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International conference on Communications, Circuits and Systems (ICCCAS)

作者： Jui-Lin Lai Zhen-Xuan Guan Yan-Ting Chen Cheng-Fang Tai Rong-Jian Chen Department of Electronic Engineering National United University Miaoli Taiwan

The architecture of the multiplicative type-ii fuzzy cellular neural networks (FCNN) with CMOS image sensor is implemented, which is with local connectivity advantageous suitable implemented for VLSI. Base on the proposed FCNN structure which is included the neuron, Min/Max, analog multiplier, pixel and CDS circuit, S/H Circuit, transfer and control circuits. The system has capability to operate the various morphological operations for binary and gray-level image, such as Dilation and Erosion. The simulation results show that the FCNN can operated the specific functions depend on the selected template is successfully verified by the TSMC 0.35 mum 2P4M CMOS technology. There have a great potential in the VLSI implementation of neural network systems for binary and gray-level patterns in image-processing applications.

关键词： Pixel CMOS integrated circuits Cellular neural networks Morphological operations Gray-scale CMOS technology artificial neural networks

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Wavelets pre-processing of artificial neural networks classifiers

Wavelets pre-processing of Artificial Neural Networks classi...

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IEEE SSD International Multi-conference on Systems, Signals and Devices

作者： Ali Al-Haj Department of Computer Engineering King Abdullah II School for Electrical Engineering Princess Sumaya University for Technology Amman Jordan

artificial neural networks are highly parallel structures inspired by the human brain. They have been used successfully in many human-like applications, such as pattern recognition. Performance of these networks can be enhanced if used properly in conjunction with equally powerful mathematical tools. In this paper, we used the discrete wavelet transform as a pre-processing tool for two well-known neural classifiers; competitive layer networks and learning vector networks. The wavelets transform was used successfully to approximate the input patterns of the two classifiers and thus reduced their input-layer requirements considerably. Such reduction facilitates cost-effective hardware implementations of artificial neural networks.

关键词： artificial neural networks Discrete wavelet transforms Training Approximation methods Support vector machine classification Neurons Transforms

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Classification of cylindrical targets by wavelet transform and ROC analysis

Classification of cylindrical targets by wavelet transform a...

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IEEE Signal processing and Communications applications (SIU)

作者： Senem Makal Lutfiye Durak Ahmet Kizilay Elektronik ve Haberleme Muhendisligi Bolumu Yildiz Teknik Universitesi Istanbul Turkey

A set of features are derived from scattered fields calculated by using the image technique formulation and method of moment (MoM) and a database is formed by using two cylindrical targets at certain angles. After the application of wavelet transform for feature extraction from this database, the coefficients of the signal are used as the inputs of the artificial neural networks. The real performances of the networks are investigated by ROC (receiver operating characteristic) analysis. This work aims to diminish the size of the database smaller by wavelet transform for finding the corresponding cylindrical target from the scattered field values.

关键词： artificial neural networks Wavelet analysis Electromagnetic scattering Wavelet transforms Biomedical imaging Databases Transforms

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neural network model of S-parameters for a dielectric post in rectangular waveguide

Neural network model of S-parameters for a dielectric post i...

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International conference on Recent Advances in Microwave Theory and applications, MICROWAVE

作者： Manidipa Bhattacharya B Gupta SAMEER Kolkata India Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India

artificial neural networks (ANN) are popular as efficient alternatives to conventional computational models like numerical modeling or analytical methods for RF and Microwave modeling and design[1]. Such models reduce both processing time and analytical complexity resulting in simpler computation process. Thus the technique has greater potential in the analysis of various problems in electromagnetism. In the present problem neural network technique has been used for non-linear modeling of the frequency response of the scattering properties of a circular dielectric cylinder in a three dimensional rectangular waveguide at X band. The scattering properties of the vertical posts are analyzed by semi analytical approach based on image theory and the scattered fields are calculated from the lattice sum and the transition matrices. Analysis results have been used to generate S parameter data for the configuration in order to train the proposed network.

关键词： neural networks Scattering parameters Dielectrics Rectangular waveguides artificial neural networks Numerical models image analysis Computer networks Computational modeling Analytical models

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image registration based on neural network

Image registration based on neural network

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IEEE EMBS International conference on Information Technology applications in Biomedicine (ITAB)

作者： Shuxiu Wang Sheng Lei Faliang Chang Institute of Control Science and Engineering Shandong University China College of Biomedical Engineering & Instrument Science University of Zhejiang China

image registration is to seek a spatial alternation which can make a 2D or 3D image to consist with another 2D or 3D image in space; it is an important technique in image processing. The concept of image registration is introduced in detail in this paper, and the main emphasis is how to choose the feature points. The author raises a new algorithm to choose the feature point, it is easy and fast. At the same time to solve the nonlinear distorting using direct mapping transformation method, this paper registers image based on neural network. By simulations of MRI images, it demonstrates that the new algorithm is of great effectiveness.

