H. Aghabarar; K. Kiani; P. Keshavarzi
Abstract
Nowadays, given the rapid progress in pattern recognition, new ideas such as theoretical mathematics can be exploited to improve the efficiency of these tasks. In this paper, the Discrete Wavelet Transform (DWT) is used as a mathematical framework to demonstrate handwritten digit recognition in spiking ...
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Nowadays, given the rapid progress in pattern recognition, new ideas such as theoretical mathematics can be exploited to improve the efficiency of these tasks. In this paper, the Discrete Wavelet Transform (DWT) is used as a mathematical framework to demonstrate handwritten digit recognition in spiking neural networks (SNNs). The motivation behind this method is that the wavelet transform can divide the spike information and noise into separate frequency subbands and also store the time information. The simulation results show that DWT is an effective and worthy choice and brings the network to an efficiency comparable to previous networks in the spiking field. Initially, DWT is applied to MNIST images in the network input. Subsequently, a type of time encoding called constant-current-Leaky Integrate and Fire (LIF) encoding is applied to the transformed data. Following this, the encoded images are input to the multilayer convolutional spiking network. In this architecture, various wavelets have been investigated, and the highest classification accuracy of 99.25% is achieved.
H.5. Image Processing and Computer Vision
M. Saeedzarandi; H. Nezamabadi-pour; S. Saryazdi
Abstract
Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the ...
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Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wavelet based image denoising, selecting a proper model for wavelet coefficients is very important. In this paper, we model wavelet coefficients in each sub-band by heavy-tail distributions that are from scale mixture of normal distribution family. The parameters of distributions are estimated adaptively to model the correlation between the coefficient amplitudes, so the intra-scale dependency of wavelet coefficients is also considered. The denoising results confirm the effectiveness of the proposed method.
H.5. Image Processing and Computer Vision
Seyed M. Ghazali; Y. Baleghi
Abstract
Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric ...
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Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric situations. In this paper an efficient method is proposed for detecting pedestrians in outdoor thermal images that adapts to variable atmospheric situations. In the first step, the type of atmospheric situation is estimated based on the global features of the thermal image. Then, for each situation, a relevant algorithm is performed for pedestrian detection. To do this, thermal images are divided into three classes of atmospheric situations: a) fine such as sunny weather, b) bad such as rainy and hazy weather, c) hot such as hot summer days where pedestrians are darker than background. Then 2-Dimensional Double Density Dual Tree Discrete Wavelet Transform (2D DD DT DWT) in three levels is acquired from input images and the energy of low frequency coefficients in third level is calculated as the discriminating feature for atmospheric situation identification. Feed-forward neural network (FFNN) classifier is trained by this feature vector to determine the category of atmospheric situation. Finally, a predetermined algorithm that is relevant to the category of atmospheric situation is applied for pedestrian detection. The proposed method in pedestrian detection has high performance so that the accuracy of pedestrian detection in two popular databases is more than 99%.
H.3.15.2. Computational neuroscience
A. Goshvarpour; A. Abbasi; A. Goshvarpour
Abstract
Emotion, as a psychophysiological state, plays an important role in human communications and daily life. Emotion studies related to the physiological signals are recently the subject of many researches. In This study a hybrid feature based approach was proposed to examine affective states. To this effect, ...
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Emotion, as a psychophysiological state, plays an important role in human communications and daily life. Emotion studies related to the physiological signals are recently the subject of many researches. In This study a hybrid feature based approach was proposed to examine affective states. To this effect, Electrocardiogram (ECG) signals of 47 students were recorded using pictorial emotion elicitation paradigm. Affective pictures were selected from the International Affective Picture System and assigned into four different emotion classes. After extracting approximate and detail coefficients of Wavelet Transform (WT / Daubechies 4 at level 8), two measures of the second-order difference plot (CTM and D) were calculated for each wavelet coefficient. Subsequently, Least Squares Support Vector Machine (LS-SVM) was applied to discriminate between affective states and the rest. The statistical analysis indicated that the density of CTM in the rest is distinctive from the emotional categories. In addition, the second-order difference plot measurements at the last level of WT coefficients showed significant differences between the rest and emotion categories. Applying LS-SVM, the maximum classification rate of 80.24 % was reached for discrimination between rest and fear. The results of this study indicate the usefulness of the WT in combination with nonlinear technique in characterizing emotional states.
H.6. Pattern Recognition
A. Ebrahimzadeh; M. Ahmadi; M. Safarnejad
Abstract
Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. ...
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Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of the ECG signals. The feature extraction module extracts a balanced combination of the Hermit features and three timing interval feature. Then a number of multi-layer perceptron (MLP) neural networks with different number of layers and eight training algorithms are designed. Seven files from the MIT/BIH arrhythmia database are selected as test data and the performances of the networks, for speed of convergence and accuracy classifications, are evaluated. Generally all of the proposed algorisms have good training time, however, the resilient back propagation (RP) algorithm illustrated the best overall training time among the different training algorithms. The Conjugate gradient back propagation (CGP) algorithm shows the best recognition accuracy about 98.02% using a little amount of features.
M. Banejad; H. Ijadi
Abstract
This paper presets a method including a combination of the wavelet transform and fuzzy function approximation (FFA) for high impedance fault (HIF) detection in distribution electricity network. Discrete wavelet transform (DWT) has been used in this paper as a tool for signal analysis. With studying different ...
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This paper presets a method including a combination of the wavelet transform and fuzzy function approximation (FFA) for high impedance fault (HIF) detection in distribution electricity network. Discrete wavelet transform (DWT) has been used in this paper as a tool for signal analysis. With studying different types of mother signals, detail types and feeder signal, the best case is selected. The DWT is used to extract the best features. The extracted features have been used as the FFA Systems inputs. The FFA system uses the input-output pairs to create a function approximation of the features. The FFA system is able to classify the new features. The combined model is used to model the HIF. This combined model has the high ability to model different types of HIF. In the proposed method, different kind of loads including nonlinear and asymmetric loads and HIF types studied. The results show that the proposed method is able to distinguish no fault and HIF state with high accuracy.
