Biomedical subjects

Gérard Medioni

Publications and source records attributed to Gérard Medioni.

5 recordsLinked to original sources

PubMed2006

Stereo using monocular cues within the tensor voting framework.

We address the fundamental problem of matching in two static images. The remaining challenges are related to occlusion and lack of texture. Our approach addresses these difficulties within a perceptual organization framework, considering both binocular and monocular cues. Initially, matching candidates for all pixels are generated by a combination of matching techniques. The matching candidates are then embedded in disparity space, where perceptual organization takes place in 3D neighborhoods and, thus, does not suffer from problems associated with scanline or image neighborhoods. The assumption is that correct matches produce salient, coherent surfaces, while wrong ones do not. Matching candidates that are consistent with the surfaces are kept and grouped into smooth layers. Thus, we achieve surface segmentation based on geometric and not photometric properties. Surface overextensions, which are due to occlusion, can be corrected by removing matches whose projections are not consistent in color with their neighbors of the same surface in both images. Finally, the projections of the refined surfaces on both images are used to obtain disparity hypotheses for unmatched pixels. The final disparities are selected after a second tensor voting stage, during which information is propagated from more reliable pixels to less reliable ones. We present results on widely used benchmark stereo pairs.

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PubMed2005

Parametric reconstruction of generalized cylinders from limb edges.

The three-dimensional (3-D) reconstruction of generalized cylinders (GCs) is an important research field in computer vision. One of the main difficulties is that some contour features in images cannot be reconstructed by traditional stereovision because they do not correspond to reflectance discontinuities of surface in space. In this paper, we present a novel, parametric approach for the 3-D reconstruction of circular generalized cylinders (CGCs) only from the limb edges of CGCs in two images. Instead of exploiting the invariant and quasiinvariant properties of some specific subclasses of GCs in projections, our reconstruction is achieved by some general assumptions on GCs, and can, therefore, be applied to a broader subclass of GCs. In order to improve robustness, we perform the extraction and labeling of the limb edge interactively, and estimate the epipolar geometry between two images by an optimal algorithm. Then, for different types of GCs, three kinds of symmetries (parallel symmetry, skew symmetry, and local smooth symmetry) are employed to compute the symmetry of limb edges. The surface points corresponding to limb edges in images are reconstructed by integrating the recovered epipolar geometry and the properties induced from the assumptions that we make on the GCs. Finally, a homography-based method is exploited to further refine the 3-D description of the GC with a coplanar curved axis.

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PubMed2005

A voting-based computational framework for visual motion analysis and interpretation.

Most approaches for motion analysis and interpretation rely on restrictive parametric models and involve iterative methods which depend heavily on initial conditions and are subject to instability. Further difficulties are encountered in image regions where motion is not smooth-typically around motion boundaries. This work addresses the problem of visual motion analysis and interpretation by formulating it as an inference of motion layers from a noisy and possibly sparse point set in a 4D space. The core of the method is based on a layered 4D representation of data and a voting scheme for affinity propagation. The inherent problem caused by the ambiguity of 2D to 3D interpretation is usually handled by adding additional constraints, such as rigidity. However, enforcing such a global constraint has been problematic in the combined presence of noise and multiple independent motions. By decoupling the processes of matching, outlier rejection, segmentation, and interpretation, we extract accurate motion layers based on the smoothness of image motion, then locally enforce rigidity for each layer in order to infer its 3D structure and motion. The proposed framework is noniterative and consistently handles both smooth moving regions and motion discontinuities without using any prior knowledge of the motion model.

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PubMed2004

First order augmentation to tensor voting for boundary inference and multiscale analysis in 3D.

Most computer vision applications require the reliable detection of boundaries. In the presence of outliers, missing data, orientation discontinuities, and occlusion, this problem is particularly challenging. We propose to address it by complementing the tensor voting framework, which was limited to second order properties, with first order representation and voting. First order voting fields and a mechanism to vote for 3D surface and volume boundaries and curve endpoints in 3D are defined. Boundary inference is also useful for a second difficult problem in grouping, namely, automatic scale selection. We propose an algorithm that automatically infers the smallest scale that can preserve the finest details. Our algorithm then proceeds with progressively larger scales to ensure continuity where it has not been achieved. Therefore, the proposed approach does not oversmooth features or delay the handling of boundaries and discontinuities until model misfit occurs. The interaction of smooth features, boundaries, and outliers is accommodated by the unified representation, making possible the perceptual organization of data in curves, surfaces, volumes, and their boundaries simultaneously. We present results on a variety of data sets to show the efficacy of the improved formalism.

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PubMed2004

Simultaneous two-view epipolar geometry estimation and motion segmentation by 4D tensor voting.

We address the problem of simultaneous two-view epipolar geometry estimation and motion segmentation from nonstatic scenes. Given a set of noisy image pairs containing matches of n objects, we propose an unconventional, efficient, and robust method, 4D tensor voting, for estimating the unknown n epipolar geometries, and segmenting the static and motion matching pairs into n independent motions. By considering the 4D isotropic and orthogonal joint image space, only two tensor voting passes are needed, and a very high noise to signal ratio (up to five) can be tolerated. Epipolar geometries corresponding to multiple, rigid motions are extracted in succession. Only two uncalibrated frames are needed, and no simplifying assumption (such as affine camera model or homographic model between images) other than the pin-hole camera model is made. Our novel approach consists of propagating a local geometric smoothness constraint in the 4D joint image space, followed by global consistency enforcement for extracting the fundamental matrices corresponding to independent motions. We have performed extensive experiments to compare our method with some representative algorithms to show that better performance on nonstatic scenes are achieved. Results on challenging data sets are presented.

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