关键词： image registration neural networks Nonlinear distortion Feature extraction artificial neural networks Parameter estimation Human anatomy Data mining Mutual information Information technology

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image clustering with spiking neuron network

Image clustering with spiking neuron network

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International Joint conference on neural networks (IJCNN)

作者： B. Meftah A. Benyettou O. Lezoray W. QingXiang Equipe EDTEC (LRSBG) Centre Universitaire Mustapha Stambouli Mascara Algerie Laboratoire Signal Image et Parole (SIMPA) Universite Mohamed Boudiaf Oran Algerie GREYC UMR CNRS 6072 Université de Caen Basse Normandie Caen France GREYC UMR CNRS 6072 Univcrsite de Caen Caen France School of Computing and Intelligent Systems University of Ulster at Magee Derry Northern Ireland UK

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often referred to as the 3 rd generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation.

关键词： Neurons image segmentation artificial neural networks Training Pixel Encoding Firing

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image quality assessment using edge and contrast similarity

Image quality assessment using edge and contrast similarity

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International Joint conference on neural networks (IJCNN)

作者： Wei Fu Xiaodong Gu Yuanyuan Wang Department of Electronic Engineering Fudan University Shanghai China

Measurement of visual quality is of fundamental importance to some image processing applications. And the perceived image distortion of any image strongly depends on the local features, such as edges, flats and textures. Since edges often convey much information of an image, we propose a novel algorithm for image quality assessment based on the edge and contrast similarity between the distorted image and the reference(perfect) image. We demonstrate its promise through a set of intuitive examples, as well as validate its performance with subjective ratings. We also compare our method with two other state-of-the-art objective ones, which uses 550 images with different distortion types and BP neural network.

关键词： image edge detection image quality Nonlinear distortion PSNR Indexes Humans artificial neural networks

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image Segmentation with Spiking Neuron Network

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AIP conference Proceedings 2008年第1期1019卷 15-19页

作者： B. Meftah A. Benyettou O. Lezoray M. Debakla aEquipe EDTEC (LRSBG) Centre Universitaire Mustapha Stambouli Mascara Algérie bLaboratoire SIMPA BP 1505 Université Mohamed Boudiaf (USTO) Oran Algérie cUniversité de Caen GREYC UMR CNRS 6072 6 Bd. Maréchal Juin F‐14050 Caen France

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron networks (SNNs) are often referred to as the 3rd generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event‐driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neuralnetworks. In this paper, we present how SNN can be applied with efficacy in image segmentation.

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Tracking Multiple Insects Using Multilayer Feed-Forward networks

Tracking Multiple Insects Using Multilayer Feed-Forward Netw...

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International conference on Signal processing, Communication and Networking (ICSCN)

作者： N. Ravi Kumar T. N. Janakiraman Hemalatha Thiagarajan K. Subaharan Department of Computer Applications National Institute of Technology Tiruchirappalli India Dept. of Computer Applications National Institute of Technology Trichy Dept. of Mathematics National Institute of Technology Trichy Department of Mathematics National Institute of Technology Tiruchirappalli India Central Plantation Crops Research Institute Kasaragod Kerala India

In the present study, attempts are made to capture and track coconut black headed caterpillar, Opisina arenosella and its parasitoid, Goniozus nephantidis with respect to their path and orientation. We devised an automatic tracking system using artificial neural Network for tracking both insects. The tracking system is based on the extracted features of the insects. Using the geometry of the image we have proposed a method to separate the two insects when they are joined in the image in the course of their motion. A fixed multilayer feed-forward backpropagation network (FMFBPN) was employed to solve the correspondence problem between frames. After establishing correspondence, the traced paths are plotted and length of the path of each insect is computed.

关键词： Insects Nonhomogeneous media Feedforward systems artificial neural networks Feature extraction Video compression Mathematics Crops Shape Computer networks

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A neural network approach for contrast enhancement image

A neural network approach for contrast enhancement image

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International conference on Electronic Design (ICED)

作者： A.S.W. Wahab M.Y. Mashor Zaleha Salleh S. A. Abdul Shukor N. Abdul Rahim F. Muhamad Idris H. Hasan S.S. Noor School of Computer Engineering Universiti Malaysia Perlis Jejawi Arau Perlis Malaysia School of Mechatronic Engineering Universiti Malaysia Perlis Jejawi Arau Perlis Malaysia Department of Microbiology Universiti Sains Malaysia Kubang Kerian Kelantan Malaysia

Tuberculosis infection is a serious disease which could be controlled by early diagnosis. A commonly used technique for detecting the TB bacilli is by analyzing sputum smear. Now days, image recognition systems have several applications in enormous fields. This paper uses an artificial neural network to enhance color images of Ziehl-Neelsen stained smear for the purpose of detecting TB bacilli. The first necessary step is the captured images are converted into usable format (RGB values) and pass the RGB values to neural network for training to emulate the contrast enhancement technique. The training is based on back-propagation algorithm. It is found that the proposed neural network approach could emulate contrast enhancement technique quite well.

关键词： neural networks artificial neural networks image processing Biological neural networks image enhancement Biomedical imaging Neurons Diseases image quality Pattern recognition

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