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pattern stability on complex-valued associative memory by local iterative learning scheme

Pattern stability on complex-valued associative memory by lo...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Hiroki Yamamoto Teijiro Isokawa Haruhiko Nishimura Naotake Kamiura Nobuyuki Matsui Graduate School of Engineering University of Hyogo Himeji Hyogo Japan Graduate School of Applied Informatics University of Hyogo Kobe Hyogo Japan

Stability of embedded patterns on associative memory is investigated in this paper. The associative memory is composed of complex-valued Hopfield neural network, in which the state of neurons are encoded by the phase values on a unit circle of complex plane. Local iterative learning scheme and Projection rule are used for embedding the patterns onto the network. The retaining performance for embedded patterns are evaluated through storing randomly generated patterns and gray-scaled images with changing the resolution of neuron state.

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Multiple classes random neural network model and color pattern recognition problems

Multiple classes random neural network model and color patte...

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international Joint conference on Neural Networks (IJCNN)

作者： J. Aguilar V. Rossell CEMISID. Departmento de Computación Facultad de Ingeniería Universidad de los Andes Merida Venezuela

The purpose of the paper is to describe the use of the multiple classes random neural network model to learn patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon the nonlinear equations of the multiple classes random neural network model using gradient descent of a quadratic error function. In addition, we propose a progressive retrieval process with adaptive threshold value.

关键词： Neural networks pattern recognition Neurons Recurrent neural networks Fires Nonlinear equations Backpropagation algorithms Pixel Signal processing Probability

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On the topological properties of intuitionistic fuzzy rough sets

On the topological properties of intuitionistic fuzzy rough ...

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IEEE international conference on Granular computing (GRC)

作者： Renbing Lin Jiyi Wang Department of Mathematics &Physics Zhejiang Shuren University Hangzhou China School of Mathematics and Information Science Zhejiang Normal University ZJNU Jinhua Zhejiang China

This paper is devoted to the discussion of the relationship between intuitionistic fuzzy rough set models and intuitionistic fuzzy topologies on a finite universe. The IFT Condition for intuitionistic fuzzy topology is proposed. It is proved that the set of all lower approximation sets based on a reflexive and transitive intuitionistic fuzzy relation consists of a intuitionistic fuzzy topology which satisfies IFT Condition.

关键词： Fuzzy sets Rough sets Topology Set theory Fuzzy set theory Physics Information science pattern analysis pattern recognition

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Rotation invariant texture classification using Support Vector Machines

Rotation invariant texture classification using Support Vect...

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international conference on Communications, computing and Control Applications (CCCA)

作者： Yassine Ben Salem Salem Nasri Electric department ENIG Gabès Tunisia Electric department ENIM Monastir Tunisia

In this paper, we study the problem of rotation invariant texture classifications. There are several methods in texture recognition problem, we compare three best known methods such us: Gabor wavelet filter, Local Binary pattern operators (LBP) and co-occurrence matrix (GLCM). A multi-class Support Vector Machines (SVM) is used as a classifier. The three methods are evaluated based on two different databases: Brodatz and Outex to bring out a comparative study about the discrimination capabilities of those different families of texture classification methods. The experimental results show that some of the studied methods are more compatible with this classification problem than the others. The SVM classifier approve the running time of the algorithm of classification.

关键词： Databases Feature extraction Support vector machine classification Gabor filters Classification algorithms pattern recognition

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Colour based off-road environment and terrain type classification

Colour based off-road environment and terrain type classific...

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8th international IEEE conference on Intelligent Transportation Systems

作者： Jansen, Paul Van Der Mark, Wannes Van Den Heuvel, Johan C. Groen, Frans C. A. Tryllian Solutions Hogehilweg 21 1101 CB Amsterdam Netherlands Observation Systems of TNO Defense Security and Safety Oude Waalsdorperweg 63 2509 JG The Hague Netherlands Intelligent Systems Laboratory Amsterdam Informatics Institute University of Amsterdam Kruislaan 403 1098 SJ Amsterdam Netherlands

ISBN: (纸本)0780392159

Terrain classification is an important problem that still remains to be solved for off-road autonomous robot vehicle guidance. Often, obstacle detection systems are used which cannot distinguish between solid obstacles such as rocks or soft obstacles such as tall patches of grass. Terrain classification is needed to prevent that the robot is stopped needlessly by the obstacle detection system. It can also be used to recognize sand roads or other drivable areas. In this paper we present a colour based method to classify typical terrain coverings such as sand, grass or foliage. Using colour recognition outdoors is difficult, because the observed colour of a material is heavily influenced by environment conditions such as the scene composition and illumination. A new approach to colour based classification is presented. It is based on the assumption that images with large similarities in environment related properties such as illumination, materials and geometry also have similar pixel distributions in a colour space. Classification based on a Maximum Likelihood method with Gaussian Mixture Models (GMMs) is improved by first distinguishing image sets in the training set that share the same environment state. Because the terrain type colours are modelled separately for each found image set, the influence of changing environment conditions is reduced. Terrain types in a new image are classified with the GMMs of the environment state that is the most similar to it. The results show that our approach is able to classify terrain types in real images with large differences in illumination. © 2005 IEEE.

