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Use of the ZeroNorm With Linear Models and Kernel Methods
, 2002
"... We explore the use of the socalled zeronorm of the parameters of linear models in learning. ..."
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Cited by 171 (3 self)
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We explore the use of the socalled zeronorm of the parameters of linear models in learning.
An ImageBased Approach to ThreeDimensional Computer Graphics
, 1997
"... The conventional approach to threedimensional computer graphics produces images from geometric scene descriptions by simulating the interaction of light with matter. My research explores an alternative approach that replaces the geometric scene description with perspective images and replaces the s ..."
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Cited by 206 (6 self)
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the simulation process with data interpolation. I derive an imagewarping equation that maps the visible points in a reference image to their correct positions in any desired view. This mapping from reference image to desired image is determined by the centerofprojection and pinholecamera model of the two
Sparse representation for color image restoration
 the IEEE Trans. on Image Processing
, 2007
"... Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted ..."
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Cited by 214 (30 self)
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Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The KSVD has been recently proposed for this task [1], and shown to perform very well for various grayscale image processing tasks. In this paper we address the problem of learning dictionaries for color images and extend the KSVDbased grayscale image denoising algorithm that appears in [2]. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to stateoftheart results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper. EDICS Category: COLCOLR (Color processing) I.
People Identification for Domestic Nonoverlapping RGBD Camera Networks
"... AbstractThe ability to identify the specific person in a home camera network is very relevant for healthcare applica tions where humans need to be observed daily in their living environment. The appearance based people identification in a domestic environment has many similarities with the problem ..."
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of reidentification in public surveillance systems, but there are also some additional beneficial and constraining factors (e.g., less people, nonpedestrian behaviour, unusual camera viewpoints). In this paper, we are considering the problem of people identification in a small home RGBD camera
Discriminative Density Propagation for 3D Human Motion Estimation
 In CVPR
, 2005
"... We describe a mixture density propagation algorithm to estimate 3D human motion in monocular video sequences based on observations encoding the appearance of image silhouettes. Our approach is discriminative rather than generative, therefore it does not require the probabilistic inversion of a predi ..."
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Cited by 113 (16 self)
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We describe a mixture density propagation algorithm to estimate 3D human motion in monocular video sequences based on observations encoding the appearance of image silhouettes. Our approach is discriminative rather than generative, therefore it does not require the probabilistic inversion of a
Variable Kernel Density Estimation of Color Invariant Images
"... Therefore, in this paper, we formulate kernel density estimation robustness against noisy data, and robustness against changing illumination. To achieve this, computational methods are presented to measure colour constant gradients. Further, a model is given to estimate the amount of sensor noise th ..."
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Therefore, in this paper, we formulate kernel density estimation robustness against noisy data, and robustness against changing illumination. To achieve this, computational methods are presented to measure colour constant gradients. Further, a model is given to estimate the amount of sensor noise
Colour Image Segmentation by NonParametric Density Estimation
 in Colour Space, in Proc. BMVC 2001, BMVA
, 2001
"... A novel colour image segmentation routine, based on clustering pixels in colour space using nonparametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated at a se ..."
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Cited by 4 (1 self)
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A novel colour image segmentation routine, based on clustering pixels in colour space using nonparametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated at a
Bayesian Classification with Gaussian Processes
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for c = 1; : : : ; m. For a twoclass problem, the probability of class 1 given x is estimated by oe(y(x)), where oe(y) = 1=(1 + e ). A Gaussian process prior is placed on y(x), and is combined wi ..."
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Cited by 178 (1 self)
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We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for c = 1; : : : ; m. For a twoclass problem, the probability of class 1 given x is estimated by oe(y(x)), where oe(y) = 1=(1 + e ). A Gaussian process prior is placed on y(x), and is combined
Results 11  20
of
10,904