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arXiv 2024年

作者： Latifi, Seyed Amir Ghassemian, Hassan Imani, Maryam Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

This paper presents a fast and cost-effective method for diagnosing cardiac abnormalities with high accuracy and reliability using low-cost systems in clinics. The primary limitation of automatic diagnosing of cardiac diseases is the rarity of correct and acceptable labeled samples, which can be expensive to prepare. Two methods are proposed to address this issue. The first method is a unique Multi-Branch Deep Convolutional Neural Network (MBDCN) architecture inspired by human auditory processing, specifically designed to optimize feature extraction by employing various sizes of convolutional filters and audio signal power spectrum as input. In the second method, called as Long short-term memory-Convolutional Neural (LSCN) model, Additionally, the network architecture includes Long Short-Term Memory (LSTM) network blocks to improve feature extraction in the time domain. The innovative approach of combining multiple parallel branches consisting of the one-dimensional convolutional layers along with LSTM blocks helps in achieving superior results in audio signal processing tasks. The experimental results demonstrate the superiority of the proposed methods over the state-of-the-art techniques. The overall classification accuracy of heart sounds with the LSCN network is more than 96%. The efficiency of this network is significant compared to common feature extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and wavelet transform. Therefore, the proposed method shows promising results in the automatic analysis of heart sounds and has potential applications in the diagnosis and early detection of cardiovascular diseases. © 2024, CC BY-NC-SA.

关键词： Long short-term memory

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Unsupervised Hyperspectral image Classification: Spatial and Spectral Feature Fusion with Masked Autoencoders

Unsupervised Hyperspectral Image Classification: Spatial and...

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International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Seyed Ali Mirsharji Hassan Ghassemian Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model’s performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications.

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Contextual information Classification of Remotely Sensed images

Contextual Information Classification of Remotely Sensed Ima...

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International Conference on Pattern Recognition and image analysis (IPRIA)

作者： Alirza Dori Hassan Ghassemian Maryam Imani Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering TarbiatModares University Tehran Iran

This work proposes a multidisciplinary contextual information extraction and decision fusion approach for increasing the classification accuracy. It improves the image classification with integrating the results of various classifiers. The proposed method is implemented in three-steps: 1) contextual feature extraction using four different feature extractors methods: a) Gray Level Cooccurrence Matrix, b) Gabor filters, c) Laplacian Gaussian filters and d) Gaussian Derivatives Functions; 2) classification of contextual features using four different classification rules (ML, Tree, KNN and SVM) by using only 2% of data for training the classifiers; and 3) finally, decision fusion using six decision fusion rules. The experimental results on real remotely sensed images have been presented.

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Unsupervised Change Detection in SAR images Using a Six-Branch CNN and Adaptive Window Approach

Unsupervised Change Detection in SAR Images Using a Six-Bran...

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Iranian Conference on electrical engineering (ICEE)

作者： Abbas Kakoolvand Maryam Imani Hassan Ghassemian Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Change detection is one of the important and hot topics in remote sensing. Adaptive windowing approaches can preserve image details while reduce noise in the process of change detection. In the proposed method, two difference images (DIs) are obtained by using the adaptive window approach. Some fake labels are provided from these DIs. Neural networks have a good performance in image processing. To use this advantage, a six-branch convolutional neural network (CNN) is trained using the fake labels. Using these six branches with utilizing the adaptive windows, the network can preserve image detail while reduce noise. The proposed method based on three criteria (PCC, Kappa, and F1 score) has the best results in two datasets and has results close to the best method in other datasets.

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Empirical Mode Decomposition Based Morphological Profile For Hyperspectral image Classification

Empirical Mode Decomposition Based Morphological Profile For...

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International Conference on Pattern Recognition and image analysis (IPRIA)

作者： Kosar Amiri Maryam Imani Hassan Ghassemian Analysis Lab Faculty of Electrical and Computer Engineering Image processing and Information Tarbiat Modares University Tehran Iran

The empirical mode decomposition (EMD) based morphological profile (MP), called as EMDMP, is proposed for hyperspectral image classification in this work. The EMD algorithm can well decompose the nonlinear spectral feature vector to intrinsic components and the residual term. To extract the main spatial characteristics and shape structures, the closing operators are applied to the intrinsic components. In contrast, to extract details and more abstract contextual features, the opening operators are applied to the residual component. Finally, a multi-resolution morphological profile is provided with concatenation of the intrinsic components-based closing profile and residual component based opening profile. EMDMP achieves 96.54% overall accuracy compared to 95.15% obtained by convolutional neural network (CNN) on Indian dataset with 10 % training samples. In University of Pavia with 1% training samples, EMDMP results in 97.66% overall accuracy compared to 95.90% obtained by CNN.

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Feature Extraction and Classification of Respiratory Sound and Lung Diseases

Feature Extraction and Classification of Respiratory Sound a...

