2,431 research outputs found
On the Two-View Geometry of Unsynchronized Cameras
We present new methods for simultaneously estimating camera geometry and time
shift from video sequences from multiple unsynchronized cameras. Algorithms for
simultaneous computation of a fundamental matrix or a homography with unknown
time shift between images are developed. Our methods use minimal correspondence
sets (eight for fundamental matrix and four and a half for homography) and
therefore are suitable for robust estimation using RANSAC. Furthermore, we
present an iterative algorithm that extends the applicability on sequences
which are significantly unsynchronized, finding the correct time shift up to
several seconds. We evaluated the methods on synthetic and wide range of real
world datasets and the results show a broad applicability to the problem of
camera synchronization.Comment: 12 pages, 9 figures, Computer Vision and Pattern Recognition (CVPR)
201
Wireless Software Synchronization of Multiple Distributed Cameras
We present a method for precisely time-synchronizing the capture of image
sequences from a collection of smartphone cameras connected over WiFi. Our
method is entirely software-based, has only modest hardware requirements, and
achieves an accuracy of less than 250 microseconds on unmodified commodity
hardware. It does not use image content and synchronizes cameras prior to
capture. The algorithm operates in two stages. In the first stage, we designate
one device as the leader and synchronize each client device's clock to it by
estimating network delay. Once clocks are synchronized, the second stage
initiates continuous image streaming, estimates the relative phase of image
timestamps between each client and the leader, and shifts the streams into
alignment. We quantitatively validate our results on a multi-camera rig imaging
a high-precision LED array and qualitatively demonstrate significant
improvements to multi-view stereo depth estimation and stitching of dynamic
scenes. We release as open source 'libsoftwaresync', an Android implementation
of our system, to inspire new types of collective capture applications.Comment: Main: 9 pages, 10 figures. Supplemental: 3 pages, 5 figure
Spatio-temporal alignment of pedobarographic image sequences
O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).This paper presents a methodology to align plantar pressure image sequences simultaneously in time and space. The spatial position and orientation of a foot in a sequence are changed to match the foot represented in a second sequence. Simultaneously with the spatial alignment, the temporal scale of the first sequence is transformed with the aim of synchronizing the two input footsteps. Consequently, the spatial correspondence of the foot regions along the sequences as well as the temporal synchronizing is automatically attained, making the study easier and more straightforward. In terms of spatial alignment, the methodology can use one of four possible geometric transformation models: rigid, similarity, affine or projective. In the temporal alignment, a polynomial transformation up to the 4th degree can be adopted in order to model linear and curved time behaviors. Suitable geometric and temporal transformations are found by minimizing the mean squared error (MSE) between the input sequences. The methodology was tested on a set of real image sequences acquired from a common pedobarographic device. When used in experimental cases generated by applying geometric and temporal control transformations, the methodology revealed high accuracy. Additionally, the intra-subject alignment tests from real plantar pressure image sequences showed that the curved temporal models produced better MSE results (p<0.001) than the linear temporal model. This paper represents an important step forward in the alignment of pedobarographic image data, since previous methods can only be applied on static images
Formalization of the General Video Temporal Synchronization Problem
In this work, we present a theoretical formalization of the temporal synchronization problem and a method to temporally synchronize multiple stationary video cameras with overlapping views of the same scene. The method uses a two stage approach that first approximates the synchronization by tracking moving objects and identifying curvature points. The method then proceeds to refine the estimate using a consensus based matching heuristic to find frames that best agree with the pre-computed camera geometries from stationary background image features. By using the fundamental matrix and the trifocal tensor in the second refinement step, we improve the estimation of the first step and handle a broader more generic range of input scenarios and camera conditions. The method is relatively simple compared to current techniques and is no harder than feature tracking in stage one and computing accurate geometries in stage two. We also provide a robust method to assist synchronization in the presence of inaccurate geometry computation, and a theoretical limit on the accuracy that can be expected from any synchronization syste
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique
for the task of 3D keypoint prediction from a single depth scan or image. Our
key idea is to utilize the fact that predictions from different views of the
same or similar objects should be consistent with each other. Such view
consistency can provide effective regularization for keypoint prediction on
unlabeled instances. In addition, we introduce a geometric alignment term to
regularize predictions in the target domain. The resulting loss function can be
effectively optimized via alternating minimization. We demonstrate the
effectiveness of our approach on real datasets and present experimental results
showing that our approach is superior to state-of-the-art general-purpose
domain adaptation techniques.Comment: ECCV 201
Multiple View Geometry For Video Analysis And Post-production
Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
We present MultiBodySync, a novel, end-to-end trainable multi-body motion
segmentation and rigid registration framework for multiple input 3D point
clouds. The two non-trivial challenges posed by this multi-scan multibody
setting that we investigate are: (i) guaranteeing correspondence and
segmentation consistency across multiple input point clouds capturing different
spatial arrangements of bodies or body parts; and (ii) obtaining robust
motion-based rigid body segmentation applicable to novel object categories. We
propose an approach to address these issues that incorporates spectral
synchronization into an iterative deep declarative network, so as to
simultaneously recover consistent correspondences as well as motion
segmentation. At the same time, by explicitly disentangling the correspondence
and motion segmentation estimation modules, we achieve strong generalizability
across different object categories. Our extensive evaluations demonstrate that
our method is effective on various datasets ranging from rigid parts in
articulated objects to individually moving objects in a 3D scene, be it
single-view or full point clouds.Comment: Contact: huang-jh18mailstsinghuaeduc
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