The automated detection of humans in computervision as well as the realistic rendering of people in computer graphics necessitates improved modeling of the human skin color. In this paper we describe the acquisition ...
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ISBN:
(纸本)0769512720
The automated detection of humans in computervision as well as the realistic rendering of people in computer graphics necessitates improved modeling of the human skin color. In this paper we describe the acquisition and modeling of skin reflectance data densely sampled over the entire visible spectrum. The data collected through a spectrograph allows us to explain skin color (and its variations) and to discriminate between human skin and dyes designed to mimic human skin. We study the approximation of these data using several sets of basis functions. Our study shows that skin reflectance data can best be approximated by a linear combination of Gaussians or their first derivatives. This result has a significant practical impact on optical acquisition devices: the entire visible spectrum of skin reflectance can now be captured with a few filters of optimally chosen central wavelengths and bandwidth.
A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion err-os, inaccurate blur models, noise, moving objects, motion blur etc....
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ISBN:
(纸本)0769512720
A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion err-os, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolution algorithm. This process can increase resolution even in regions with outliers, where other super. resolution methods actually degrade the image.
The "direct methods" achieve global image registration without explicit knowledge of feature correspondences. We employ the motion gradient constraint as the relation between the motion parameters and the me...
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ISBN:
(纸本)0769512720
The "direct methods" achieve global image registration without explicit knowledge of feature correspondences. We employ the motion gradient constraint as the relation between the motion parameters and the measured image gradients. While this relation appears as a linear system of equations, for any motion model (other than a translation) we show that the underlying noise process is data-dependent, i.e., heteroscedastic, a fact which must be taken into account in the parameter estimation process. The improvement obtained using the adequate procedure is confirmed for the 2D rigid motion model through comparison with the traditional total least square approach.
We derive single view indexing functions for dynamic scenes - where dynamic is defined as a scene consisting of multiply moving points each moving independently with constant velocity. The indexing functions we derive...
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ISBN:
(纸本)0769512720
We derive single view indexing functions for dynamic scenes - where dynamic is defined as a scene consisting of multiply moving points each moving independently with constant velocity. The indexing functions we derive are view independent and form a generalization of the "shape tensors" associated with rigid scenes by introducing a time-varying parameter We derive those indexing functions under full 3D projective, 3D affine, and various reduced configurations. The indexing functions were implemented and tested for matching against objects for which their non-rigid motion is an intrinsic part of their character - human gait recognition and hand gesture identification are the two chosen application examples.
Isometric surfaces share the same geometric structure also known as the 'first fundamental form'. For example, all possible bending of a given surface, that include all length preserving deformations without. ...
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ISBN:
(纸本)0769512720
Isometric surfaces share the same geometric structure also known as the 'first fundamental form'. For example, all possible bending of a given surface, that include all length preserving deformations without. tearing or stretching the surface, are considered to be isometric. We present a method to construct a bending invariant canonical form for such surfaces. This invariant representation is an embedding of the intrinsic geodesic structure of the surface in a finite dimensional Euclidean space, in which geodesic distances are approximated by Euclidean ones. The canonical representation is constructed by first measuring the inter geodesic distances between points on the surfaces. Next, multi-dimensional scaling (MDS) techniques are applied to extract a finite dimensional flat space in which geodesic distances are represented as Euclidean ones. The geodesic distances are measured by the efficient fast marching on triangulated domains' numerical algorithm. Applying this transform to various objects with similar geodesic structures (similar first fundamental form) maps isometric objects into similar canonical forms. We show a simple surface classification method based on the bending invariant canonical form.
A novel robust method for outlier detection in structure and motion recovery for affine cameras is presented. It is an extension of the well-known Tomasi-Kanade factorization technique designed to handle outliers. It ...
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ISBN:
(纸本)0769512720
A novel robust method for outlier detection in structure and motion recovery for affine cameras is presented. It is an extension of the well-known Tomasi-Kanade factorization technique designed to handle outliers. It can also be seen as an importation of the LMedS technique or RANSAC into the factorization framework. Based on the computation of distances between subspaces, it relates closely. with the subspace-based factorization methods for the perspective case presented by Sparr and others and the subspace-based factorization for affine cameras with missing data by Jacobs. Key features of the method presented here are its ability to compare different subspaces and the complete automation of the detection and elimination of outliers. Its performance and effectiveness are demonstrated by experiments involving simulated and real video sequences.
In area-based stereo matching, there is a problem called "boundary overreach", i.e. the recovered object boundary turns out to be wrongly located away from the real one. This is especially harmful to segment...
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ISBN:
(纸本)0769512720
In area-based stereo matching, there is a problem called "boundary overreach", i.e. the recovered object boundary turns out to be wrongly located away from the real one. This is especially harmful to segmenting objects using depth information. A few approaches have been proposed to solve this problem. However, these techniques tend to degrade on smooth surfaces. That is, there seems to be a trade-off problem between recovering precise object edges and obtaining smooth surfaces. In this paper, we propose a new simple method to solve this problem. Using multiple stereo pairs and multiple windowing, our method detects the region where the boundary overreach is likely to occur (let us call it "BO region") and adopts appropriate methods for the BO and non-BO regions. Although the proposed method is quite simple, the experimental results have shown that it is very effective at recovering both sharp object edges at their correct locations and smooth object surfaces.
Since image-based servoing is a local control solution, it requires the definition of intermediate subgoals in the sensor space when the initial robot position is far away from the desired one. This paper addresses th...
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ISBN:
(纸本)0769512720
Since image-based servoing is a local control solution, it requires the definition of intermediate subgoals in the sensor space when the initial robot position is far away from the desired one. This paper addresses the problem of generating and tracking realistic and optimal smooth trajectories of complex features in the image space. The model of the observed target and the internal camera parameters are assumed to be unknown. First a closed-form smooth collineation path (related to a reference plane) between given starts and end-points is obtained. This path is generated in order to correspond to an optimal camera path. The trajectories of the image features (corresponding to points belonging or not to the reference plane) are then derived and tracked using an image based control.
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