3,189 research outputs found
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
We present a novel approach for vanishing point detection from uncalibrated
monocular images. In contrast to state-of-the-art, we make no a priori
assumptions about the observed scene. Our method is based on a convolutional
neural network (CNN) which does not use natural images, but a Gaussian sphere
representation arising from an inverse gnomonic projection of lines detected in
an image. This allows us to rely on synthetic data for training, eliminating
the need for labelled images. Our method achieves competitive performance on
three horizon estimation benchmark datasets. We further highlight some
additional use cases for which our vanishing point detection algorithm can be
used.Comment: Accepted for publication at German Conference on Pattern Recognition
(GCPR) 2017. This research was supported by German Research Foundation DFG
within Priority Research Programme 1894 "Volunteered Geographic Information:
Interpretation, Visualisation and Social Computing
The toulouse vanishing points dataset
International audienceIn this paper we present the Toulouse Vanishing Points Dataset, a public photographs database of Manhattan scenes taken with an iPad Air 1. The purpose of this dataset is the evaluation of vanishing points estimation algorithms. Its originality is the addition of Inertial Measurement Unit (IMU) data synchronized with the camera under the form of rotation matrices. Moreover, contrary to existing works which provide vanishing points of reference in the form of single points, we computed uncertainty regions. The Toulouse Vanishing Points Dataset is publicly available at http://ubee.enseeiht.fr/tvp
Automatic Detection of Calibration Grids in Time-of-Flight Images
It is convenient to calibrate time-of-flight cameras by established methods,
using images of a chequerboard pattern. The low resolution of the amplitude
image, however, makes it difficult to detect the board reliably. Heuristic
detection methods, based on connected image-components, perform very poorly on
this data. An alternative, geometrically-principled method is introduced here,
based on the Hough transform. The projection of a chequerboard is represented
by two pencils of lines, which are identified as oriented clusters in the
gradient-data of the image. A projective Hough transform is applied to each of
the two clusters, in axis-aligned coordinates. The range of each transform is
properly bounded, because the corresponding gradient vectors are approximately
parallel. Each of the two transforms contains a series of collinear peaks; one
for every line in the given pencil. This pattern is easily detected, by
sweeping a dual line through the transform. The proposed Hough-based method is
compared to the standard OpenCV detection routine, by application to several
hundred time-of-flight images. It is shown that the new method detects
significantly more calibration boards, over a greater variety of poses, without
any overall loss of accuracy. This conclusion is based on an analysis of both
geometric and photometric error.Comment: 11 pages, 11 figures, 1 tabl
Real-time robust estimation of vanishing points through nonlinear optimization
Vanishing points are elements of great interest in the computer vision field, since they are the main source of information about the geometry of the scene and the projection process associated to the camera. They have been studied and applied during decades for plane rectification, 3D reconstruction, and mainly auto-calibration tasks. Nevertheless, the literature lacks accurate online solutions for multiple vanishing point estimation. Most strategies focalize on the accuracy, using highly computational demanding iterative procedures. We propose a novel strategy for multiple vanishing point estimation that finds a trade-off between accuracy and efficiency, being able to operate in real time for video sequences. This strategy takes advantage of the temporal coherence of the images of the sequences to reduce the computational load of the processing algorithms while keeping a high level of accuracy due to an optimization process. The key element of the approach is a robust scheme based on the MLESAC algorithm, which is used in a similar way to the EM algorithm. This approach ensures robust and accurate estimations, since we use the MLESAC in combination with a novel error function, based on the angular error between the vanishing point and the image features. To increase the speed of the MLESAC algorithm, the selection of the minimal sample sets is substituted by a random sampling step that takes into account temporal information to provide better initializations. Besides, for the sake of flexibility, the proposed error function has been designed to work using as image features indiscriminately gradient-pixels or line segments. Hence, we increase the range of applications in which our approach can be used, according to the type of information that is available. The results show a real-time system that delivers real-time accurate estimations of multiple vanishing points for online processing, tested in moving camera video sequences of structured scenarios, both indoors and outdoors, such as rooms, corridors, facades, roads, etc
A vision-based fully automated approach to robust image cropping detection
The definition of valid and robust methodologies for assessing the authenticity of digital information is nowadays critical to contrast social manipulation through the media. A key research topic in multimedia forensics is the development of methods for detecting tampered content in large image collections without any human intervention. This paper introduces AMARCORD (Automatic Manhattan-scene AsymmetRically CrOpped imageRy Detector), a fully automated detector for exposing evidences of asymmetrical image cropping on Manhattan-World scenes. The proposed solution estimates and exploits the camera principal point, i.e., a physical feature extracted directly from the image content that is quite insensitive to image processing operations, such as compression and resizing, typical of social media platforms. Robust computer vision techniques are employed throughout, so as to cope with large sources of noise in the data and improve detection performance. The method leverages a novel metric based on robust statistics, and is also capable to decide autonomously whether the image at hand is tractable or not. The results of an extensive experimental evaluation covering several cropping scenarios demonstrate the effectiveness and robustness of our approac
Coupling Vanishing Point Tracking with Inertial Navigation to Estimate Attitude in a Structured Environment
This research aims to obtain accurate and stable estimates of a vehicle\u27s attitude by coupling consumer-grade inertial and optical sensors. This goal is pursued by first modeling both inertial and optical sensors and then developing a technique for identifying vanishing points in perspective images of a structured environment. The inertial and optical processes are then coupled to enable each one to aid the other. The vanishing point measurements are combined with the inertial data in an extended Kalman filter to produce overall attitude estimates. This technique is experimentally demonstrated in an indoor corridor setting using a motion profile designed to simulate flight. Through comparison with a tactical-grade inertial sensor, the combined consumer-grade inertial and optical data are shown to produce a stable attitude solution accurate to within 1.5 degrees. A measurement bias is manifested which degrades the accuracy by up to another 2.5 degrees
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal
vanishing points, and the unknown focal length of the camera, leveraging a
prior vertical direction. The direction can come from an Inertial Measurement
Unit that is a standard component of recent consumer devices, e.g.,
smartphones. We provide an exhaustive analysis of minimal line configurations
and derive two new 2-line solvers, one of which does not suffer from
singularities affecting existing solvers. Additionally, we design a new
non-minimal method, running on an arbitrary number of lines, to boost the
performance in local optimization. Combining all solvers in a hybrid robust
estimator, our method achieves increased accuracy even with a rough prior.
Experiments on synthetic and real-world datasets demonstrate the superior
accuracy of our method compared to the state of the art, while having
comparable runtimes. We further demonstrate the applicability of our solvers
for relative rotation estimation. The code is available at
https://github.com/cvg/VP-Estimation-with-Prior-Gravity.Comment: Accepted at ICCV 202
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