For a one-layered-feedback neural network e.g., a Hopfield net, containing discrete sign-function neurons, the nonlinear properties of this network can be studied very efficiently using simple discrete mathematics. Th...
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ISBN:
(纸本)0819444081
For a one-layered-feedback neural network e.g., a Hopfield net, containing discrete sign-function neurons, the nonlinear properties of this network can be studied very efficiently using simple discrete mathematics. This paper summarizes the discrete-formulation of the problem as a matrix difference equation, the simple iterative method of solving this difference equation and the derivation of the major anomalous properties of the system from the solutions. These anomalous properties include, eigen-state storage, associative storage, domain of attraction, content-addressable recall, fault-tolerant recall, capacity of storage, binary oscillating states, limit-cycles in the state space, and noise-sensitive input states. The physical origin and the systematic trend of the derivation of these properties are easily seen in the numerical examples given.
The problem of an image best approximation within the class of piecewise constant functions is considered. This allows a simpler data representation with a lower number of grey levels while retaining all information r...
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The problem of an image best approximation within the class of piecewise constant functions is considered. This allows a simpler data representation with a lower number of grey levels while retaining all information relevant to the particular application considered. The approximant can be found by solving a segmentation problem. The search for a solution is solved efficiently by training an artificialneural network (ANN) on a suitable set of templates by a standard procedure. The samples of the training alphabet fit the signal's local behaviour in the homogeneous image subregions and in the regions crossed by the edges. Therefore the original image domain is partitioned into disjoint 2D intervals (tiling), and for each one of them, the network selects the alphabet element closest to the corresponding image component. The main motivation of this work consists in devising a methodology suitable for real-time applications;indeed, the ANN tool is attractive for a hardware implementation.
In this paper, a stereo matching approach for 3D reconstruction based on wavelet analysis is presented. It can be used in neuro-vision system. The approach can be divided into two parts. First, the stereo matching pro...
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ISBN:
(纸本)081944815X
In this paper, a stereo matching approach for 3D reconstruction based on wavelet analysis is presented. It can be used in neuro-vision system. The approach can be divided into two parts. First, the stereo matching problem is solved with wavelet analysis. Dyadic discrete wavelet analysis is adopted in this process and stereo matching process is realized with global optimization. A coherent hierarchical matching strategy is constructed, so that the stereo matching process can be accomplished with coarse to fine techniques. Second, a 3D object reconstruction neural network is constructed by using BP neural network. By feeding the image corresponding points between the left image and right image in a stereo image pair, the 3D coordinates of points on object surface can be obtained using this neural network and the configuration and shape of the object can be reconstructed. With multiple 3D reconstruction neuralnetworks the 3D reconstruction processes can be performed in parallel. The examples for both synthetic and real images are shown in the experiment, and good results are obtained.
The hybrid evolutionary algorithm is used for image registration formulated as an optimization problem of finding a vector of parameters minimizing the difference between images. The reproduction phase of the algorith...
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ISBN:
(纸本)081944815X
The hybrid evolutionary algorithm is used for image registration formulated as an optimization problem of finding a vector of parameters minimizing the difference between images. The reproduction phase of the algorithm is enhanced with a two-level operation of local correction performed on the best genes in the reproduction pool. Random search is performed in the neighborhood of a gene until the time interval reaches a pre-set threshold. If the gene still retains its position in the pool, a refined multi-step search is performed using the Downhill simplex method. In order to improve the computational performance of the local search, local response analysis is used in the following way. All domains of the given reference image are classified according to their local response to a unit variation of the parameter vector. The classification scheme is based on a self-organizing neural network. During the local correction of the reproduction pool, the step size in the Downhill simplex search is modified according to the class of the image domain.
artificial intelligence (AI) is one of the fastest growing disciplines in electronic information technology. Along with diversified data, AI-enabled technologies such as imageprocessing, smart sensors, and intelligen...
