of O(n
log2 n) with n = N^2 for an NxN image, is robust in the presence of noise and
produces no artefacts in the reconstruction process, as is the case with
conventional tomographic methods. The reconstruction process is approximation
free because the object is assumed to be discrete and utilizes fully discrete
Radon transforms. Noise in the projection data can be suppressed further by
introducing redundancy in the reconstruction. The number of projections
required for exact reconstruction and the response to noise can be controlled
without comprising the digital nature of the algorithm. The digital projections
are those of the Mojette Transform, a form of discrete linogram. A simple
analytical mapping is developed that compacts these projections exactly into
symmetric periodic slices within the Discrete Fourier Transform. A new digital
angle set is constructed that allows the periodic slices to completely fill all
of the objects Discrete Fourier space. Techniques are proposed to acquire these
digital projections experimentally to enable fast and robust two dimensional
reconstructions.Comment: 22 pages, 13 figures, Submitted to Elsevier Signal Processin
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
A Novel Framework for Highlight Reflectance Transformation Imaging
We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa
Development of a Computer Vision-Based Three-Dimensional Reconstruction Method for Volume-Change Measurement of Unsaturated Soils during Triaxial Testing
Problems associated with unsaturated soils are ubiquitous in the U.S., where expansive and collapsible soils are some of the most widely distributed and costly geologic hazards. Solving these widespread geohazards requires a fundamental understanding of the constitutive behavior of unsaturated soils. In the past six decades, the suction-controlled triaxial test has been established as a standard approach to characterizing constitutive behavior for unsaturated soils. However, this type of test requires costly test equipment and time-consuming testing processes. To overcome these limitations, a photogrammetry-based method has been developed recently to measure the global and localized volume-changes of unsaturated soils during triaxial test. However, this method relies on software to detect coded targets, which often requires tedious manual correction of incorrectly coded target detection information. To address the limitation of the photogrammetry-based method, this study developed a photogrammetric computer vision-based approach for automatic target recognition and 3D reconstruction for volume-changes measurement of unsaturated soils in triaxial tests. Deep learning method was used to improve the accuracy and efficiency of coded target recognition. A photogrammetric computer vision method and ray tracing technique were then developed and validated to reconstruct the three-dimensional models of soil specimen
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
We introduce a method based on the deflectometry principle for the
reconstruction of specular objects exhibiting significant size and geometric
complexity. A key feature of our approach is the deployment of an Automatic
Virtual Environment (CAVE) as pattern generator. To unfold the full power of
this extraordinary experimental setup, an optical encoding scheme is developed
which accounts for the distinctive topology of the CAVE. Furthermore, we devise
an algorithm for detecting the object of interest in raw deflectometric images.
The segmented foreground is used for single-view reconstruction, the background
for estimation of the camera pose, necessary for calibrating the sensor system.
Experiments suggest a significant gain of coverage in single measurements
compared to previous methods. To facilitate research on specular surface
reconstruction, we will make our data set publicly available
CosmoDM and its application to Pan-STARRS data
The Cosmology Data Management system (CosmoDM) is an automated and flexible
data management system for the processing and calibration of data from optical
photometric surveys. It is designed to run on supercomputers and to minimize
disk I/O to enable scaling to very high throughput during periods of
reprocessing. It serves as an early prototype for one element of the
ground-based processing required by the Euclid mission and will also be
employed in the preparation of ground based data needed in the eROSITA X-ray
all sky survey mission. CosmoDM consists of two main pipelines. The first is
the single-epoch or detrending pipeline, which is used to carry out the
photometric and astrometric calibration of raw exposures. The second is the co-
addition pipeline, which combines the data from individual exposures into
deeper coadd images and science ready catalogs. A novel feature of CosmoDM is
that it uses a modified stack of As- tromatic software which can read and write
tile compressed images. Since 2011, CosmoDM has been used to process data from
the DECam, the CFHT MegaCam and the Pan-STARRS cameras. In this paper we shall
describe how processed Pan-STARRS data from CosmoDM has been used to optically
confirm and measure photometric redshifts of Planck-based Sunyaev-Zeldovich
effect selected cluster candidates.Comment: 11 pages, 4 figures. Proceedings of Precision Astronomy with Fully
Depleted CCDs Workshop (2014). Accepted for publication in JINS
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