Many algorithms have been devised for halftoning digital images. These algorithms all suffer well-studied defects, which are especially apparent in the case where the resulting image is displayed at the marginally ove...
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Many algorithms have been devised for halftoning digital images. These algorithms all suffer well-studied defects, which are especially apparent in the case where the resulting image is displayed at the marginally oversampled resolution and viewed at the critical pixel merge distance. Recently, it has been shown that a neural network approach may be useful for halftoning. Here, the feasibility of using neural networks in a practical application is considered. The cellular neural network (CNN) architecture is chosen for its proven implementability in VLSI and high speed operation. Since both the CNN and halftoning have a geometrically local character, the CNN provides a natural implementation. The CNN template weights are derived by analogy to the well-known error diffusion algorithm for halftoning. Some limitations of the neural network approach are analyzed providing an advance in designing template weights over previous methods. These limitations are shown to be especially critical in the case of the small interconnection neighborhoods needed for efficient implementation. Our design criteria are validated by direct simulation. The resulting halftones are shown to be more faithful reproductions of the original than those produced by the error diffusion algorithm. It is suggested that a CNN with optical inputs could provide a high-speed scanner/halftoner for applications such as FAX.
In this paper, we propose the knowledge representation and evidence propagation schemes based on multivariate belief functions and present a medical image recognition system as an example to demonstrate their effectiv...
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ISBN:
(纸本)0819412813
In this paper, we propose the knowledge representation and evidence propagation schemes based on multivariate belief functions and present a medical image recognition system as an example to demonstrate their effectiveness in evidential reasoning. The multivariate belief functions, defined in a product space, are employed to represent domain specific knowledge such as rules or propositions. The product space and its sub-spaces (margins) are composed of a set of compatible frames. The logical relationships among these margins can be easily defined by using multivariate belief functions. Propagation of evidence is executed by extending or marginalizing the associated multivariate belief function to those margins characterized by their logical relationships. By using the blackboard-based architecture and the profound features of D-S theory, the proposed image recognition system is capable of mimicking the reasoning process of a human expert in recognizing anatomical entities efficiently in a set of correlated x-ray computed tomography, proton density weighted, and T2-weighted magnetic resonance images. Additionally, the proposed schemes can be also applied to other problem domains by employing the appropriate knowledge base. Several experiment results are given to illustrate the performance of the proposed system.
A signal classifier is presented whose features consist of characteristic frequencies/resonances of time series of the signal over the observation window. signals of interest here include those which can be well appro...
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A new stochastic optimization algorithm is introduced in which a pipeline of many biased stochastic procedures cooperate to concurrently sample the usual Boltzmann distribution for different temperatures. Convergence ...
ISBN:
(纸本)0819412813
A new stochastic optimization algorithm is introduced in which a pipeline of many biased stochastic procedures cooperate to concurrently sample the usual Boltzmann distribution for different temperatures. Convergence and efficiency of the pipeline algorithm is proved under certain conditions. Experimental confirmation is provided using seven standard test problems in nonlinear optimization.
A disadvantage of using discrete-state Markov random field models of images is that optimal estimators for reconstruction problems require excessive and typically random amounts of computation. In one approach the key...
ISBN:
(纸本)0819412813
A disadvantage of using discrete-state Markov random field models of images is that optimal estimators for reconstruction problems require excessive and typically random amounts of computation. In one approach the key task is the computation of the conditional mean of the field given the data or equivalently the unconditional mean of the a posteriori field. In this paper we describe a hierarchy of deterministic parallelizable methods for such computations.
This paper presents neural network models for storing terminating and cyclic temporal sequences of patterns under synchronous, sequential and asynchronous dynamics. We use fully interconnected neural networks with asy...
ISBN:
(纸本)0819412813
This paper presents neural network models for storing terminating and cyclic temporal sequences of patterns under synchronous, sequential and asynchronous dynamics. We use fully interconnected neural networks with asymmetric weight connections for synchronous and sequential dynamics and a layered neural network with feedback for asynchronous dynamics. The network were successfully implemented and the number of patterns that could be stored and recalled was approximately 12% of the size of the patterns in the network.
This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements ...
ISBN:
(纸本)0819412813
This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements a parallel nonlinear regression technique and represents a powerful method for evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis & Wets random optimization method thereby maintaining the integrity of the stochastic search while taking into account empirical information about the response surface. A network architecture is proposed which is motivated by the structures generated in projection pursuit regression and the cascade-correlation learning architecture. Results are given for the 3-bit parity, normally distributed data, and the T-C classifier problems.
Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech, image recognition and pattern recognition. For high performance and for controllin...
ISBN:
(纸本)0819412813
Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech, image recognition and pattern recognition. For high performance and for controlling the size of the network, the input information must be preprocessed before being fed into the neural network. In this paper, a probabilistic spectral feature extraction technique (PSFET) with multiview spectral representations and its applications are described. During training and testing, the PSFET allows efficient extraction of useful information in addition to generating an input vector size for best classification performance by the following neural network. Experimental results indicate that the performance of the neural network increases in classification accuracy when PSFET is used at the input. The network also generalizes better.
Numerical computation with Bayesian posterior densities has recently received much attention both in the statistics and computer vision communities. This paper explores the computation of marginal distributions for mo...
ISBN:
(纸本)0819412813
Numerical computation with Bayesian posterior densities has recently received much attention both in the statistics and computer vision communities. This paper explores the computation of marginal distributions for models that have been widely considered in computer vision. These computations can be used to assess homogeneity for segmentation, or can be used for model selection. In particular, we discuss computation methods that apply to a Markov random field formation, implicit polynomial surface models, and parametric polynomial surface models, and present some demonstrative experiments.
Within the framework of pattern recognition via Markov random field modelling, we propose three methods for estimating the topological and statistical parameters characterizing the model, namely clique orders, anisotr...
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