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International conference on Communication Software and networks, ICCSN

作者： Zhengmao Ye Habib Mohamadian Yongmao Ye Southern University Baton Rouge LA USA Liaoning TV Station Shenyang Liaoning China

Digital image compression has numerous practical applications to effectively utilize storage capacities. Two basic techniques are lossless and lossy compression. The former employs probabilistic models for lossless storage on a basis of statistical redundancy occurred in digital images. However, it has limitation in compression ratio. Thus, the latter has been widely applied to further improve storage capacities, covering various fundamental digital image processing approaches. It has been documented that most lossy compression schemes will provide perfect visual perception under an exceptional compression ratio, among which discrete wavelet transform (DWT), discrete Fourier transform (DFT) and statistical optimization compression schemes (e.g., principal component analysis or independent component analysis) are dominant approaches. It is necessary to evaluate these compression and reconstruction schemes objectively. Using a well defined set of quantitative metrics by means of Information Theory, a comparative study on several typical image compression and reconstruction schemes will be made in this research.

关键词： Manganese Biomedical imaging artificial neural networks Discrete wavelet transforms image reconstruction

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Capsule image segmentation in pharmaceutical applications using edge-based techniques

Capsule image segmentation in pharmaceutical applications us...

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IEEE International conference on Electro-Information Technology

作者： Mohammed J. Islam Saleh Basalamah Majid Ahmadi Maher A. Sid-Ahmed College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Department of Electrical and Computer Engineering University of Windsor Windsor ONT Canada

Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of grey levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels that is used as an input to the high throughput quality inspection system. In this paper, we develop a system using edge-based image segmentation techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications.

关键词： Inspection image segmentation image edge detection Computers artificial neural networks Pixel Pharmaceuticals

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neural network based handwritten character recognition system without feature extraction

Neural network based handwritten character recognition syste...

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International conference on Computer, Communication and Electrical Technology (ICCCET)

作者： J. Pradeep E. Srinivasan S. Himavathi Department of ECE Pondicherry Engineering College Pondicherry India Department of EEE Pondicherry Engineering College Pondicherry India

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30×20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition.

关键词： Character recognition Handwriting recognition artificial neural networks image segmentation Pixel Accuracy Feature extraction

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image processing technique for segmenting microstructural porosity of laser-welded thermoplastics

Image processing technique for segmenting microstructural po...

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IEEE International conference on Electronics, Circuits and Systems (ICECS)

作者： Karl Leboeuf Iman Makaremi Roberto Muscedere Majid Ahmadi Department of Electrical and Computer Engineering University of Windsor Windsor ONT Canada

Plastics are used in a truly vast number of applications, and research is continously carried out to improve every aspect of the plastics industry. A recent study of laser transmission welding [1] required cross-sectional images of the weld's microstructure to be analyzed for the presence of pores, which are tiny bubbles that may form during the weld process. It is believed that the number and size of pores may be indicative of the weld strength [1]. The current state of the art for detecting these pores involves manually drawing a contour around each one; a laborious process given that a typical sample may have hundreds-to-thousands of pores. This paper presents a segmentation system for classifying the pixels of a microstructural image of a thermoplastic laser weld as either belonging to a pore or the background. The algorithm is robust in terms of dealing with noise from flbreglass strands, cloudy pores, and varying exposure time. On average, it is estimated that the proposed algorithm is able to correctly classify pores at a rate of approximately 90% without requiring any user intervention.

关键词： image segmentation Welding artificial neural networks Training Humans Noise Neurons

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Select and Sample - A Model of Efficient neural Inference and Learning 11

Select and Sample - A Model of Efficient Neural Inference an...

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Annual conference on neural Information processing Systems

作者： Jacquelyn A. Shelton Joerg Bornschein Abdul-Saboor Sheikh Pietro Berkes Joerg Luecke Frankfurt Institute for Advanced Studies Goethe-University Frankfurt Germany Volen Center for Complex Systems Brandeis University Boston USA

ISBN: (纸本)9781618395993

An increasing number of experimental studies indicate that perception encodes a posterior probability distribution over possible causes of sensory stimuli, which is used to act close to optimally in the environment. One outstanding difficulty with this hypothesis is that the exact posterior will in general be too complex to be represented directly, and thus neurons will have to represent an approximation of this distribution. Two influential proposals of efficient posterior representation by neural populations are: 1) neural activity represents samples of the underlying distribution, or 2) they represent a parametric representation of a variational approximation of the posterior. We show that these approaches can be combined for an inference scheme that retains the advantages of both: it is able to represent multiple modes and arbitrary correlations, a feature of sampling methods, and it reduces the represented space to regions of high probability mass, a strength of variational approximations. neurally, the combined method can be interpreted as a feed-forward preselection of the relevant state space, followed by a neural dynamics implementation of Markov Chain Monte Carlo (MCMC) to approximate the posterior over the relevant states. We demonstrate the effectiveness and efficiency of this approach on a sparse coding model. In numerical experiments on artificial data and image patches, we compare the performance of the algorithms to that of exact EM, variational state space selection alone, MCMC alone, and the combined select and sample approach. The select and sample approach integrates the advantages of the sampling and variational approximations, and forms a robust, neurally plausible, and very efficient model of processing and learning in cortical networks. For sparse coding we show applications easily exceeding a thousand observed and a thousand hidden dimensions.

