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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Nonnegative Tensor Decomposition and It's applications in image processing

Nonnegative Tensor Decomposition and It's Applications in Im...

引用

International conference on Computer Science and Information Technology (CSIT)

作者： Hongli Yang Guoping He Yulin Dong Information Science and Engineering College of Shandong University of Science and Technology Qingdao Shandong China

Nonnegative tensor decomposition is an effective data processing technique that has proven to be useful for a wide variety of applications. In this paper we get some results that a three order tensor can be represented by the matrix product. By the representation of matrix product, we give an iterative algorithm for nonnegative tensor decomposition which has good performance. We also applied our algorithm to image processing, and the reconstruct image is in good quality.

关键词： Tensile stress Matrix decomposition artificial neural networks Computational modeling Brain models Brain modeling Signal processing algorithms

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Nonlinear dimension reduction methods and segmentation of hyperspectral images

Nonlinear dimension reduction methods and segmentation of hy...

引用

IEEE Signal processing and Communications applications (SIU)

作者： Gokhan Bilgin Sarp Erturk Tulay Yildirim Elektronik ve Haberleme Muhendisligi Bolumu Yildiz Teknik Universitesi Turkey Elektronik ve Haberleme Muhendisligi Bolumu Kocaeli Universitesi Turkey

In this paper nonlinear dimension reduction methods are applied to hyperspectral images and the segmentation performance is investigated. The biggest disadvantage of nonlinear dimension reduction techniques is their long computational processing time. To overcome this problem, prototypes which represent the spectral distribution of the scene have been obtained with vector quantization and dimension reduction has been applied on these prototypes. Dimension reduction of all pixels in the scene has been accomplished using Radial Basis Function (RBF) neural networks and the developed `K-point mean interpolation' method. The positive effects of these methods on the segmentation of the scene have been presented in the experimental results section using objective evaluation criterion.

关键词： Hyperspectral imaging Hyperspectral sensors Principal component analysis Remote sensing image segmentation Prototypes artificial neural networks

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Complex mapping-based blind image superresolution

Complex mapping-based blind image superresolution

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IEEE Signal processing and Communications applications (SIU)

作者： Fatih Kara Cabir Vural TUBITAK-UEKAE (Ulusal Elektronik ve Kriptoloji Arastirma Enstitusu) Turkey Elektrik ve Elektronik Muhendisligi Bolumu Sakarya Universitesi Esentepe Turkey

Recently, we proposed a blind resolution enhancement method for pure translational motion and shift invariant blur which used a two-dimensional and single-input multiple-output extension of the constant modulus algorithm. The method worked well when the bit number per pixel was low, but the performance decreased as the bit number increased. In this work, we propose a refined scheme in which complex representation of images and a set of complex deconvolution FIR filters are used. Simulations show that the refined method succeeds in recontructing the high-resolution image without the knowledge of blur parameters even when the number of bits per pixel is high.

关键词： artificial neural networks image resolution Signal resolution Reactive power image processing Deconvolution Pixel

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J-Net system: A new paradigm for artificial neural networks applied to diagnostic imaging

J-Net system: A new paradigm for Artificial Neural Networks ...

引用

conference of the North American Fuzzy Information processing Society - NAFIPS

作者： Enzo Grossi Massimo Buscema Medical Department Bracco SpA Milan Italy Semeion Research Center of Science of Communications Rome Italy

In this paper we present a new unsupervised artificial adaptive system, able to extract features of interest in digital imaging, to reduce image noise maintaining the spatial resolution of high contrast structures and the expression of hidden morphological features. The new system, named J-Net, belongs to the family of ACM systems developed by Semeion Research Institute. J-Net is able to isolate in an almost geological way different brightness layers in the same image. These layers seem to be invisible to the human eye and for the other mathematical imaging system. This ability of the J-Net can have important medical applications. Two examples of application are reported: the first in digital subtraction angiography for arterial stenosis diagnosis and the second in Multi-slice CT for lung cancer early detection and evolution prediction.

关键词： artificial neural networks Cancer High-resolution imaging Adaptive systems Feature extraction Digital images Noise reduction Spatial resolution Geology Brightness

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Simulated annealing for hierarchical pattern detection and seismic applications

Simulated annealing for hierarchical pattern detection and s...

引用

International Joint conference on neural networks (IJCNN)

作者： Kou-Yuan Huang Ying-Liang Chou Department of Computer Science National Chiao Tung University Hsinchu Taiwan

A hierarchical system is proposed by using simulated annealing for the detection of lines, circles, ellipses, and hyperbolas in image. The hierarchical detection procedures are type by type and pattern by pattern. The equation of ellipse and hyperbola is defined under translation and rotation. The distance from all points to all patterns is defined as the error. Also we use the minimum error to determine the number of patterns. The proposed simulated annealing parameter detection system can search a set of parameter vectors for the global minimal error. In the experiments, using the hierarchical system, the result of the detection of a large number of simulated image patterns is better than that of using the synchronous system. In the seismic experiments, both of two systems can well detect line of direct wave and hyperbola of reflection wave in the simulated one-shot seismogram and the real seismic data, but the hierarchical system can converge faster. The results of seismic pattern detection can improve seismic interpretation and further seismic data processing.

关键词： artificial neural networks Joints

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Fourth-order Partial Differential Equations for image Inpainting

Fourth-order Partial Differential Equations for Image Inpain...

