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

共 2695 条记录，以下是1761-1770 订阅

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The Random neural Network and its learning process in Cognitive Packet networks

The Random Neural Network and its learning process in Cognit...

引用

International conference on Natural Computation (ICNC)

作者： Peixiang Liu Graduate School of Computer and Information Sciences Nova Southeastern University Fort Lauderdale FL USA

The Random neural Network (RNN) is a recurrent neural network in which neurons interact with each other by exchanging excitatory and inhibitory spiking signals. The stochastic excitatory and inhibitory interactions in the network make the RNN an excellent modeling tool for various interacting entities. It has been applied in a number of applications such as optimization, image processing, communication systems, simulation pattern recognition and classification. In this paper, we briefly describe the RNN model and some learning algorithms for RNN. We discuss how the RNN with reinforcement learning was successfully applied to Cognitive Packet Network (CPN) architecture so as to offer users QoS driven packet delivery services. The experiments conducted on a 26-node testbed clearly demonstrated the learning capability of the RNNs in CPN.

关键词： Neurons Quality of service Routing Mathematical model Delays Learning (artificial intelligence) neural networks

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Application image processing to predict personality based on structure of handwriting and signature

Application image processing to predict personality based on...

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International conference on Computer, Control, Informatics and Its applications (IC3INA)

作者： Esmeralda Contessa Djamal Risna Darmawati Sheldy Nur Ramdlan Department of Informatics Universitas Jenderal Achmad Yani Cimahi Indonesia

Handwriting stroke reflects how the author faced his world and the emotional honesty. By examining all elements of handwriting and interpreting them separately or integrated, we could generate a sketch of the writer's character traits, emotional disposition and social style using standard of graphology. As image, the analysis of graphology is divided into two approaches that graphics features and segmentation digit each character. In this research, using graphical approach based on a combination of signature and handwriting to predict the more personality using structure algorithms and multiple artificial neural networks (ANN). The image in A4 paper split into two areas: Signature area which nine features and handwriting based on five features. Each area had pre-processing performed to improve the recognition accuracy. Signature area is classified using ANN based on five features and using multi structure algorithms based on four features. While the handwriting area is classified using multi structure algorithm based on four features (margins, spacing between words and lines, and zone domination) and using ANN after hill valley extraction based on baseline features. Eight features are processed using multi-structure algorithms that provide 87-100% accuracy. In the meantime, six features are classified using an ANN which result an accuracy of 52-100%. It used 100 sets of data testing after training using back propagation with 25-75 data. The system has been implemented with the software so that it can be used for classification of personality from handwriting scanned automatically.

关键词： artificial neural networks Feature extraction Accuracy Pattern recognition Neurons image segmentation Classification algorithms

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Modeling Approaches and Algorithms for Advanced Computer applications 1

引用

丛书名： Studies in Computational Intelligence

2013年

作者： Frédéric Boniol (auth.), Abdelmalek Amine, Ait Mohamed Otmane, Ladjel Bellatreche (eds.)

ISBN: (数字)9783319005607

ISBN: (纸本)9783319005591;9783319005607

"During the last decades Computational Intelligence has emerged and showed its contributions in various broad research communities (computer science, engineering, finance, economic, decision making, etc.). This was done by proposing approaches and algorithms based either on turnkey techniques belonging to the large panoply of solutions offered by computational intelligence such as data mining, genetic algorithms, bio-inspired methods, Bayesian networks, machine learning, fuzzy logic, artificial neural networks, etc. or inspired by computational intelligence techniques to develop new ad-hoc algorithms for the problem under consideration. This volume is a comprehensive collection of extended contributions from the 4th International conference on Computer Science and Its applications (CiiA2013) organized into four main tracks: Track 1: Computational Intelligence, Track 2: Security & Network Technologies, Track 3: Information Technology and Track4: Computer Systems and applications. This book presents recent advances in the use and exploitation of computational intelligence in several real world hard problems covering these tracks such as image processing, Arab text processing, sensor and mobile networks, physical design of advanced databases, model matching, etc. that require advanced approaches and algorithms borrowed from computational intelligence for solving them.

