We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of different...
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We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be adapted to real-time imaging scenarios.
We present a parallel implementation of an MPEG encoder on the Intel Paragon supercomputer. In our approach, both spatial and temporal parallelism have been exploited, While the Paragon has the computation capacity to...
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We propose an extension of RBF networks which includes a mechanism for optimizing the complexity of the network. The approach involves two procedures: adaptation (training) and selection. The first procedure adaptivel...
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Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results th...
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In this paper we present new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured i...
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In this paper we present new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the algorithm.
In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) pa...
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In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.
The growth of networked multimedia systems has magnified the need for image copyright protection. One approach used to address this problem is to add an invisible structure to an image that can be used to seal or mark...
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The growth of networked multimedia systems has magnified the need for image copyright protection. One approach used to address this problem is to add an invisible structure to an image that can be used to seal or mark it. These structures are known as digital watermarks. We describe two techniques for the invisible marking of images. We analyze the robustness of the watermarks with respect to linear and nonlinear filtering, and JPEG compression. The results show that our watermarks detect all but the most minute changes to the image.
Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results th...
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Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent postprocessing stages. In this paper we present a modification of the standard area-based correlation approach so that it can tolerate a significant number of outliers. The approach exhibits a robust behavior not only in the presence of mismatches but also in the case of depth discontinuities. The confidence measure of the correlation and the number of outliers provide two complementary sources of information which, when implemented in a multiresolution framework, result in a robust and efficient method. We present the results of this approach on a number of synthetic and real images.
In ATM networks cell loss causes data to be dropped in the channel. When digital video is transmitted over these networks one must be able to reconstruct the missing data so that the impact of these errors is minimize...
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In ATM networks cell loss causes data to be dropped in the channel. When digital video is transmitted over these networks one must be able to reconstruct the missing data so that the impact of these errors is minimized. In this paper we describe a Bayesian approach to conceal these errors. Assuming that the digital video has been encoded using the MPEG1 or MPEG2 compression scheme, each frame is modeled as a Markov random field. A maximum a posteriori estimate of the missing macroblocks and motion vectors is described based on the model.
The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this pa...
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The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages. we extract them by a hvpothesize-and-test paradigm using subsets of image points. Competing hypotheses arc then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only lo reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.
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