2,655 research outputs found
Geometric potential of cartosat-1 stereo imagery
Cartosat-1 satellite, launched by Department of Space (DOS), Government of India, is dedicated to stereo viewing for large scale mapping and terrain modelling applications. This stereo capability fills the limited capacity of very high resolution satellites for three-dimensional point determination and enables the generation of detailed digital elevation models (DEMs) not having gaps in mountainous regions like for example the SRTM height model.The Cartosat-1 sensor offers a resolution of 2.5m GSD in panchromatic mode. One CCD-line sensor camera is looking with a nadir angle of 26' in forward direction, the other 5' aft along the track. The Institute "Area di Geodesia e Geomatica"-Sapienza UniversitĂ di Roma and the Institute of Photogrammetry and Geoinformation, Leibniz University Hannover participated at the ISPRS-ISRO Cartosat-1 Scientific Assessment Programme (CSAP), in order to investigate the generation of Digital Surface Models (DSMs) from Cartosat-1 stereo scenes. The aim of this work concerns the orientation of Cartosat-1 stereo pairs, using the given RPCs improved by control points and the definition of an innovative model based on geometric reconstruction, that is used also for the RPC extraction utilizing a terrain independent approach. These models are implemented in the scientific software (SISAR-Software per Immagini Satellitari ad Alta Risoluzione) developed at Sapienza UniversitĂ di Roma. In this paper the SISAR model is applied to different stereo pairs (Castelgandolfo and Rome) and to point out the effectiveness of the new model, SISAR results are compared with the corresponding ones obtained by the software OrthoEngine 10.0 (PCI Geomatica).By the University of Hannover a similar general satellite orientation program has been developed and the good results, achieved by bias corrected sensor oriented RPCs, for the test fields Mausanne (France) and Warsaw (Poland) have been described.For some images, digital height models have been generated by automatic image matching with least squares method, analysed in relation to given reference height models. For the comparison with the reference DEMs the horizontal fit of the height models to each other has been checked by adjustment
Interactive Camera Network Design using a Virtual Reality Interface
Traditional literature on camera network design focuses on constructing
automated algorithms. These require problem specific input from experts in
order to produce their output. The nature of the required input is highly
unintuitive leading to an unpractical workflow for human operators. In this
work we focus on developing a virtual reality user interface allowing human
operators to manually design camera networks in an intuitive manner. From real
world practical examples we conclude that the camera networks designed using
this interface are highly competitive with, or superior to those generated by
automated algorithms, but the associated workflow is much more intuitive and
simple. The competitiveness of the human-generated camera networks is
remarkable because the structure of the optimization problem is a well known
combinatorial NP-hard problem. These results indicate that human operators can
be used in challenging geometrical combinatorial optimization problems given an
intuitive visualization of the problem.Comment: 11 pages, 8 figure
Sublabel-Accurate Relaxation of Nonconvex Energies
We propose a novel spatially continuous framework for convex relaxations
based on functional lifting. Our method can be interpreted as a
sublabel-accurate solution to multilabel problems. We show that previously
proposed functional lifting methods optimize an energy which is linear between
two labels and hence require (often infinitely) many labels for a faithful
approximation. In contrast, the proposed formulation is based on a piecewise
convex approximation and therefore needs far fewer labels. In comparison to
recent MRF-based approaches, our method is formulated in a spatially continuous
setting and shows less grid bias. Moreover, in a local sense, our formulation
is the tightest possible convex relaxation. It is easy to implement and allows
an efficient primal-dual optimization on GPUs. We show the effectiveness of our
approach on several computer vision problems
End-to-End Learning of Representations for Asynchronous Event-Based Data
Event cameras are vision sensors that record asynchronous streams of
per-pixel brightness changes, referred to as "events". They have appealing
advantages over frame-based cameras for computer vision, including high
temporal resolution, high dynamic range, and no motion blur. Due to the sparse,
non-uniform spatiotemporal layout of the event signal, pattern recognition
algorithms typically aggregate events into a grid-based representation and
subsequently process it by a standard vision pipeline, e.g., Convolutional
Neural Network (CNN). In this work, we introduce a general framework to convert
event streams into grid-based representations through a sequence of
differentiable operations. Our framework comes with two main advantages: (i)
allows learning the input event representation together with the task dedicated
network in an end to end manner, and (ii) lays out a taxonomy that unifies the
majority of extant event representations in the literature and identifies novel
ones. Empirically, we show that our approach to learning the event
representation end-to-end yields an improvement of approximately 12% on optical
