We consider a scene containing two independently and generally moving objects, viewed by two general perspective views. Using matching points arising from both objects simultaneously we derive a geometrical constraint...
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ISBN:
(纸本)0769512720
We consider a scene containing two independently and generally moving objects, viewed by two general perspective views. Using matching points arising from both objects simultaneously we derive a geometrical constraint, applicable to points from both objects, we call the segmentation matrix. We then use this constraint in order to recover the fundamental matrices associated with each object, or simply to segment the scene into the two objects. Moreover, when the two bodies move in pure translation relative to each other we can both segment the scene and recover the affine calibration (homography at infinity) of the camera geometry. Unlike algorithms suggested in the past we need only two images, we work with general projective cameras (rather than affine or orthographic) and with general body motion, and no prior information beyond point matches is required.
In this paper we address the problem of model-based image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters an initial probability distribution of t...
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ISBN:
(纸本)0769512720
In this paper we address the problem of model-based image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters an initial probability distribution of the model edge in the image is obtained From the vicinity of the surmised edge local statistics are learned for both sides of the edge. These local statistics provide locally adapted criteria to distinguish the two sides of the edge even in the presence of spatially changing properties such as texture, shading, or color Based on the local statistics the model parameters are iteratively refined using a MAP estimation. Experiments with RGB images show that the method is capable of achieving high subpixel accuracy and robustness even in the presence of texture, shading, clutter, and partial occlusion.
Intensity histograms have been used extensively for recognition and for retrieval of images and video from visual databases. Intensity histograms of images at different individual resolutions have also been used for i...
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ISBN:
(纸本)0769512720
Intensity histograms have been used extensively for recognition and for retrieval of images and video from visual databases. Intensity histograms of images at different individual resolutions have also been used for indexing. They suffer, however, from the inability to encode spatial image information. Spatial information can be incorporated into histograms simply by taking histograms of an image at multiple resolutions together to form a multiresolution histogram. Multiresolution histograms can also be computed, stored, and matched efficiently. In this work we analyze and quantify the relation and sensitivity of the multiresolution histogram to spatial image information as well as to properties of shapes and textures in an image. We verify the analytical results experimentally. We demonstrate the ability of multiresolution histograms to discriminate between images, as well as their robustness to noise.
We present an original approach for non parametric motion analysis in image sequences. It relies on the statistical modeling,of distributions of local motion-related measurements computed over image sequences. Contrar...
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ISBN:
(纸本)0769512720
We present an original approach for non parametric motion analysis in image sequences. It relies on the statistical modeling,of distributions of local motion-related measurements computed over image sequences. Contrary to previously proposed methods, the use of temporal multiscale Gibbs models allows us to handle in a unified statistical framework both spatial and temporal aspects of motion content. The important feature of our probabilistic scheme is to make the exact computation of conditional likelihood functions feasible and simple. It enables us to straightforwardly achieve model estimation according to the ML criterion and to benefit from a statistical point of view for classification issues. We have conducted motion recognition experiments over a large set of real image sequences comprising various motion types such as temporal texture samples, human motion examples and rigid motion situations.
The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the proje...
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ISBN:
(纸本)0769512720
The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the projection matrices, the scene point positions, and their depths. Since these unknowns are not independent, constraints must be chosen to ensure that the optimization process is well posed. This paper examines three plausible choices, and shows that the first one leads to the Sturm-Triggs projective factorization algorithm, while the other two lead to new provably-convergent approaches. Experiments with synthetic and real data axe used to compare the proposed techniques to the Sturm-Triggs algorithm and bundle adjustment.
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.
For Motion Picture Special Effects, it is often necessary to take a source image of an actor, segment the actor from the unwanted background, and then composite over a new background. The standard approach requires th...
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ISBN:
(纸本)0769512720
For Motion Picture Special Effects, it is often necessary to take a source image of an actor, segment the actor from the unwanted background, and then composite over a new background. The standard approach requires the unwanted background to be a blue screen. While this technique is capable of handling areas where the foreground blends into the background, the physical requirements present many practical problems. This paper presents an algorithm that requires minimal human interaction to segment Motion Picture resolution images and image sequences. We show that it can be used not only to segment badly lit or noisy bluescreen images, but also to segment actors where the background is more varied.
The objective of this work is the super-resolution restoration of a set of images, and we investigate the use of learnt image models within a generative Bayesian framework. It is demonstrated that restoration of far h...
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ISBN:
(纸本)0769512720
The objective of this work is the super-resolution restoration of a set of images, and we investigate the use of learnt image models within a generative Bayesian framework. It is demonstrated that restoration of far higher quality than that determined by classical maximum likelihood estimation can be achieved by either constraining the solution to lie on a restricted sub-space, or by using the sub-space to define a spatially varying prior This sub-space can be learnt from image examples. The methods are applied to both real and synthetic images of text and faces, and results are compared to Schultz and Stevenson's MAP estimator [15]. We consider in particular images of scenes for which the point-to-point mapping is a plane projective transformation which has 8 degrees of freedom. In the real image examples, registration is obtained from the images using automatic methods.
The human visual system can interpret two-dimensional (2-D) line drawings like the Necker cube as three-dimensional (3-D) wire frames. On this human ability Thomas Marill presented two important papers. First one prop...
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ISBN:
(纸本)0769512720
The human visual system can interpret two-dimensional (2-D) line drawings like the Necker cube as three-dimensional (3-D) wire frames. On this human ability Thomas Marill presented two important papers. First one proposed the 3-D interpretation model based on the principle to minimize the standard deviation of the angles between line segments in 3-D wire frame (MSDA), and reported the results of simulation experiments. Second one proposed the principle to minimize the description length on the internal representation in visual system. Motivated by Marill's principle to minimize the description length, we propose a principle to minimize the entropy of angle distribution between line segments in a 3-D wire frame (MEAD), which is more general than the MSDA one. And we implement the principle MEAD using a genetic algorithm (GA) as a simulation program. The results of simulation experiments show that the proposed principle of MEAD is more appropriate than the MSDA and another principle.
Face recognition has established itself as an important subbranch of patternrecognition within the field of computer science. Many state-of-the-art systems have focused on the task of recognizing frontal views of peo...
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ISBN:
(纸本)0780367251
Face recognition has established itself as an important subbranch of patternrecognition within the field of computer science. Many state-of-the-art systems have focused on the task of recognizing frontal views of people. In this work we will present an approach to recognize profile views (90degrees) with a system trained on transformed frontal views. The system combines an Artificial Neural Network (ANN) and a classification process based on Hidden Markov Models (HMM). One of the main ideas of this system is to perform the recognition task without the use of any 3D-information of heads and faces. The presented system has been tested with subsets of the FERET and the MUGSHOT databases.
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