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2008 International conference on Audio，Language and image processing(2008国际声音、语言、图像过程大会)

作者： Peiying Chen Yuandi Wang Department of Mathematics Shanghai University Shanghai 200444

Inpainting is an image interpolation problem, with broad applications in image processing and vision analysis. PDE-based image inpainting has become a very active area of research in recent years. The Total variation model for imge inpainting is an effective method. But the interpolation of this model is limited to creating straight isophotes, not necessarily smoothly continued from the boundary and it does not always follow the Connectivity Principle. We have made some improvements on it and propose a novel fourth-order PDE method to inpaint missing data domain. In both smooth of inpainting and connectivity, our method is outstanding than other methods.

关键词： Mathematical model artificial neural networks TV Equations Interpolation Mathematics image processing

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Fast digital image stabilization using constrained one-bit transform, sparse search points, and partial distortion search

Fast digital image stabilization using constrained one-bit t...

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IEEE Signal processing and Communications applications (SIU)

作者： Oguzhan Urhan Elektronik ve Haberleşme Mühendisliği Bölümü Kocaeli Üniversitesi İşaret ve Görüntü İşleme Laboratuvarl-KULİS Turkey

Several improvements are proposed to speed-up the two dimensional full search based digital image sequence stabilization process in this work. Firstly, the kernel used in the constrained one-bit transform based stabilization approach is modified to reduce the computational load. Next, the utilization of a sparse search point approach is investigated. Finally, a partial distortion search approach using spiral scanning combined with a sparse search point approach. Experiments show 10 to 20 times reduction of computational complexity without much decrease in the performance.

关键词： Digital images Transforms artificial neural networks Motion estimation Motorcycles Complexity theory Kernel

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Advanced Intelligent Computing Theories and applications. With Aspects of artificial Intelligence 2008

引用

丛书名： Lecture Notes in Computer Science

2008年

作者： De-Shuang Huang Donald C. Wunsch Daniel S. Levine Kang-Hyun Jo

ISBN: (数字)9783540859840

ISBN: (纸本)9783540859833

This book - in conjunction with the two volumes CCIS 0015 and LNCS 5226 - constitutes the refereed proceedings of the 4th International conference on Intelligent Computing, ICIC 2008, held in Shanghai, China, in September 2008. The 136 revised full papers were carefully reviewed and selected from 2336 submissions. The papers address all current issues in the field of intelligent computing technology, including neural networks, evolutionary computing and genetic algorithms, fuzzy systems and soft computing, particle swarm optimization and niche technology, supervised and semi-supervised learning, unsupervised and reinforcement learning, fine feature extraction methods, combinatorial and numerical optimization, neural computing and optimization, case based reasoning and autonomy-oriented computing, as well as artificial life and artificial immune systems. The volume is rounded off by three special sessions on computational analysis and data mining in biological systems, on data mining and fusion in bioinformatics, and on intelligent pattern recognition and understanding in image processing.

关键词： Computational intelligence Congresses. Computational intelligence Industrial applications Congresses. Computational biology Congresses. Bioinformatics Congresses. Cognitive neuroscience Congresses.

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A biologically-inspired computational model for transformation invariant target recognition

A biologically-inspired computational model for transformati...

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International Joint conference on neural networks (IJCNN)

作者： Khan M. Iftekharuddin Yaqin Li Intelligence System and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. One of the primary challenges is detection and recognition of objects in the presence of transformations such as resolution, rotation, translation, scale and occlusion. In this work, we investigate a biologically-inspired computational modeling approach that exploits reinforcement learning (RL) for transformation-invariant image recognition. The RL is implemented in an adaptive critic design (ACD) framework to approximate the neuro-dynamic programming. Two ACD algorithms such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) are investigated and compared for transformation invariant recognition. The two learning algorithms are evaluated statistically using simulated transformations in 2-D images as well as with a large-scale UMIST 2-D face database with pose variations. Our simulations show promising results for both HDP and DHP for transformation-invariant image recognition as well as face authentication. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. On the other hand, HDP is more robust than DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational complexity involved in HDP is much less.

关键词： artificial neural networks Visualization Biology Biological system modeling image recognition Dynamic programming image resolution

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image annotation with semi-supervised clustering

Image annotation with semi-supervised clustering

引用

IEEE Signal processing and Communications applications (SIU)

作者： Ahmet Sayar Fatos T. Yarman-Vural Bilgisayar Muhendisligi Bolumu Orta Dogu Teknik Universitesi Turkey

Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks generated for region clusters, using low level features are matched with words in various ways. In this work, we ensured that clustering is more meaningful by using words in associated text in addition to image data in clustering of image regions to generate a codebook. We first compute topic probabilities of text documents associated with each image in the training set. Next, we eliminate low probability topics and use highly probable ones in the supervision of region clustering algorithm. To implement this supervision, we force our region clustering algorithm to assign each region to one of the clusters reserved for high probability topics of the associated text. Consequently, regions in generated clusters not only become visually closer, but also the probability of them to belong to the same topic increases. Experiment results show that image annotation with semi-supervised clustering is more successful compared to existing methods. To implement the algorithm parallel computation methods have been used.

关键词： Clustering algorithms Analytical models Indexing Data models Computational modeling Probabilistic logic artificial neural networks

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