Most illuminant estimation algorithms worked on point light sources or directional light sources. Little attempt has been made, however, to estimate area light sources. In this paper, we present a novel scheme that es...
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ISBN:
(纸本)0769521282
Most illuminant estimation algorithms worked on point light sources or directional light sources. Little attempt has been made, however, to estimate area light sources. In this paper, we present a novel scheme that estimates the size and location of multiple area light sources using a set of stereo images of a sphere with a shiny surface. The parameters of the area light source are estimated by a novel algorithm which minimizes the matching error between the corresponding specular patches. Experiments on real images show that our method is accurate and robust in estimating the parameters of the area light source.
Face recognition under unconstrained illuminations (FR/I) received extensive study because of the existence of illumination subspace. P. Belhumer et al. (1996) presented a study on the comparison between principal com...
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Face recognition under unconstrained illuminations (FR/I) received extensive study because of the existence of illumination subspace. P. Belhumer et al. (1996) presented a study on the comparison between principal component analysis (PCA) and subspace linear discriminant analysis (LDA) for this problem. PCA and subspace LDA are two well-known linear projection methods that can be characterized as trace optimization on scatter matrices. Generally, a linear projection method can be derived by applying a specific matrix analysis technique on specific scatter matrices under some optimization criterion. Several novel linear projection methods were proposed recently using generalized singular value decomposition or QR decomposition matrix analysis techniques [H. Park, et al., 2003], [J. Ye and Q. Li, 2004]. In this paper, we present a comparative study on these linear projection methods in FR/I. We further involve multiresolution analysis in the study. Our comparative study is expected to give a relatively comprehensive view on the performance of linear projection methods in FR/I problems.
In this paper, we take advantage of extended superquadrics to represent and recognize the entire set of 36 geons. Extended superquadrics are novel volumetric shape models that include superquadrics as a special case. ...
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In this paper, we take advantage of extended superquadrics to represent and recognize the entire set of 36 geons. Extended superquadrics are novel volumetric shape models that include superquadrics as a special case. An extended superquadric model can be deformed in any direction because it extends the exponents of the superquadric model from constants to functions of the latitude and longitude angles in the spherical coordinate system. Thirteen features derived from the extended superquadric parameters are recovered in order to distinguish between all 36 geon classes. Classification error rates are estimated for the nearest neighbor classifier and backpropagation neural network. Both simulated data (at different noise levels) and real geon models are tested in our experiments. The results are very encouraging and have significant benefits for an object recognition system.
In this paper, novel algorithms computing dense 3D scene flow from multiview image sequences are described. A new hierarchical rule-based stereo matching algorithm is presented to estimate the initial disparity map. D...
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ISBN:
(纸本)0769512720
In this paper, novel algorithms computing dense 3D scene flow from multiview image sequences are described. A new hierarchical rule-based stereo matching algorithm is presented to estimate the initial disparity map. Different available constraints under a multiview camera setup are investigated and then utilized in the proposed motion estimation algorithms. We show two different formulations for 3D scene flow computation. One formulation assumes that initial disparity map is accurate while the other does not make this assumption. image segmentation information is used to maintain the motion and depth discontinuities. Iterative implementations are used to successfully compute 3D scene flow and structure at every point in the reference image. Novel hard constraints are introduced in this paper to make the algorithms more accurate and robust. Promising experimental results are seen by applying our algorithms to real imagery.
Scene flow is the 3D motion field of points in the world. Given N (N>1) image sequences gathered with a N-eye stereo camera or N calibrated cameras, we present a novel system which integrates 3D scene flow and stru...
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Scene flow is the 3D motion field of points in the world. Given N (N>1) image sequences gathered with a N-eye stereo camera or N calibrated cameras, we present a novel system which integrates 3D scene flow and structure recovery in order to complement each other's performance. We do not assume rigidity of the scene motion, thus allowing for non-rigid motion in the scene. In our work, images are segmented into small regions. We assume that each small region is undergoing similar motion, represented by a 3D affine model. Nonlinear motion model fitting based on both optical flow constraints and stereo constraints is then carried over each image region in order to simultaneously estimate 3D motion correspondences and structure. To ensure the robustness, several regularization constraints are also introduced. A recursive algorithm is designed to incorporate the local and regularization constraints. Experimental results on both synthetic and real data demonstrate the effectiveness of our integrated 3D motion and structure analysis scheme.
Inferring both 3D structure and motion of nonrigid objects from monocular images is an important problem in computer vision. The challenges stem not only from the absence of point correspondences but also from the str...
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Inferring both 3D structure and motion of nonrigid objects from monocular images is an important problem in computer vision. The challenges stem not only from the absence of point correspondences but also from the structure ambiguity. In this paper, a hierarchical method which integrates both local patch analysis and global shape descriptions is devised to solve the dual problem of structure and nonrigid motion recovery by using an elastic geometric model-extended superquadrics (ESQ). The nonrigid object of interest is segmented into many small areas and local analysis is performed to recover small details for each small area, assuming that each small area is undergoing similar nonrigid motion. Then, a recursive algorithm is proposed to guide and regularize local analysis with global information by using an appropriate global ESQ model. This local-global hierarchy enables us to capture both local and global deformations accurately and robustly. Experimental results on both simulation and real data are presented to validate and evaluate the effectiveness and robustness of the proposed approach.
We propose a novel method for continuous 3D depth recovery and tracking using calibrated stereo. The method integrates stereo correspondence, surface reconstruction and tracking by using a new single deformable dual m...
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We propose a novel method for continuous 3D depth recovery and tracking using calibrated stereo. The method integrates stereo correspondence, surface reconstruction and tracking by using a new single deformable dual mesh optimization, resulting in simplicity, robustness and efficiency. In order to combine stereo correspondence and structure recovery, the method introduces an external energy function defined for a 3D volume based on cross-correlation between the stereo pairs. The internal energy functional of the deformable dual mesh imposes smoothness on the surfaces and it serves as a communication tool between the two meshes. Under the forces produced by the energy terms, the dual mesh deforms to recover and track the 3D surface. The newly introduced dual-mesh model, which is one of the main contributions of this paper, makes the system robust against local minima and yet it is efficient. A coarse-to-fine minimization approach makes the system even more efficient. Tracking is achieved by using the recovered surface as an initial position for the next time frame. Although the system can effectively utilize initial surface positions and disparity data, they are not needed for a successful operation, which makes this system applicable to a wide range of areas. We present the results of a number of experiments on stereo human face and cloud images, which proves that our new method is very effective.
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