Graphical models are powerful tools for processing.images. However, the large dimensionality of even local image data poses a difficulty: representing the range of possible graphical model node variables with discrete...
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Graphical models are powerful tools for processing.images. However, the large dimensionality of even local image data poses a difficulty: representing the range of possible graphical model node variables with discrete states leads to an overwhelmingly large number of states for the model, often making both exact and approximate inference computationally intractable. We propose a representation that allows a small number of discrete states to represent the large number of possible image values at each pixel or local image patch. Each node in the graph represents the best regression function, chosen from a set of candidate functions, for estimating the unobserved image pixels from the observed samples. This permits a small number of discrete states to summarize the range of possible image values at each point in the image. Belief propagation is then used to find the best regressor to use at each point. To demonstrate the usefulness of this technique, we apply it to two problems: super-resolution and color demosaicing. In both cases, we find our method compares well against other techniques for these problems.
Color values in an image are related to image irradiance by a nonlinear function called radiometric response function. Since this function depends on the aperture and the shutter speed, image intensity of a same objec...
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Color values in an image are related to image irradiance by a nonlinear function called radiometric response function. Since this function depends on the aperture and the shutter speed, image intensity of a same object may vary during the acquisition of an image sequence due to auto exposure feature of the camera. While this is desirable to make optimal use of the limited dynamic range of most cameras, this causes problems for a number of applications in computer vision. In this paper, we propose a method for estimating the radiometric response function and apply it to radiometrically align images so that the color values are consistent for all images of a sequence. Our approach computes the response function, exposure and white balance changes between images (up to some ambiguity) for a moving camera without any prior knowledge about exposures. We show the performance of our algorithm by estimating the response function from synthetic images and also from real world data, using it to radiometrically align the images.
In this paper, a new approach to imageprocessing.in arts is presented in the domain of generating a series of artistic mosaic pictures. The mosaic pictures consist of the same elements as each other, but might be ext...
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Our goal is to augment images of non-rigid scenes coming from single-camera footage. We do not assume any a priori information about the scene being viewed, such as for example a parameterized 3D model or the motion o...
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ISBN:
(纸本)0769521584
Our goal is to augment images of non-rigid scenes coming from single-camera footage. We do not assume any a priori information about the scene being viewed, such as for example a parameterized 3D model or the motion of the camera. One possible solution is to use non-rigid factorization of points, from which a dense interpolating function modeled by a thin-plane spline can be computed. However, in many cases, point correspondences fail to capture precisely all the deformations occurring in the scene. Examples include the eyebrows or the lips when augmenting sequences of a face. Such deformations can be captured by tracking curves, but then point correspondences are not obtained directly due to the aperture problem. We propose an integrated method for non-rigid factorization and thin-plate spline interpolant estimation using point and curve correspondences over multiple views. The main novelties lie in the introduction of curves into the non-rigid factorization framework and in a direct global solution for the registration map, obtained by minimizing the registration error over all points and curves while taking all the images into account. The parameters of the registration map are set using cross-validation. The fidelity of the map is demonstrated by augmenting video footage undergoing various types of deformation.
Tensor fields specifically, matrix valued data sets, have recently attracted increased attention in the fields of imageprocessing.computer vision, visualization and medical imaging. In this paper, we present a novel...
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Tensor fields specifically, matrix valued data sets, have recently attracted increased attention in the fields of imageprocessing.computer vision, visualization and medical imaging. In this paper, we present a novel definition of tensor "distance" grounded in concepts from information theory and incorporate it in the segmentation of tensor-valued images. In some applications, a symmetric positive definite (SPD) tensor at each point of a tensor valued image can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between SPD tensors would be the KL divergence or its relative. We propose the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the tensors being compared that leads to a novel closed form expression. Unlike the traditional Frobenius norm-based tensor distance, our "distance" is affine invariant, a desirable property in many applications. We then incorporate this new tensor "distance" in a region based active contour model for bimodal tensor field segmentation and show its application to the segmentation of diffusion tensor magnetic resonance images (DT-MRI) as well as for the texture segmentation problem in computer vision. Synthetic and real data experiments are shown to depict the performance of the proposed model.
This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image space. The tracker adaptively updates this...
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This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image space. The tracker adaptively updates this subspace while tracking by finding a linear subspace that best approximates the observations made in the previous frames. Instead of the traditional L2- reconstruction error norm which leads to subsapce estimation using PCA or SVD, we argue that a variant of it, the uniform L2-reconstruction error norm, is the right one for tracking. Under this framework, we provide a simple and a computationally inexpensive algorithm for finding a subspace whose uniform L2-reconstruction error norm for a given collection of data samples is below some threshold, and a simple tracking algorithm is an immediate consequence. We show experimental results on a variety of image sequences of people and man-made objects moving under challenging imaging conditions, which include drastic illumination variation, partial occlusion and extreme pose variation.
In this paper, we propose a new method, video repairing, to robustly infer missing static background and moving fore-ground due to severe damage or occlusion from a video. To recover background pixels, we extend the i...
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In this paper, we propose a new method, video repairing, to robustly infer missing static background and moving fore-ground due to severe damage or occlusion from a video. To recover background pixels, we extend the image repairing method, where layer segmentation and homography blending are used to preserve temporal coherence and avoid flickering. By exploiting the constraint imposed by periodic motion and a subclass of camera and object motions, we adopt a two-phase approach to repair moving foreground pixels: In the sampling phase, motion data are sampled and regularized by 3D tensor voting to maintain temporal coherence and motion periodicity. In the alignment phase, missing moving foreground pixels are inferred by spatial and temporal alignment of the sampled motion data at multiple scales. We experimented our system with some difficult examples, where the camera can be stationary or in motion.
Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance pro...
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Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms. This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. There is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.
Wavelet image denoising has been well acknowledged as an important method of denoising in imageprocessing. This paper describers a new method for the suppression of noise in image by fusing the wavelet denoising tech...
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This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DIM/DEM). More specifically, i...
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This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DIM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish mat the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.
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