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A Statistically Correct Estimation Of Epipolar Geometry

A Statistically Correct Estimation Of Epipolar GeometryBy: Stas Rozenfeld, Ilan Shimshoni and Michael Lindenbaum

The fundamental matrix is an essential tool for characterizing the relative geometry of two cameras. The matrix should be consistent with the image data and, at the same time, satisfy a singularity constraint. The fundamental matrix is usually estimated by means of an initial solution, which is calculated from the image data and then modified to satisfy the singularity constraint, by zeroing its smallest singular value. This approach, however, produces suboptimal results, especially when the amount of image data is small. This is the case when the matrix is estimated within a RANSAC process. We argue that this deficiency is due to (implicit) incorrect statistical modeling, which we rectify in this paper. The proposed method propagates the image noise distribution to a distribution on the initial solutions. It then uses this distribution to find a maximum likelihood solution which satisfies the constraint. The possibility of such a method has been considered in the literature, but was assumed to be inefficient in the RANSAC context. The same general technique can be applied to produce an improved estimate of a large class of models. In this work, it was also applied to estimating the essential matrix, which must satisfy an additional constraint. Our experiments show greater accuracy, at a modest computational cost. When used in the context of a RANSAC procedure, this accuracy enhancement leads to faster algorithms.

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Vesicles And Amoebae: On Globally Constrained Shape Deformations

Vesicles And Amoebae: On Globally Constrained Shape DeformationsBy: I. Goldin,   J. M. Delosme,   A. M. Bruckstein

Modeling the deformation of shapes under constraints on both perimeter and area is a challenging task due to the highly nontrivial interaction between the need for °exible local rules for manipulating the boundary and the global constraints. We propose several methods to address this problem and generate "random walks" in the space of shapes obeying quite general possibly time varying constraints on their perimeter and area. Design of perimeter and area preserving deformations are an interesting and useful special case of this problem.

 

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Visual Tracking Of Object Silhouettes

Visual Tracking Of Object SilhouettesBy: Guy Boudoukh, Ido Leichter and Ehud Rivlin

To appear in 2009 IEEE International Conference on Image Processing

In this research we propose a new method that addresses the problem of visually tracking the bitmap (silhouette) of an object in a video under very general conditions. We assume a general target, possibly non rigid, with no prior information except initialization. The target, as well as the background, may change its appearance over time and the camera may move arbitrarily. The proposed algorithm fuses different visual cues by means of a conditional random field probabilistic model. The target's bitmap is estimated every frame by incorporating temporal color similarity, spatial color continuity and spatial motion continuity into an energy function that is minimized via min-cut. The spatial motion continuity is incorporated in the energy function in multiple image resolutions by a novel multi-scale energy term. Compared to other methods that calculate optical flow for the whole image, the algorithm complexity is reduced when the optical flow calculation is done only at specific feature points. Experimental results show that our method outperforms other algorithms that address the problem of tracking under general conditions. Experiments on a variety of video clips demonstrate the robustness and effectiveness of our method to track an object under very general conditions.

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Fragtrack - Robust Fragments-Based Tracking Using The Integral Histogram

Fragtrack - Robust Fragments-Based Tracking Using The Integral HistogramBy: Amit Adam, Ehud Rivlin and Ilan Shimshoni

In this work we apply a recognition-by-parts approach to object tracking. The template object is represented by multiple image fragments or patches.
The patches are arbitrary and are not based on an object model (in contrast with traditional use of model-based parts e.g. limbs and torso in human tracking). Every patch votes on the possible positions and scales of the object in the current frame, by comparing its histogram with the corresponding image patch histogram. We then minimize a robust statistic in order to combine the vote maps of the multiple patches. 

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Tracking By Affine Kernel Transformations

Tracking By Affine Kernel TransformationsBy: Ido Leichter, Michael Lindenbaum and Ehud Rivlin

Kernel-based trackers aggregate image features within the support of a kernel (a mask) regardless of their spatial structure. These trackers spatially fit the kernel (usually in location and in scale) such that a function of the aggregate is optimized.We propose a kernel-based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary. The tracker estimates the best affinity of a spatially aligned pair of kernels, one of which is color-related and the other of which is object boundary-related. In a sense, this work extends previous kernel-based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled. These two extensions make for more precise target localization. Moreover, a more accurately localized target facilitates safer updating of its reference color model, further enhancing the tracker’s robustness. The improved tracking is demonstrated for several challenging image sequences.

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