artificial intelligence (AI) is one of the fastest growing disciplines in electronic information technology. Along with diversified data, AI-enabled technologies such as imageprocessing, smart sensors, and intelligent inversion, are being tested by researchers in a wide variety of geosciences domains. These technologies have the potential to help geosciences move from qualitative to quantitative analysis. We believe that taking an interdisciplinary approach will deliver benefits to both geosciences and AI. artificial Intelligence in Geosciences is an open access journal providing an interdisciplinary forum where ideas and solutions related to artificial intelligence and its applications in geosciences can be shared and discussed. To support this discussion, we encourage authors to open source their code, data, and the labels used in AI. We welcome both fundamental science and applied research describing the practical applications of AI methods in the fields of geology, rock physics, seismicity, hydrology, ecology, marine geosciences, planetary science, environment, volcanology, oceanography, remote sensing and GIS, and related areas. Submissions to artificial Intelligence in Geosciences may take the form of original research articles, review articles, perspective papers, or short communications, and a variety of topics will be considered. These include, but are not limited to: AI-based decision support systems AI-based precision geosciences Smart sensors and the Internet of Things Geosciences robotics and automation equipment Geosciences knowledge-based systems Computational intelligence in geosciences AI in geosciences optimization management Intelligent interfaces and human-machine interaction Machine vision and image/signal processing Machine learning and pattern recognition neuralnetworks, fuzzy systems, neuro-fuzzy systems Systems modeling and analysis Expert systems in geosciences Big data and cloud computing in geosciences Automatic navigation and self-driv
While finding many applications in science, engineering, and medicine, artificialneuralnetworks (ANNs) have typically been limited to small architectures. In this paper, we demonstrate how very large architecture ne...
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While finding many applications in science, engineering, and medicine, artificialneuralnetworks (ANNs) have typically been limited to small architectures. In this paper, we demonstrate how very large architecture neuralnetworks can be trained for medical imageprocessing utilizing a massively parallel, single-instruction multiple data (SIMD) computer. The two- to three-orders of magnitude improvement in processing time attainable using a parallel computer makes it practical to train very large architecture ANNs. As an example we have trained several ANNs to demonstrate the tomographic reconstruction of 64 x 64 single photon emission computed tomography (SPECT) images from 64 planar views of the images. The potential for these large architecture ANNs lies in the fact that once the neural network is properly trained on the parallel computer the corresponding interconnection weight file can be loaded on a serial computer. Subsequently, relatively fast processing of all novel images can be performed on a PC or workstation.
Pulse-Couple neuralnetworks have generated quite a bit of interest as imageprocessing tools. Past application include image segmentation, edge extraction, texture extractions, de-noising, object isolation, foveation...
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Pulse-Couple neuralnetworks have generated quite a bit of interest as imageprocessing tools. Past application include image segmentation, edge extraction, texture extractions, de-noising, object isolation, foveation and fusion. These past applications do not comprise a complete list of useful applications of the PCNN. Future avenues of research will include level set analysis, binary (optical) correlators, artificial life simulations, maze running and filter jet analysis. This presentation will explore these future avenues of PCNN research.
This paper reviews the research status of pulse-coupled neuralnetworks (PCNN) in the past decade. Considering there are too many publications about the PCNN, we summarize main approaches and point out interesting par...
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This paper reviews the research status of pulse-coupled neuralnetworks (PCNN) in the past decade. Considering there are too many publications about the PCNN, we summarize main approaches and point out interesting parts of the PCNN researches rather than contemplate to go into details of particular algorithms or describe results of comparative experiments. First, the current status of the PCNN and some modified models are briefly introduced. Second, we review the PCNN applications in the field of imageprocessing (e.g. image segmentation, image enhancement, image fusion, object and edge detection, pattern recognition, etc.), then applications in other fields also are mentioned. Subsequently, some existing problems are summarized, while we give some suggestions for the solutions to some puzzles. Finally, the trend of the PCNN is pointed out. (C) 2009 Elsevier B.V. All rights reserved.
Given that neuralnetworks have been widely reported in the research community of medical imaging we provide a focused literature survey on recent neural network developments in computer-aided diagnosis medical image ...
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Given that neuralnetworks have been widely reported in the research community of medical imaging we provide a focused literature survey on recent neural network developments in computer-aided diagnosis medical image segmentation and edge detection towards visual content analysts and medical image registration for Its pre-processing and post-processing with the aims of increasing awareness of how neuralnetworks can be applied to these areas and to provide a foundation for further research and practical development Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem (ii) how medical images could be analysed processed and characterised by neuralnetworks and (iii) how neuralnetworks could be expanded further to resolve problems relevant to medical imaging In the concluding section a highlight of comparison among many neural network applications is Included to provide a global view on computational intelligence with neuralnetworks in medical Imaging (C) 2010 Elsevier Ltd All rights reserved
Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. U...
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Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis. neuralnetworks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities. In imageprocessing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using imageprocessing methods. The analyzed factors are training parameters, imageprocessing technique and the obtained performances. This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions.
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