11,870 research outputs found
Impact of lens distrortions on strain measurements obtained with digital image correlation
The determination of strain fields based on displacements obtained via DIC at the micro-strain level is still a cumbersome task. In particular when high-strain gradients are involved, e.g. in composite materials with multidirectional fibre reinforcement, uncertainties in the experimental setup and errors in the derivation of the displacement fields can substantially hamper the strain identification process. In this contribution, the aim is to investigate the impact of lens distortions on strain measurements. To this purpose, we first perform pure rigid body motion experiments, revealing the importance of precise correction of lens distortions. Next, a uni-axial tensile test on a textile composite with spatially varying high strain gradients is performed, resulting in very accurate determined strains along the fibers of the materia
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning process. As a consequence, although current deep learning
approaches show superior performance when considering quantitative benchmark
results, traditional approaches are still dominant in achieving high
qualitative results. In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by deep learning
capabilities, (2) considers a physics-based reflection model to steer the
learning process, and (3) exploits the traditional approach to obtain intrinsic
images by exploiting reflectance and shading gradient information. The proposed
model is fast to compute and allows for the integration of all intrinsic
components. To train the new model, an object centered large-scale datasets
with intrinsic ground-truth images are created. The evaluation results
demonstrate that the new model outperforms existing methods. Visual inspection
shows that the image formation loss function augments color reproduction and
the use of gradient information produces sharper edges. Datasets, models and
higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.Comment: CVPR 201
Accurate Feature Extraction and Control Point Correction for Camera Calibration with a Mono-Plane Target
The paper addresses two problems related to 3D camera calibration using a single mono-plane calibration target with circular control marks. The first problem is how to compute accurately the locations of the features (ellipses) in images of the target. Since the structure of the control marks is known beforehand, we propose to use a shape-specific searching technique to find the optimal locations of the features. Our experiments have shown this technique generates more accurate feature locations than the state-of-the-art ellipse extraction methods. The second problem is how to refine the control mark locations with unknown manufacturing errors. We demonstrate in a case study, where the control marks are laser printed on a A4 paper, that the manufacturing errors of the control marks can be compensated to a good extent so that the remaining calibration errors are reduced significantly. 1
Flash Photography Enhancement via Intrinsic Relighting
We enhance photographs shot in dark environments by combining a picture taken with the available light and one taken with the flash. We preserve the ambiance of the original lighting and insert the sharpness from the flash image. We use the bilateral filter to decompose the images into detail and large scale. We reconstruct the image using the large scale of the available lighting and the detail of the flash. We detect and correct flash shadows. This combines the advantages of available illumination and flash photography.Singapore-MIT Alliance (SMA
Navigation without localisation: reliable teach and repeat based on the convergence theorem
We present a novel concept for teach-and-repeat visual navigation. The
proposed concept is based on a mathematical model, which indicates that in
teach-and-repeat navigation scenarios, mobile robots do not need to perform
explicit localisation. Rather than that, a mobile robot which repeats a
previously taught path can simply `replay' the learned velocities, while using
its camera information only to correct its heading relative to the intended
path. To support our claim, we establish a position error model of a robot,
which traverses a taught path by only correcting its heading. Then, we outline
a mathematical proof which shows that this position error does not diverge over
time. Based on the insights from the model, we present a simple monocular
teach-and-repeat navigation method. The method is computationally efficient, it
does not require camera calibration, and it can learn and autonomously traverse
arbitrarily-shaped paths. In a series of experiments, we demonstrate that the
method can reliably guide mobile robots in realistic indoor and outdoor
conditions, and can cope with imperfect odometry, landmark deficiency,
illumination variations and naturally-occurring environment changes.
Furthermore, we provide the navigation system and the datasets gathered at
http://www.github.com/gestom/stroll_bearnav.Comment: The paper will be presented at IROS 2018 in Madri
Helicopter flights with night-vision goggles: Human factors aspects
Night-vision goggles (NVGs) and, in particular, the advanced, helmet-mounted Aviators Night-Vision-Imaging System (ANVIS) allows helicopter pilots to perform low-level flight at night. It consists of light intensifier tubes which amplify low-intensity ambient illumination (star and moon light) and an optical system which together produce a bright image of the scene. However, these NVGs do not turn night into day, and, while they may often provide significant advantages over unaided night flight, they may also result in visual fatigue, high workload, and safety hazards. These problems reflect both system limitations and human-factors issues. A brief description of the technical characteristics of NVGs and of human night-vision capabilities is followed by a description and analysis of specific perceptual problems which occur with the use of NVGs in flight. Some of the issues addressed include: limitations imposed by a restricted field of view; problems related to binocular rivalry; the consequences of inappropriate focusing of the eye; the effects of ambient illumination levels and of various types of terrain on image quality; difficulties in distance and slope estimation; effects of dazzling; and visual fatigue and superimposed symbology. These issues are described and analyzed in terms of their possible consequences on helicopter pilot performance. The additional influence of individual differences among pilots is emphasized. Thermal imaging systems (forward looking infrared (FLIR)) are described briefly and compared to light intensifier systems (NVGs). Many of the phenomena which are described are not readily understood. More research is required to better understand the human-factors problems created by the use of NVGs and other night-vision aids, to enhance system design, and to improve training methods and simulation techniques
- âŠ