3,487 research outputs found
Multi-Scale 3D Scene Flow from Binocular Stereo Sequences
Scene ïŹow methods estimate the three-dimensional motion ïŹeld for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene ïŹow estimation that provides reliable results using only two cameras by fusing stereo and optical ïŹow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical ïŹow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene ïŹow than previous methods allow. To handle the aperture problems inherent in the estimation of optical ïŹow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization â two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.National Science Foundation (CNS-0202067, IIS-0208876); Office of Naval Research (N00014-03-1-0108
A statistical model to describe invariants extracted from a 3-D quadric surface patch and its applications in region-based recognition
A statistical model, describing noise-disturbed invariants extracted from a surface patch of a range image, has been developed and applied to region based pose estimation and classification of 3D quadrics. The Mahalanobis distance, which yields the same results as a Baysian classifier, is used for the classification of the surface patches. The results, compared with the Euclidean distance, appear to be much more reliabl
Finding lumbar vertebrae by evidence gathering
Low back pain is a very common problem and lumbar segmental instability is one of the causes. It is essential to investigate lumbar spine movement in order to understand instability better and as an aid to diagnosis. Digital videofluoroscopy (DVF) provides a method of quantifying the motion of individual vertebra. In this paper, we apply a new version of the Hough transform (HT) to locate the lumbar vertebra automatically in DVF image sequences. At present, this algorithm has been applied to a calibration model and to the vertebra L3 in DVF images, and has shown to provide satisfactory results. Further work will concentrate on reducing the computational time for realtime application, on developing a spatiotemporal sequences method and on determining the spinal kinematics based on the extracted parameters
Curve Reconstruction via the Global Statistics of Natural Curves
Reconstructing the missing parts of a curve has been the subject of much
computational research, with applications in image inpainting, object
synthesis, etc. Different approaches for solving that problem are typically
based on processes that seek visually pleasing or perceptually plausible
completions. In this work we focus on reconstructing the underlying physically
likely shape by utilizing the global statistics of natural curves. More
specifically, we develop a reconstruction model that seeks the mean physical
curve for a given inducer configuration. This simple model is both
straightforward to compute and it is receptive to diverse additional
information, but it requires enough samples for all curve configurations, a
practical requirement that limits its effective utilization. To address this
practical issue we explore and exploit statistical geometrical properties of
natural curves, and in particular, we show that in many cases the mean curve is
scale invariant and oftentimes it is extensible. This, in turn, allows to boost
the number of examples and thus the robustness of the statistics and its
applicability. The reconstruction results are not only more physically
plausible but they also lead to important insights on the reconstruction
problem, including an elegant explanation why certain inducer configurations
are more likely to yield consistent perceptual completions than others.Comment: CVPR versio
A Framework for Symmetric Part Detection in Cluttered Scenes
The role of symmetry in computer vision has waxed and waned in importance
during the evolution of the field from its earliest days. At first figuring
prominently in support of bottom-up indexing, it fell out of favor as shape
gave way to appearance and recognition gave way to detection. With a strong
prior in the form of a target object, the role of the weaker priors offered by
perceptual grouping was greatly diminished. However, as the field returns to
the problem of recognition from a large database, the bottom-up recovery of the
parts that make up the objects in a cluttered scene is critical for their
recognition. The medial axis community has long exploited the ubiquitous
regularity of symmetry as a basis for the decomposition of a closed contour
into medial parts. However, today's recognition systems are faced with
cluttered scenes, and the assumption that a closed contour exists, i.e. that
figure-ground segmentation has been solved, renders much of the medial axis
community's work inapplicable. In this article, we review a computational
framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009,
2013), that bridges the representation power of the medial axis and the need to
recover and group an object's parts in a cluttered scene. Our framework is
rooted in the idea that a maximally inscribed disc, the building block of a
medial axis, can be modeled as a compact superpixel in the image. We evaluate
the method on images of cluttered scenes.Comment: 10 pages, 8 figure
3D Well-composed Polyhedral Complexes
A binary three-dimensional (3D) image is well-composed if the boundary
surface of its continuous analog is a 2D manifold. Since 3D images are not
often well-composed, there are several voxel-based methods ("repairing"
algorithms) for turning them into well-composed ones but these methods either
do not guarantee the topological equivalence between the original image and its
corresponding well-composed one or involve sub-sampling the whole image.
In this paper, we present a method to locally "repair" the cubical complex
(embedded in ) associated to to obtain a polyhedral
complex homotopy equivalent to such that the boundary of every
connected component of is a 2D manifold. The reparation is performed via
a new codification system for under the form of a 3D grayscale image
that allows an efficient access to cells and their faces
Shape-from-shading using the heat equation
This paper offers two new directions to shape-from-shading, namely the use of the heat equation to smooth the field of surface normals and the recovery of surface height using a low-dimensional embedding. Turning our attention to the first of these contributions, we pose the problem of surface normal recovery as that of solving the steady state heat equation subject to the hard constraint that Lambert's law is satisfied. We perform our analysis on a plane perpendicular to the light source direction, where the z component of the surface normal is equal to the normalized image brightness. The x - y or azimuthal component of the surface normal is found by computing the gradient of a scalar field that evolves with time subject to the heat equation. We solve the heat equation for the scalar potential and, hence, recover the azimuthal component of the surface normal from the average image brightness, making use of a simple finite difference method. The second contribution is to pose the problem of recovering the surface height function as that of embedding the field of surface normals on a manifold so as to preserve the pattern of surface height differences and the lattice footprint of the surface normals. We experiment with the resulting method on a variety of real-world image data, where it produces qualitatively good reconstructed surfaces
- âŠ