关键词： Computer science

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Optimization of ANPR algorithm on android mobile phone

Optimization of ANPR algorithm on android mobile phone

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IEEE International conference on Smart Instrumentation, Measurement and applications (ICSIMA)

作者： Abdul Mutholib Teddy S. Gunawan Jalel Chebil Mira Kartiwi Electrical Computer Engineering Department International Islamic University Malaysia Kuala Lumpur Malaysia Information System Department International Islamic University Malaysia Kuala Lumpur Malaysia

Since the past decades, many researchers proposed their methods to recognize the vehicle number plate. One of the methods is template matching which is executed in the optical character recognition (OCR) step of the automatic number plate recognition (ANPR) system. In previous researches, many researchers are used a high end desktop PC and high resolution camera to implement the ANPR system. In this paper, the optimization of ANPR algorithm on limited hardware of Android mobile phone is presented. First, various steps to optimize ANPR and OCR block using template matching are described. Our proposed algorithm was based on Tesseract library. For comparison purpose, the template matching based OCR will be compared to artificial neural Network (ANN) based OCR. The optimization on ANPR was performed as currently there is no image processing tool available on the standard Android mobile phone. By optimization of ANPR, many advantages could be achieved, such as higher recognition accuracy, less resource consumption, and less computational complexity. Results on 30 images showed that the recognition rate was 97.46% while the processing time was 1.13.

关键词： Optimization Optical character recognition software Character recognition Smart phones Licenses artificial neural networks Accuracy

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Individual processing speed analysis for traffic sign detection and recognition

Individual processing speed analysis for traffic sign detect...

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IEEE International conference on Smart Instrumentation, Measurement and applications (ICSIMA)

作者： Nursabilillah Mohd Ali Nur Maisarah Mohd Sobran Syahar Azalia Ab. Shukur Mariam Md Ghazaly Ahmad Fayeez Tuani Ibrahim Department of Mechatronic Engineering University Teknikal Malaysia Melaka Malaysia Department of Mechatronic Engineering Faculty of Electrical Engineering University Teknikal Malaysia Melaka Malaysia

Of late, traffic sign detection and recognition are becoming very prevalent topic as it enhances drivers towards safety and alert them with precaution information. This study reports about processing time of the individual color detection and recognition of the partial occlusion traffic sign that have been previously implemented using HSV and RGB color ratio and ANN and PCA method respectively for detection and recognition. The data set for detection and classification process has been successfully created in various places in Malaysia that involved with degradation and out of planes rotated of the signs. There are three standard types of colored images have been used in the study namely Red, Blue and Yellow signs. In this study, we analyze the system processing speed of individual color detection and classification respectively using red, green and blue (RGB) and hue, saturation and value (HSV) color segmentation techniques, supervised feed forward artificial neural network (ANN) and principal component analysis (PCA). The experimental result shown that processing time of individual color detection during daytime and at night using HSV method is slightly faster than RGB technique. On the other hand, supervised feed forward neural network has reached almost 1s in recognizing traffic sign images rather than PCA with only 0.0238s.

关键词： image color analysis Vehicles Principal component analysis artificial neural networks Roads Accidents Hardware

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Implementation of online optical character recognition system using embedded DSP platform

Implementation of online optical character recognition syste...

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International conference on Advanced Electronic Systems (ICAES)

作者： Nirmal Savio Paul J Suriya Prakash Student Trainee CSIR-CEERI Chennai Centre CSIR Madras Complex Chennai India Central Electronics Engineering Research Institute CSIR Pilani Rajasthan IN

This paper describes the implementation of an Optical Character Recognition (OCR) system using embedded DSP hardware and software platforms. The system utilizes an area-scan camera and the HALCON image processing library running upon the Strampe VisionBox hardware platform programmed through the Code Composer Studio IDE. This prototype system can be modified for several potential applications such as sorting of packed food based on expiry date.

关键词： Optical character recognition software Cameras Character recognition artificial neural networks Hardware Digital signal processing Optical imaging

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Application of the artificial neural Network for blood pressure evaluation with smartphones

Application of the Artificial Neural Network for blood press...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Francesco Lamonaca Kurt Barbe Yuriy Kurylyak Domenico Grimaldi Wendy Van Moer Angelo Furfaro Vitaliano Spagnuolo Department of Computer Science Universiy of Calabria. Rende (Cs) Ttaly Department of Fundamental Electricity and Instrumentation (M2ESA) Vrije Universiteit Brussel Brussels Belgium Clinical and Therapeutic Unit of Arterial Hypertension - Unit of Internal Medicine-AO of Cosenza Italy

The smartphone is proposed to evaluate the Blood Pressure (BP) anywhere and anytime. The tasks performed by smartphone are (i) extraction of the PhotoPlethysmoGram (PPG) signal from a frame sequence acquired by the integrated camera, and (ii) processing it by artificial neural Network for the evaluation of the BP. The PPG signal is evaluated by analyzing the volumetric blood variation of the fingertip on the frame sequence. Successively, parameters characterizing the pulses of the PPG signal are sent to the Fit Forward neural Network for the simultaneously evaluation of the systolic and the diastolic BP. The validation of the results is performed by comparing them with the ones obtained by the Ambulatory Blood Pressure monitor ABP Spacelabs 90207. Preliminary experimental results show useful information to address the future research devoted to reduce the maximum error.

