This book constitutes the refereed proceedings of the 8th IFIP WG 12.5 International conference on artificial Intelligence applications and Innovations, AIAI 2012, held in Halkidiki, Greece, in September 2012. The 44 ...
ISBN:
(数字)9783642334092
ISBN:
(纸本)9783642334085
This book constitutes the refereed proceedings of the 8th IFIP WG 12.5 International conference on artificial Intelligence applications and Innovations, AIAI 2012, held in Halkidiki, Greece, in September 2012. The 44 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on ANN-classification and pattern recognition, optimization - genetic algorithms, artificialneuralnetworks, learning and mining, fuzzy logic, classification - pattern recognition, multi-agent systems, multi-attribute DSS, clustering, image-video classification and processing, and engineering applications of AI and artificialneuralnetworks.
A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificialneuralnetworks is presented in this work. The 1D descriptors were computed by two methods, firs...
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ISBN:
(纸本)9780819492166
A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificialneuralnetworks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.(1) and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation;most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation
In this paper, we solve the impulse noise detection problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent impulse noise detector. MLMVN was a...
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ISBN:
(纸本)9780819489425
In this paper, we solve the impulse noise detection problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent impulse noise detector. MLMVN was already used for point spread function identification and intelligent edge enhancement. So it is very attractive to apply it for solving another imageprocessing problem. The main result, which is presented in the paper, is the proven ability of MLMVN to detect impulse noise on different images after a learning session with the data taken just from a single noisy image. Hence MLMVN can be used as a robust impulse detector. It is especially efficient for salt and pepper noise detection and outperforms all competitive techniques. It also shows comparable results in detection of random impulse noise. Moreover, for random impulse noise detection, MLMVN with the output neuron with a periodic activation function is used for the first time.
With the advancement in technology, we see that complex-valued data arise in many practical applications, specially in signal and imageprocessing. In this paper, we introduce a new application by generating complex-v...
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ISBN:
(纸本)9781467314909
With the advancement in technology, we see that complex-valued data arise in many practical applications, specially in signal and imageprocessing. In this paper, we introduce a new application by generating complex-valued dataset that represents various hand gestures in complex domain. The system consists of three components: real time hand tracking, hand-skeleton construction, and hand gesture recognition. A complex-valued neural network (CVNN) having one hidden layer and trained with Complex Levenberg-Marquardt (CLM) algorithm has been used to recognize 26 different gestures that represents English Alphabet. The result shows that the CLM provides reasonable recognition performance. In addition to that, a comparison among different activation functions have been presented.
This book constitutes the refereed proceedings of the International conference on artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. The 163 revised full papers...
ISBN:
(数字)9783642342400
ISBN:
(纸本)9783642342394
This book constitutes the refereed proceedings of the International conference on artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. The 163 revised full papers presented were carefully reviewed and selected from 724 submissions. The papers are organized in topical sections on applications of artificial intelligence; applications of computational intelligence; data mining and knowledge discovering; evolution strategy; intelligent imageprocessing; machine learning; neuralnetworks; pattern recognition.
Segmentation is a fundamental component of many image-processingapplications. Various algorithms were proposed so far for segmentation of plant disease images. The researchers raised some corresponding solutions to d...
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ISBN:
(纸本)9783037854488
Segmentation is a fundamental component of many image-processingapplications. Various algorithms were proposed so far for segmentation of plant disease images. The researchers raised some corresponding solutions to different characteristics of disease spot, and these algorithms are continually improved to enhance the speed and veracity. Based on current progress, this paper gives a study on the image segmentation classification. In addition, this article also makes a comprehensive expatiation on how to solve the problem of plant disease spot by using image segmentation techniques. In the end, open problems and future trend of segmentation algorithm were discussed.
The developed model consists of a multilayer neural network with receptive fields used to estimate the local direction of the neuron on a fragment of microscopy image. It can be used in a wide range of classical neuro...
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ISBN:
(纸本)9783642293498;9783642293504
The developed model consists of a multilayer neural network with receptive fields used to estimate the local direction of the neuron on a fragment of microscopy image. It can be used in a wide range of classical neuron reconstruction methods (manual, semi-automatic, local automatic or global automatic), some of which are also outlined in this paper. The model is trained on an automatically generated training set extracted from a provided example image stack and corresponding reconstruction file. During the experiments the model was tested in simple statistical tests and in real applications, and achieved good results. The main advantage of the proposed approach is its simplicity for the end-user, one who might have little or no mathematical/computer science background, as it does not require any manual configuration of constants.
The estimation of the volume occupied by an object is an important task in the fields of granulometry, quality control, and archaeology. An accurate and well know technique for the volume measurement is based on the A...
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ISBN:
(纸本)9781467314909
The estimation of the volume occupied by an object is an important task in the fields of granulometry, quality control, and archaeology. An accurate and well know technique for the volume measurement is based on the Archimedes' principle. However, in many applications it is not possible to use this technique and faster contact-less techniques based on imageprocessing or laser scanning should be adopted. In this work, we propose a low-cost approach for the volume estimation of different kinds of objects by using a two-view vision approach. The method first computes a reduced three-dimensional model from a single couple of images, then extracts a series of features from the obtained model. Lastly, the features are processed using a computational intelligence approach, which is able to learn the relation between the features and the volume of the captured object, in order to estimate the volume independently of its position and angle, and without computing a full three-dimensional model. Results show that the approach is feasible and can obtain an accurate volume estimation. Compared to the direct computation of the volume from the three-dimensional models, the approach is more accurate and also less dependent to the position and angle of the measured objects with respect to the cameras.
The main goal of this paper is to investigate the applicability of a back-propagation artificialneural network on the encryption of huge-sized satellite images. The central contribution is using fixed, arbitrary keys...
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ISBN:
(纸本)9781467328241;9781467328234
The main goal of this paper is to investigate the applicability of a back-propagation artificialneural network on the encryption of huge-sized satellite images. The central contribution is using fixed, arbitrary keys in the training process as in classical symmetric and asymmetric cryptography. The used network is of NxMxN neurons representing the input, hidden, and output layers, respectively. The network is trained by adjusting the weights while the bias is given a constant value between 0 and 1 after normalizing the values. A bias is determined. The bias between the input layer and the hidden layer works as the first key (K-1), while the bias between the hidden layer and the output layer represents a second key (K-2). The training method uses K-1, K2, or both and is done using images of small sizes to improve speed. Then, the network is used to encrypt and decrypt normal satellite images. Numerous trials were done for different satellite optical and SAR images and the goodness of fit (quality of decryption) between the original images and the decrypted ones was at least 98%, even for the images that the network was not previously trained to decrypt. It was also found that the network is not affected by geometrical image distortions like translation, size, and rotation.
In the last decade, osteoporotic fractures became one of the most serious problems in public health. The life risk of suffering of an osteoporotic fracture is estimated to be 30% for 50 years old and in postmenopausal...
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