关键词： pattern recognition

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Estimation of camera pose using 2D to 3D corner correspondence

Estimation of camera pose using 2D to 3D corner corresponden...

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international conference on Information Technology: Coding and computing (ITCC)

作者： Fanhuai Shi Yuncai Liu Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)0769521088

We present a new method for the determination of camera pose from 2D to 3D corner correspondence. Two cases are considered: the orthogonal corner and the general corner with known space angles. The contribution of the paper is in two folds: one is that the camera pose parameters, i.e., the rotation and translation, are easily recovered from a 2D to 3D corner correspondence; the other is that experiments using both simulated data and real images are conducted, which present good results.

关键词： Cameras Robot vision systems Aircraft navigation Image processing pattern recognition Image edge detection Image motion analysis Robot kinematics Orbital robotics Calibration

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Bearing Fault recognition Based on Feature Extraction and Clustering Analysis

Bearing Fault Recognition Based on Feature Extraction and Cl...

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2016 4th international conference on Machinery,Materials and computing Technology(ICMMCT 2016)

作者： Xin Zhang Jianmin Zhao Haiping Li Fucheng Sun Mechanical Engineering College

ISBN: (纸本)9781510821934

In this paper, the clustering analysis is used to distinguish bearing fault pattern. Some time domain feature parameters are extracted from vibration signal, and the combination of three feature parameters are chosen from these feature parameters for the clustering analysis. The Euclidean distance is used to calculate the distance of point-to-center. After validation, the effect of clustering analysis is effective to distinguish the bearing fault pattern, and the best combination of feature parameters for fault pattern recognition by clustering analysis is found.

关键词： Clustering analysis bearing fault pattern time domain feature parameters

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Monogenic Binary pattern (MBP): A Novel Feature Extraction and Representation Model for Face recognition

Monogenic Binary Pattern (MBP): A Novel Feature Extraction a...

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international conference on pattern recognition

作者： Meng Yang Lei Zhang Lin Zhang David Zhang Biometric Research Center Department of Computing Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)9781424475421

A novel feature extraction method, namely monogenic binary pattern (MBP), is proposed in this paper based on the theory of monogenic signal analysis, and the histogram of MBP (HMBP) is subsequently presented for robust face representation and recognition. MBP consists of two parts: one is monogenic magnitude encoded via uniform LBP, and the other is monogenic orientation encoded as quadrant-bit codes. The HMBP is established by concatenating the histograms of MBP of all sub-regions. Compared with the well-known and powerful Gabor filtering based LBP schemes, one clear advantage of HMBP is its lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. The experimental results on the AR and FERET face databases validate that the proposed MBP algorithm has better performance than or comparable performance with state-of-the-art local feature based methods but with significantly lower time and space complexity.

关键词： Face Histograms Face recognition Feature extraction Databases Complexity theory Filtering

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3-dimensional pattern recognition using weightless neural networks (Aleksander's model)

3-dimensional pattern recognition using weightless neural ne...

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international Joint conference on Neural Networks (IJCNN)

作者： J. Figueroa-Nazuno A. Vazquez-Nava E. Vargas Medina Lab. de Sistemas Complejos Univ. La Salle Mexico Laboratorio de Sistemas Complejos Universidad de La Salle Mexicali Mexico

A system composed of a weightless neural network (Aleksander's model) is described. This system performs 2D and 3D recognition of patterns which are drawn with five different gray tones or colors. The system is capable of recognizing from 2 to 15 different pattern classes. The authors describe the structure and behavior of this system during the learning and recognition phases.< >

关键词： pattern recognition Neural networks Artificial neural networks Sampling methods Probability distribution Laboratories Bismuth Learning Artificial intelligence Distributed computing

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An improvement on the Euler number computing algorithm used in MATLAB

An improvement on the Euler number computing algorithm used ...

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IEEE Region 10 international conference TENCON

作者： Bin Yao Hua Wu Yun Yang Yuyan Chao Lifeng He Shaanxi University of Science and Technology Xi'an Shaanxi CN Nagoya Sangyo Daigaku Owariasahi Aichi JP Aichi Kenritsu Daigaku Nagakute Aichi JP

ISBN: (纸本)9781479928286

Computation of the Euler number of a binary image is often necessary in image matching, image database retrieval, image analysis, pattern recognition, and computer vision. This paper proposes an improvement on the Euler number computing algorithm used in the famous image processing tool MATLAB. By use of the information obtained during processing the previous pixel, the number of times of checking the neighbor pixels for processing a pixel decrease from 4 to 2. Our method is very simple in principle, and easily implemented. The experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms.

关键词： Algorithm design and analysis pattern recognition Noise Labeling Image processing MATLAB Educational institutions

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