In this paper we discuss the advantages of two specific neural net models for the purposes of scene analysis. Two applications of those models are also presented. One concerns the prediction of sea water depth on the ...
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ISBN:
(纸本)0819403458
In this paper we discuss the advantages of two specific neural net models for the purposes of scene analysis. Two applications of those models are also presented. One concerns the prediction of sea water depth on the basis of the intensity of reflected light. The second one concerns the characterization of biological cells from slide images. The fractal dimension is found to be a very good parameter for the latter case. 1.
In the previous Studies, the standard boards of yarn (ASTM) were analyzed using the image analysis method and artificialneuralnetworks;the appearance of different knitted fabrics samples was tested for appearance Th...
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In the previous Studies, the standard boards of yarn (ASTM) were analyzed using the image analysis method and artificialneuralnetworks;the appearance of different knitted fabrics samples was tested for appearance There was a strong Influence of yarn type and fabrics structure on fabrics apparent quality. In the present research, the artificialneural network (ANN) has been applied to predict the fabric apparent parameters. The Optimum structure of ANN has been designed using the genetic algorithm method The results show that the ANN can be optimized very well by the CA and the designed ANN is very accurate and applicable to predict the apparent parameters (C) 2009 Elsevier Ltd All rights reserved.
Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different image pr...
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Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different imageprocessing and classification schemes. In this paper we present results on the classification of eight natural objects in the complete set of 448 natural images using neuralnetworks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a tenfold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.
Multilayer perceptrons (MLP), or fully connected artificialneuralnetworks, are known for performing vector-matrix multiplications using learnable weight matrices;however, their practical application in many machine ...
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In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of...
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ISBN:
(纸本)0819424412
In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of code words in which each neural network is updated through reproduction every time an input vector is processed. The other is at the level of code-books in which five neuralnetworks are included in the gene pool. Extensive experiments on a group of image samples show that the genetic algorithm outperforms other vector quantization algorithms which include competitive learning, frequency sensitive learning and LBG.
Food safety is essential for protecting health and supply chain management. Food engineering plays a foundational role in ensuring food safety by developing innovative processes and technologies for quality control, c...
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Corner detection represents one of the most important steps to identify features in images. Due to their powerful local processing capabilities, Cellular Nonlinear/neuralnetworks (CNN) are commonly utilized in image ...
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Corner detection represents one of the most important steps to identify features in images. Due to their powerful local processing capabilities, Cellular Nonlinear/neuralnetworks (CNN) are commonly utilized in imageprocessingapplications such as image edge detection, image encoding and image hole filling. CNN perform well for locating corner features in binary images. However, their use in grayscale images has not been considered due to their design difficulties. In this paper, a corner detector based on CNN for grayscale images is presented. In the approach, the original processing scheme of the CNN is modified to include a nonlinear operation for increasing the contrast of the local information in the image. With this adaptation, the final CNN parameters that allow the appropriate detection of corner points are estimated through the Differential evolution algorithm by using standard training images. Different test images have been used to evaluate the performance of the proposed corner detector. Its results are also compared with popular corner methods from the literature. Computational simulations demonstrate that the proposed CNN approach presents competitive results in comparison with other algorithms in terms of accuracy and robustness. (C) 2019 Elsevier B.V. All rights reserved.
Spiking neuralnetworks (SNN) hold the promise of being a more biologically plausible, low-energy alternative to conventional artificialneuralnetworks. Their time-variant nature makes them particularly suitable for ...
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Our goal in this article is to present a quantitative study about speech recognition and the inherent problems of its applications and the computer processing. Our approach is characterized by independent speaker and ...
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ISBN:
(纸本)0819425915
Our goal in this article is to present a quantitative study about speech recognition and the inherent problems of its applications and the computer processing. Our approach is characterized by independent speaker and we made use of pre-processing the concept as Wavelets Transform and as pattern recognition an artificialneural Network (ANN - Multilayer Perceptron -Backpropagation Algorithm).
The random neural network (RNN) is a recurrent neural network model inspired by the spiking behaviour of biological neuronal networks. Contrary to most artificialneural network models, neurons in the RNN interact by ...
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The random neural network (RNN) is a recurrent neural network model inspired by the spiking behaviour of biological neuronal networks. Contrary to most artificialneural network models, neurons in the RNN interact by probabilistically exchanging excitatory and inhibitory spiking signals. The model is described by analytical equations, has a low complexity supervised learning algorithm and is a universal approximator for bounded continuous functions. The RNN has been applied in a variety of areas including pattern recognition, classification, imageprocessing, combinatorial optimization and communication systems. It has also inspired research activity in modelling interacting entities in various systems such as queueing and gene regulatory networks. This paper presents a review of the theory, extension models, learning algorithms and applications of the RNN.
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