The limited joint time and frequency resolution of Fourier analysis makes an accurate analysis of speech signals difficult. Fourier analysis offers either good temporal accuracy or good frequency resolution, never bot...
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The limited joint time and frequency resolution of Fourier analysis makes an accurate analysis of speech signals difficult. Fourier analysis offers either good temporal accuracy or good frequency resolution, never both. Many methods have been proposed to overcome this limitation. The results do not show a great improvement in the readability of the representation due to the presence of many components in the speech signal. The reallocation defined by Kodera and al. (1978) seems a good way to improve the localisation of the spectrogram. Recent work simplified the implementation of this method, which makes it attractive. This paper explores the applicability of this method to the analysis of speech signals.< >
While the view of constructive and hierarchical vision prevails, the issues of cooperation and competition among individual modules become crucial. These issues are directly related to one of the most important aspect...
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ISBN:
(纸本)078031865X
While the view of constructive and hierarchical vision prevails, the issues of cooperation and competition among individual modules become crucial. These issues are directly related to one of the most important aspects in computer vision research: integration. A major source of difficulty in developing a consistent and systematic integration formalism is the heterogeneity existing in modules, in information, and in knowledge. The author exploits, using the central theme of grouping, the homogeneous characteristics in vision problem solving and proposes a general framework, called hierarchical token grouping, that facilitates vision problem solving by providing a consistent and systematic environment for integrating modules, cues, and knowledge, all in a globally coherent mechanism.< >
This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension ...
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作者：
R. BaseriJ.W. ModestinoElectrical
Computer and Systems Engineering Department Center for Image Processing Research Rensselaer Polytechnic Institute Troy NY USA
This paper describes a second-generation region-based coding scheme operating in a subband environment. Specifically, this is a compression technique based on approximating the lowest-frequency subband of an image wit...
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This paper describes a second-generation region-based coding scheme operating in a subband environment. Specifically, this is a compression technique based on approximating the lowest-frequency subband of an image with a 2-D spline model. Following a segmentation procedure, parametric polynomials are fitted to the resulting regions of the lowest-frequency subband. This subband is then represented by the spline coefficients, the region boundary description and the residual error. The residual error plus the higher-frequency subbands are encoded using conventional pixel-based quantization schemes while we describe an efficient encoding scheme for the 2-D spline representation. An entropy-constrained design procedure is developed for designing these individual encoders to minimize the overall mean-square distortion subject to a constraint on the output entropy. A comprehensive study of the operational rate-distortion performance of the resulting scheme is presented, which indicates the superiority of this approach to the more traditional pixel-based entropy-constrained schemes.< >
A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, an...
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＜正＞Feature extraction is very important for the classifier design and the overall performance of *** recognition ***,due to the lack of theoretical guidances,feature extraction and classifier design are usually tre...
＜正＞Feature extraction is very important for the classifier design and the overall performance of *** recognition ***,due to the lack of theoretical guidances,feature extraction and classifier design are usually treated separately in current speech recognition *** *** proposes an approach to combine linear feature extraction with continuous density hidden Markov modeling（HMM） which is currently the most successful speech pattern classifier.A maximumlikelihood based algorithm is derived to iteratively train HMM parameters as well as the parameters of the feature *** algorithm is an exteusion of the Baum-Welcli parameter re-estimation algorithm for conventional HMMs and thus has a nice property of guara, nteed convergence.
This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension ...
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This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension of our previously proposed (1993) generalized tube (GT) model. Transitions among adjacent tubes are explicitly parameterized. Integrated with a bivariate Gaussian density function adopted to model the blood flow within cross sections, the GST model is applied to tracking blood vessels in MRA volumetric data. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that simultaneously utilizing both models yields robust performance under noisy conditions.
A new adaptive post-processing algorithm to enhance the quality of a noisy video sequence is presented. This algorithm employs motion compensated frame averaging to improve picture quality. A classification algorithm ...
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In a heterogeneous system, the efficient exploitation of parallelism requires scheduling strategies that account for heterogeneity among processors to achieve an effective mapping of computations to processors. The pa...
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In a heterogeneous system, the efficient exploitation of parallelism requires scheduling strategies that account for heterogeneity among processors to achieve an effective mapping of computations to processors. The paper presents a scheduling heuristic specialized for heterogeneous multiprocessor systems which make use of several different types of processors. The new algorithm is based on the greedy strategy: no processor remains idle if there is some task available that it could process. A graph called the classified typed task graph is used to describe the current status of tasks of different types at each scheduling step. With the graph, the algorithm dynamically evaluates the priorities of tasks only when there are multiply executable candidates, and then schedules an appropriate one onto the currently available processors of the corresponding type. A preliminary evaluation shows that this algorithm has promising performance.< >
Lower bound on the finishing time of optimal schedules is used as an absolute performance measure of static scheduling heuristics. This paper presents an efficient method of computing such a bound based on estimating ...
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Lower bound on the finishing time of optimal schedules is used as an absolute performance measure of static scheduling heuristics. This paper presents an efficient method of computing such a bound based on estimating overlaps among the execution ranges of tasks in a given task graph and analyzing the delays of tasks on the critical paths of the graph. The computation performed by this method is shown to be of higher quality than that of other known methods. The future work and directions on this topic are also indicated.< >
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