flow estimation and object recognition over state-of-the-art methods.Comment: To appear at ICCV 201
Analysis and approximation of some Shape-from-Shading models for non-Lambertian surfaces
The reconstruction of a 3D object or a scene is a classical inverse problem
in Computer Vision. In the case of a single image this is called the
Shape-from-Shading (SfS) problem and it is known to be ill-posed even in a
simplified version like the vertical light source case. A huge number of works
deals with the orthographic SfS problem based on the Lambertian reflectance
model, the most common and simplest model which leads to an eikonal type
equation when the light source is on the vertical axis. In this paper we want
to study non-Lambertian models since they are more realistic and suitable
whenever one has to deal with different kind of surfaces, rough or specular. We
will present a unified mathematical formulation of some popular orthographic
non-Lambertian models, considering vertical and oblique light directions as
well as different viewer positions. These models lead to more complex
stationary nonlinear partial differential equations of Hamilton-Jacobi type
which can be regarded as the generalization of the classical eikonal equation
corresponding to the Lambertian case. However, all the equations corresponding
to the models considered here (Oren-Nayar and Phong) have a similar structure
so we can look for weak solutions to this class in the viscosity solution
framework. Via this unified approach, we are able to develop a semi-Lagrangian
approximation scheme for the Oren-Nayar and the Phong model and to prove a
general convergence result. Numerical simulations on synthetic and real images
will illustrate the effectiveness of this approach and the main features of the
scheme, also comparing the results with previous results in the literature.Comment: Accepted version to Journal of Mathematical Imaging and Vision, 57
page
Robust 3-Dimensional Object Recognition using Stereo Vision and Geometric Hashing
We propose a technique that combines geometric hashing with stereo vision. The idea is to use the robustness of geometric hashing to spurious data to overcome the correspondence problem, while the stereo vision setup enables direct model matching using the 3-D object models. Furthermore, because the matching technique relies on the relative positions of local features, we should be able to perform robust recognition even with partially occluded objects. We tested this approach with simple geometric objects using a corner point detector. We successfully recognized objects even in scenes where the objects were partially occluded by other objects. For complicated scenes, however, the limited set of model features and required amount of computing time, sometimes became a proble
Influence of Stereoscopic Camera System Alignment Error on the Accuracy of 3D Reconstruction
The article deals with the influence of inaccurate rotation of cameras in camera system alignment on 3D reconstruction accuracy. The accuracy of the all three spatial coordinates is analyzed for two alignments (setups) of 3D cameras. In the first setup, a 3D system with parallel optical axes of the cameras is analyzed. In this stereoscopic setup, the deterministic relations are derived by the trigonometry and basic stereoscopic formulas. The second alignment is a generalized setup with cameras in arbitrary positions. The analysis of the situation in the general setup is closely related with the influence of errors of the points' correspondences. Therefore the relation between errors of points' correspondences and reconstruction of the spatial position of the point was investigated. This issue is very complex. The worst case analysis was executed with the use of Monte Carlo method. The aim is to estimate a critical situation and the possible extent of these errors. Analysis of the generalized system and derived relations for normal system represent a significant improvement of the spatial coordinates accuracy analysis. A practical experiment was executed which confirmed the proposed relations
Combinatorial Continuous Maximal Flows
Maximum flow (and minimum cut) algorithms have had a strong impact on
computer vision. In particular, graph cuts algorithms provide a mechanism for
the discrete optimization of an energy functional which has been used in a
variety of applications such as image segmentation, stereo, image stitching and
texture synthesis. Algorithms based on the classical formulation of max-flow
defined on a graph are known to exhibit metrication artefacts in the solution.
Therefore, a recent trend has been to instead employ a spatially continuous
maximum flow (or the dual min-cut problem) in these same applications to
produce solutions with no metrication errors. However, known fast continuous
max-flow algorithms have no stopping criteria or have not been proved to
converge. In this work, we revisit the continuous max-flow problem and show
that the analogous discrete formulation is different from the classical
max-flow problem. We then apply an appropriate combinatorial optimization
technique to this combinatorial continuous max-flow CCMF problem to find a
null-divergence solution that exhibits no metrication artefacts and may be
solved exactly by a fast, efficient algorithm with provable convergence.
Finally, by exhibiting the dual problem of our CCMF formulation, we clarify the
fact, already proved by Nozawa in the continuous setting, that the max-flow and
the total variation problems are not always equivalent.Comment: 26 page
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