关键词： Smart phones Biomedical monitoring artificial neural networks Blood pressure Cameras Monitoring Training

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The impact of transformation function on the classification ability of complex valued extreme learning machines

The impact of transformation function on the classification ...

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International conference on Control Computing Communication & Materials (ICCCCM)

作者： Ram Govind Singh Nikhitha Kishore Computer Science & Engineering United Institute of Technology Allahabad India Computer Science & Engineering Maulana Azad National Institute of technology Bhopal India

Classification is a rather omnipresent problem in many of the technological areas ranging from image processing to medical applications. With complex-valued neural network classifiers posing better decision making capabilities due to its orthogonal decision boundaries and it's comparatively better computational capability many complex valued neural network (CVNN) classifiers has been presented in literature. In this paper a review on the state of the art on a family of CVNNs known as complex valued extreme learning machines (CELM) is presented. With their better generalization ability and lesser computational efforts for classification problems CELMs provide a better solution for real-valued classification problems. The four CELMs that is used for solving real valued classification problems namely, Circular CELM (CC-ELM), Phase encoded CELM (PE-CELM), Bilinear Branch cut CELM (BB-CELM) and Fast Learning Complex valued neural Classifier (FLCNC). The evaluations are done based on the datasets available in the UCI repository. Through this study it could proved that the synergy between the ELM and CVNN has brought better results in the classification arena.

关键词： Accuracy Biological neural networks Testing Classification algorithms Materials artificial neural networks Equations

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The smart device for healthcare service: Iris diagnosis application

The smart device for healthcare service: Iris diagnosis appl...

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International conference on ICT and Knowledge Engineering

作者： David Habsara Hareva Samuel Lukas Natanael Oktavian Suharta Informatics Universitas Pelita Harapan Tangerang Indonesia

We have developed an iridology application for predicting a person health through analysis of iris image. Commonly for commercial purpose, it used Desktop-PC, but we embedded it on Android-based Smartphone. The objective of this research is to find out whether healthcare application that providing direct diagnosis from real-time capturing image can be carried out through mobile phone in general. For that purpose, we developed other version of application that run on windows mobile and windows operating system. The algorithm and structure of the application is made as closely as possible with a previous application. Those applications have been tested on different environment, such as operating system, programming language, and mobile device. The device tested with regard to product maker, processor speed, screen size, storage size, and image retrieval using built-in camera. Although the results can be predicted that the used device with high specifications will be faster to process image analysis, better to obtain images, clearly in displaying images, however finding out the low limit of requirement for running the application is essential. It is found using low camera specification to take the image of iris is not recommended. Additional macro lens causes difficulty when taking a picture. processing of iris image to predict health condition could be handled by 1 GHz processor, 512 internal memories, and 3.5 inches screen size, even it used low-end product.

关键词： Iris artificial neural networks Lungs image edge detection Mobile communication Liver Heart

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RANDOM FOREST CLASSIFICATION FOR P300 BASED BRAIN COMPUTER INTERFACE applications

RANDOM FOREST CLASSIFICATION FOR P300 BASED BRAIN COMPUTER I...

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European Signal processing conference

作者： Faisal Farooq Preben Kidmose Department of Engineering Aarhus University

ISBN: (纸本)9781479936878

One of the most successful types of brain computer interfaces (BCI) is based on the P300 evoked potential (EP) elicited by oddball type of paradigms. Given a particular paradigm the main challenge is to obtain an efficient and robust classification. This paper proposes the use of Random Forest (RF), a tree based ensemble learning method providing state-of-the-art generalization performance, for P300 BCI classification. The performance of the proposed method is compared to both the most commonly used classifiers for this problem: the support vector machine (SVM), and the step-wise linear discriminant analysis (SWLDA);to two state-of-the-art methods: the multiple convolutional neural networks (MCNN) and the ensemble support vector machine (ESVM). The proposed method has been evaluated on two public available BCI datasets: the BCI competition dataset ii for healthy subjects and the image driven paradigm dataset for disabled subjects. The proposed method demonstrated a significant improvement in classification accuracy on both datasets.

关键词： Brain Computer Interfaces (BCIs) Evoked potential (EP) Random Forest (RF)

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