10,519 research outputs found
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
Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
It is well known that the accuracy of a calibration depends strongly on the
choice of camera poses from which images of a calibration object are acquired.
We present a system -- Calibration Wizard -- that interactively guides a user
towards taking optimal calibration images. For each new image to be taken, the
system computes, from all previously acquired images, the pose that leads to
the globally maximum reduction of expected uncertainty on intrinsic parameters
and then guides the user towards that pose. We also show how to incorporate
uncertainty in corner point position in a novel principled manner, for both,
calibration and computation of the next best pose. Synthetic and real-world
experiments are performed to demonstrate the effectiveness of Calibration
Wizard.Comment: Oral presentation at ICCV 201
3D Scanning System for Automatic High-Resolution Plant Phenotyping
Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing
structures make plants difficult for three-dimensional (3D) scanning and
reconstruction -- two critical steps in automated visual phenotyping. Many
current solutions such as laser scanning, structured light, and multiview
stereo can struggle to acquire usable 3D models because of limitations in
scanning resolution and calibration accuracy. In response, we have developed a
fast, low-cost, 3D scanning platform to image plants on a rotating stage with
two tilting DSLR cameras centred on the plant. This uses new methods of camera
calibration and background removal to achieve high-accuracy 3D reconstruction.
We assessed the system's accuracy using a 3D visual hull reconstruction
algorithm applied on 2 plastic models of dicotyledonous plants, 2 sorghum
plants and 2 wheat plants across different sets of tilt angles. Scan times
ranged from 3 minutes (to capture 72 images using 2 tilt angles), to 30 minutes
(to capture 360 images using 10 tilt angles). The leaf lengths, widths, areas
and perimeters of the plastic models were measured manually and compared to
measurements from the scanning system: results were within 3-4% of each other.
The 3D reconstructions obtained with the scanning system show excellent
geometric agreement with all six plant specimens, even plants with thin leaves
and fine stems.Comment: 8 papes, DICTA 201
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
Estimating Epipolar Geometry With The Use of a Camera Mounted Orientation Sensor
Context: Image processing and computer vision are rapidly becoming more and more commonplace, and the amount of information about a scene, such as 3D geometry, that can be obtained from an image, or multiple images of the scene is steadily increasing due to increasing resolutions and availability of imaging sensors, and an active research community. In parallel, advances in hardware design and manufacturing are allowing for devices such as gyroscopes, accelerometers and magnetometers and GPS receivers to be included alongside imaging devices at a consumer level.
Aims: This work aims to investigate the use of orientation sensors in the field of computer vision as sources of data to aid with image processing and the determination of a scene’s geometry, in particular, the epipolar geometry of a pair of images - and devises a hybrid methodology from two sets of previous works in order to exploit the information available from orientation sensors alongside data gathered from image processing techniques.
Method: A readily available consumer-level orientation sensor was used alongside a digital camera to capture images of a set of scenes and record the orientation of the camera. The fundamental matrix of these pairs of images was calculated using a variety of techniques - both incorporating data from the orientation sensor and excluding its use
Results: Some methodologies could not produce an acceptable result for the Fundamental Matrix on certain image pairs, however, a method described in the literature that used an orientation sensor always produced a result - however in cases where the hybrid or purely computer vision methods also produced a result - this was found to be the least accurate.
Conclusion: Results from this work show that the use of an orientation sensor to capture information alongside an imaging device can be used to improve both the accuracy and reliability of calculations of the scene’s geometry - however noise from the orientation sensor can limit this accuracy and further research would be needed to determine the magnitude of this problem and methods of mitigation
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