作者：
M. WallaceG. AkrivasG. StamouImage
Video & Multimedia Systems Laboratory Department of Electrical & Computer Engineering National and Technical University of Athens Athens Greece
In this paper we formally define the problem of automatic detection of thematic categories in a semantically indexed document, and identify the main obstacles to overcome in this process. Furthermore, we explain how d...
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In this paper we formally define the problem of automatic detection of thematic categories in a semantically indexed document, and identify the main obstacles to overcome in this process. Furthermore, we explain how detection of thematic categories can be achieved, with the use of a fuzzy quasi-taxonomic relation. Our approach relies on a fuzzy hierarchical clustering algorithm; this algorithm uses a similarity measure that is based on the notion of context.
作者：
W. Abd-AlmageedC.E. SmithS. RamadanRobotics
Artificial Intelligence and Vision Laboratory Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA
In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model t...
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In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.
In this paper, we present a 3D reconstruction approach of a liver tumour model from a sequence of 2D MR parallel cross-sections, and the integration of this reconstructed 3D model with a mechanical tissue model. The r...
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Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way imageanalysis ...
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ISBN:
(纸本)0780383508
Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way imageanalysis algorithms are designed. In this paper, we take up this issue and focus on algorithms designed and developed for target detection and classification in hyperspectral imagery. In order to effectively characterize the information available before and after the data are processed, the a priori information and a posteriori information are used in accordance with how the information is obtained. A piece of information is referred to as a priori information if it is provided by known knowledge before data are processed. On the other hand, a piece of information is referred to as a posteriori information if it is unknown a priori, but can be obtained directly from the data in an unsupervised fashion during the course of data processing. Since a priori information is known beforehand, it can be further decomposed into two types of information, desired and undesired a priori information. The desired a priori information is the knowledge that will assist, improve and enhance data analysis, whereas the undesired a priori information is the knowledge that hinders, interferes or destructs analysis during data processing. This paper investigates how these three types of information play their roles in design and development of several hyperspectral target detection and classification algorithms. Experiments are also conducted to validate their utility.
The FastHARP magnetic resonance imaging pulse sequence, coupled with fast processing, has the potential to measure and display cardiac function in real-time during clinical dobutamine stress tests. In this paper, we p...
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Pattern classification problems involve constrained optimization in the form of minimization of chosen cost functions. Such embedded constraints in integrated neural-fuzzy pattern recognition systems improve the perfo...
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Pattern classification problems involve constrained optimization in the form of minimization of chosen cost functions. Such embedded constraints in integrated neural-fuzzy pattern recognition systems improve the performance of these systems. The comparative performance of an ART-based pattern classifier integrated with fuzzy optimization constraints is demonstrated in designing vector quantizers.
作者：
Abd-Almageed, WaelSmith, ChristopherRobotics
Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico United States
In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov Models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach a...
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ISBN:
(纸本)1889335185
In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov Models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use Hidden Markov Models for object classification from silhouettes is presented.
This paper presents a multicamera Visual Room (ViRoom). It is constructed from low-cost digital cameras and standard computers running on Linux. Software based synchronized image capture is introduced. A fully automat...
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