2,730 research outputs found
A Variational Stereo Method for the Three-Dimensional Reconstruction of Ocean Waves
We develop a novel remote sensing technique for the observation of waves on the ocean surface. Our method infers the 3-D waveform and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are given by minimizers of a composite energy functional that combines a photometric matching term along with regularization terms involving the smoothness of the unknowns. The desired ocean surface shape and radiance are the solution of a system of coupled partial differential equations derived from the optimality conditions of the energy functional. The proposed method is naturally extended to study the spatiotemporal dynamics of ocean waves and applied to three sets of stereo video data. Statistical and spectral analysis are carried out. Our results provide evidence that the observed omnidirectional wavenumber spectrum S(k) decays as k-2.5 is in agreement with Zakharov's theory (1999). Furthermore, the 3-D spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents
Catadioptric system optimisation for omnidirectional Robocup MSL robots
Omnidirectional Robocup MSL robots often use catadioptric vision systems in order to enable 360Âș of field view. It comprises an upright camera facing a convex mirror, commonly spherical, parabolic or hyperbolic, that reflects the entire space around the robot. This technique is being used for more than a decade and in a similar way by most teams. Teams upgrade their cameras in order to obtain more and better information of the captured area in pixel quantity and quality, but a large image area outside the convex mirror is black and unusable. The same happens on the image centre where the robot shows itself. Some efficiency though, can be improved in this technique by the methods presented in this paper such as developing a new convex mirror and by repositioning the camera viewpoint. Using 3D modelling CAD/CAM software for the simulation and CNC lathe mirror construction, some results are presented and discussed
Vision-Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison
Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRC) global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data
Calibration by correlation using metric embedding from non-metric similarities
This paper presents a new intrinsic calibration method that allows us to calibrate a generic single-view point camera just
by waving it around. From the video sequence obtained while the camera undergoes random motion, we compute the pairwise time
correlation of the luminance signal for a subset of the pixels. We show that, if the camera undergoes a random uniform motion, then
the pairwise correlation of any pixels pair is a function of the distance between the pixel directions on the visual sphere. This leads to
formalizing calibration as a problem of metric embedding from non-metric measurements: we want to find the disposition of pixels on
the visual sphere from similarities that are an unknown function of the distances. This problem is a generalization of multidimensional
scaling (MDS) that has so far resisted a comprehensive observability analysis (can we reconstruct a metrically accurate embedding?)
and a solid generic solution (how to do so?). We show that the observability depends both on the local geometric properties (curvature)
as well as on the global topological properties (connectedness) of the target manifold. We show that, in contrast to the Euclidean case,
on the sphere we can recover the scale of the points distribution, therefore obtaining a metrically accurate solution from non-metric
measurements. We describe an algorithm that is robust across manifolds and can recover a metrically accurate solution when the metric
information is observable. We demonstrate the performance of the algorithm for several cameras (pin-hole, fish-eye, omnidirectional),
and we obtain results comparable to calibration using classical methods. Additional synthetic benchmarks show that the algorithm
performs as theoretically predicted for all corner cases of the observability analysis
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning
Recent work about synthetic indoor datasets from perspective views has shown
significant improvements of object detection results with Convolutional Neural
Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale
indoor dataset containing 100,000 high-resolution diversified fisheye images
with 14 classes. To this end, we create 3D virtual environments of living
rooms, different human characters and interior textures. Beside capturing
fisheye images from virtual environments we create annotations for semantic
segmentation, instance masks and bounding boxes for object detection tasks. We
compare our synthetic dataset to state of the art real-world datasets for
omnidirectional images. Based on MS COCO weights, we show that our dataset is
well suited for fine-tuning CNNs for object detection. Through a high
generalization of our models by means of image synthesis and domain
randomization, we reach an AP up to 0.84 for class person on High-Definition
Analytics dataset.Comment: Paper accepted in WACV 202
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