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International Conference on Pattern Recognition and image analysis (IPRIA)

作者： Seyed Amir Latifi Hassan Ghassemian Maryam Imani Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Bacteria, viruses, and fungi can cause respiratory infections. It is usually possible to detect respiratory diseases early by listening to the lung sounds with a stethoscope. In reality, lung sound analysis is a time-consuming and difficult task that depends on medical skills and recognition experience. Recent advances in automatic respiratory sound recognition and classification have attracted more attention. The outbreak of COVID-19 throughout the world and the high patient numbers have placed a great deal of pressure on medical professionals. A smart algorithm is therefore a necessity to provide a faster and more accurate detection of lung infections by automatically processing the sounds of the lungs. This paper proposes two new lung sound feature extraction, maximum entropy Gabor filter bank (MAGFB), and maximum entropy Mel filter bank (MAMFB). The classification is performed by a deep neural convolution network (DCNN) by using 50% of data for training the classifier. The filter banks have been substituted, instead of the convolutional layers. Experiments were conducted on the ICBHI 2017 Challenge dataset (with eight classes). The proposed method has a better performance compared to famous methods such as MFCC and Wavelet transform. Particularly, the performance of the second method is significant. For ICBHI 2017 challenge dataset, the overall accuracy of MFCC, Wavelet, MAGFB and MAMFB were 87%, 86%,90% and 93%, respectively.

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Fusion of Multi-Level CNN With LBP Features For Facial Emotion Recognition

Fusion of Multi-Level CNN With LBP Features For Facial Emoti...

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Iranian Conference on electrical engineering (ICEE)

作者： Ehsan Bahmanabady Maryam Imani Hassan Ghassemian Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

A facial emotion recognition framework is proposed in this work. The convolutional neural network (CNN) has high ability in extraction of hierarchical spatial features from low level texture characteristics to high level contextual features. A simple CNN model with three layers is suggested in this paper where the features extracted in all layers containing multi-level features are activated and used for classification. In addition, the local binary pattern (LBP) descriptor is used to extract discriminative features from the spatial structure of the input image. Therefore, four feature sources are provided by multi levels of CNN and the LBP descriptor. Each feature source is used for facial emotion recognition by applying to the support vector machine (SVM) classier. Finally, the majority voting rule is used for decision fusion to provide the final emotional label of each given face image. The proposed method with 84% overall accuracy, 83% weighted F1-score and 81% kappa coefficient provides the best performance compared to LBP, multi-level CNN and two-dimensional principal component analysis (2DPCA) methods.

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A New Spectral-Spatial Network for Feature Fusion and Classification of Hyperspectral images 7

A New Spectral-Spatial Network for Feature Fusion and Classi...

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7th International Conference on Signal processing and Intelligent Systems, ICSPIS 2021

作者： Aghili, Mohamad Ebrahim Ghassemian, Hassan Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665409384

Hyperspectral image (HSI) classification is one of the most important applications among all types of classification fields. Proper classification of spectral data leads to discovery of important land covers. In recent years, many methods have been introduced to increase the HSI classification accuracy. Methods based on neural networks show superior results compared to other methods. Among them, the two-dimensional convolutional neural networks (2D-CNNs) inspired by the human eye retina have achieved higher accuracy in classification. In most cases, HSI classifiers use only spectral features. In this paper, the spectral-spatial feature fusion and HSI classification using 2D-CNN are focused. For this purpose, the first 2D-convolutional layer of CNN is substituted by two combined 2D-Gabor-Shapelet filter banks. This layer extracts contextual information and provides valuable joint spectral-spatial features. The experimental results on real HSI (including the urban and agricultural areas and their mixture) show that the proposed method improves the overall classification performance. Compared to several famous HSI classification based on neural networks, the proposed method has higher speed and classification accuracy. © 2021 IEEE.

关键词： Gabor filters

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

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2023 International Conference on Earth Observation and Geo-Spatial information, ICEOGI 2023

作者： Ourabia, Soumeya Baaziz, Nadia Smara, Youcef University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Departement of Electronics Tipaza Algeria University of Quebec in Outaouais - Uqo Research Laboratory in Multimedia Information Department of Computer Science and Engineering GatineauQC Canada University of Sciences and Technology Houari Boumediene -USTHB Laboratory of Image Processing and Radiation Faculty of Electrical Engineering Algiers Algeria

ISBN: (纸本)9798350396720

The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods. © 2023 IEEE.

关键词： image enhancement

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Deep Curriculum Learning for PolSAR image Classification

arXiv

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arXiv 2021年

作者： Mousavi, Hamidreza Imani, Maryam Ghassemian, Hassan Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data. This method utilizes the entropy-alpha target decomposition method to estimate the degree of complexity of each PolSAR image patch before applying it to the convolutional neural network (CNN). Also, an accumulative mini-batch pacing function is used to introduce more difficult patches to CNN. Experiments on the widely used data set of AIRSAR Flevoland reveal that the proposed curriculum learning method can not only increase classification accuracy but also lead to faster training convergence. © 2021, CC BY.

关键词： Curricula

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