关键词： Learning Sparse coding state space parametric representation sensory stimulation Combined Method sampling methods Probability distribution approximation Markov chain monte carlo methods

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Medical image analysis with artificial neural networks

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2010年第8期34卷 617-631页

作者： Jiang, J. Trundle, P. Ren, J. Univ Bradford Digital Media & Syst Res Inst Bradford BD7 1DP W Yorkshire England

Given that neural networks 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 neural networks 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 neural networks and (iii) how neural networks 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 neural networks in medical Imaging (C) 2010 Elsevier Ltd All rights reserved

关键词： neural networks Medical imaging analysis Intelligent computing

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Semantic Analysis of Skin Lesions Using Radial Basis Function neural networks

Semantic Analysis of Skin Lesions Using Radial Basis Functio...

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3rd Annual International conference on Human System Interaction (HSI)

作者： Przystalski, Karol Nowak, Leszek Ogorzalek, Maciej Surowka, Grzegorz Jagiellonian Univ Krakow Poland

ISBN: (纸本)9781424475629

artificial neural networks have been successfully applied to abroad spectrum of complex analysis problems. Computational intelligence is finding more and more applications in computer aided diagnostics, helping doctors to process large quantities of various medical data. In dermatology it is extremely difficult to perform automatic diagnostic differentiation of malignant melanoma based only on dermatoscopic images. Applying artificial intelligence algorithms to explore and search large database of dermatoscopic images allow doctors to semantically filter out image with specified characteristics. This paper presents an approach for characteristic objects classification found in image database of pigment skin lesions, based on radial basis function kernel for artificial neural networks.

关键词： artificial neural networks image analysis image processing malignant melanoma neural networks radial basis function semantic semantic analysis

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Removing an Object from Video Sequence Algorithm Implemented on Analog CNN and DSP Microprocessors

Removing an Object from Video Sequence Algorithm Implemented...

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20th International conference on artificial neural networks

作者： Arslan, Emel Arik, Sabri Istanbul Univ Res & Applicat Ctr Comp Sci Istanbul Turkey Istanbul Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9783642158186

CNN Universal Machine which contains Digital Signal Processor (DSP) in addition to ACE16k which is the hardware verification of Cellular neural networks are used increasingly in image processing applications with their advanced computational features. In this study, an removing an object from video sequence algorithm on a Bi-i Cellular Vision System which is a CNN Universal Machine was applied and the running times of both processors were evaluated.

关键词： image processing Cellular neural networks Digital Signal Processor ACE16k Bi-i Cellular Vision System CNN Universal Machine

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Biospeckle image Stack Process based on artificial neural networks

Biospeckle Image Stack Process based on Artificial Neural Ne...

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32nd Annual International conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Meschino, Gustavo Murialdo, Silvia Passoni, Lucia Rabal, Hector Trivi, Marcelo

ISBN: (纸本)9781424441242

This paper proposes the identification of regions of interest in biospeckle patterns using unsupervised neural networks of the type Self-Organizing Maps. Segmented images are obtained from the acquisition and processing of laser speckle sequences. The dynamic speckle is a phenomenon that occurs when a beam of coherent light illuminates a sample in which there is some type of activity, not visible, which results in a variable pattern over time. In this particular case the method is applied to the evaluation of bacterial chemotaxis. image stacks provided by a set of experiments are processed to extract features of the intensity dynamics. A Self-Organizing Map is trained and its cells are colored according to a criterion of similarity. During the recall stage the features of patterns belonging to a new biospeckle sample impact on the map, generating a new image using the color of the map cells impacted by the sample patterns. It is considered that this method has shown better performance to identify regions of interest than those that use a single descriptor. To test the method a chemotaxis assay experiment was performed, where regions were differentiated according to the bacterial motility within the sample.

关键词： artificial neural networks bacterial chemotaxis bacterial motility biomedical optical imaging biospeckle image stack process cell motility feature extraction feature extraction image segmentation image segmentation image sequences laser applications in medicine laser speckle sequences medical image processing microorganisms self-organising feature maps self-organizing maps unsupervised learning unsupervised neural networks

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Design of artificial neural networks Using Differential Evolution Algorithm

Design of Artificial Neural Networks Using Differential Evol...

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17th International conference on neural Information processing

作者： Garro, Beatriz A. Sossa, Humberto Vazquez, Roberto A. IPN Ctr Invest Computac Av Juan de Dios Batiz Mexico City 07738 DF Mexico La Salle Univ Escuela Ingn Mexico City DF 06140 Mexico

ISBN: (纸本)9783642175336

The design of an artificial neural Network (ANN) is a difficult task for it depends on the human experience. Moreover it needs a process of testing and error to select which kind of a transfer function and which algorithm should be used to adjusting the synaptic weights in order to solve a specific problem. In the last years, bio-inspired algorithms have shown their power in different non-linear optimization problems. Due to their efficiency and adaptability, in this paper we explore a new methodology to automatically design an ANN based on the Differential Evolution (DE) algorithm. The proposed method is capable to find the topology, the synaptic weights and the transfer functions to solve a given pattern classification problems.

关键词： neural networks

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