Fa
  • Ph.D. (2015)

    Electrical Engineering - Telecommunications

    Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

  • Pattern Recognition and Machine Learning
  • Signal and Image Processing
  • Remote Sensing

    Maryam Imani completed her Ph.D in Electrical Engineering, Communication, from Tarbiat Modares University, Tehran, Iran in 2015. She continued her research in Tarbiat Modares University as a postdoc. Since 2018, she has been with Tarbiat Modares University in Tehran, Iran, where she is the Associate Professor of Computer and Electrical Engineering. Her research interests include statistical pattern recognition, machine learning, signal and image processing and remote sensing.

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    Curriculum Vitae (CV)

    Hyperspectral Image Classification by Optimizing Convolutional Neural Networks based on Information Theory and 3D-Gabor Filters

    Mohammad Ghassemi, Hassan Ghassemian, Maryam Imani
    Journal PaperInternational Journal of Remote Sensing , Volume 42 , Issue 11, 2021 January , {Pages 4383–4413 }

    Abstract

    Target Detection Using Multispectral Images, a Case Study: Wheat Detection in Chenaran County in Iran

    M Imani
    Journal PaperJournal of Electrical and Computer Engineering Innovations (JECEI) , Volume 9 , Issue 1, 2021 January 1, {Pages 24-Nov }

    Abstract

    Background and Objectives: Target detection is one of the main applications of remote sensing. Multispectral (MS) images with higher spatial resolution than hyperspectral images are an important source for shape and geometric characterization, and so, MS target detection is interested. Methods: A target detector appropriate for multispectral (MS) images is selected among hyperspectral target detectors and redefined in this paper. Many target detectors have been proposed for hyperspectral images in the remote sensing filed. Most of these detectors just use the spectral information. Since, the MS images have higher spatial resolution compared to hyperspectral ones, it is proposed that select a target detector that uses both of the spectral an

    Convolutional and Recurrent Neural network Based Model for Short-Term Load Forecasting

    Hosein Eskandari, Maryam Imani, Mohsen Parsa Moghaddam
    Journal PaperElectric Power Systems Research , Volume 195 , 2021 January , {Pages }

    Abstract

    The consumed electrical load is affected by many external factors such as weather, season of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high accurate forecasting model for hourly load consumption with considering these external variables. At first, the electrical load and temperature time series are rearranged into separate two-dimensional matrices. Convolutional neural networks (CNNs) are utilized to extract the load and temperature features. The autocorrelation coefficients of the load and temperature sequences are used to determine the kernel size of the convolutional layers. At this stage, the convolutional layers specifically convert the univariate data to multidimensional features by applying two

    Electrical Load-Temperature CNN for Residential Load Forecasting

    Maryam Imani
    Journal PaperEnergy , 2021 March 31, {Pages 120480 }

    Abstract

    Residential load forecasting is a challenging problem due to complex relations among the hourly electrical load values along the time and also nonlinear relationships among the consumed electricity values and their associated temperature values. A nonlinear relationship extraction (NRE) method is proposed in this work. NRE obtains a load cube where each hourly load value is surrounded by load values of past, present and future hours in previous, same and next days of the same week and previous week. Then, a convolutional neural network (CNN) is used to extract the nonlinear relationships among the load values. In addition, a load-temperature cube is composed from the hourly load and temperature values of a week. Another CNN is trained by us

    Automatic Diagnosis of Coronavirus (COVID-19) Using Shape and Texture Characteristics Extracted From X-Ray and CT-Scan Images

    Maryam Imani
    Journal PaperBiomedical Signal Processing and Control , 2021 April 2, {Pages 102602 }

    Abstract

    Automatic diagnosis of coronavirus (COVID-19) is studied in this research. Deep learning methods especially convolutional neural networks (CNNs) have shown great success in COVID-19 diagnosis in recent works. But they are efficient when the depth of network is high enough. However, the use of a deep network requires a sufficiently large training set, which is not available in practice. From the other hand, the use of a shallow CNN may not provide superior results because it is not able to rich feature extraction due to lacking enough convolutional layers. To deal with this difficulty, the contextual features reduced by convolutional filters (CFRCF) is proposed in this work. CFRCF extracts shape and textural features as contextual feature ma

    Polarimetric SAR Classification Using Ridge Regression-Based Polarimetric-Spatial Feature Extraction

    Maryam Imani
    Conference Paper2021 26th International Computer Conference, Computer Society of Iran (CSICC) , 2021 March 3, {Pages 05-Jan }

    Abstract

    A polarimetric synthetic aperture radar (PolSAR) image classification is introduced in this work. The proposed method called as ridge regression-based polarimetric-spatial (RRPS) feature extraction generates polarimetric-spatial features with minimum overlapping and redundant information. To this end, each polarimetric-spatial channel of PolSAR data is represented through a ridge regression model using the farthest neighbors of that channel. The weights of the regression model compose the projection matrix for dimensionality reduction. The proposed RRPS method with a closed form solution has high performance in PolSAR image classification using small training sets.

    A Random Patches Based Edge Preserving Network for Land Cover Classification Using Polarimetric Synthetic Aperture Radar Images

    Maryam Imani
    Journal PaperInternational Journal of Remote Sensing , Volume 42 , Issue 13, 2021 January , {Pages 4946–4964 }

    Abstract

    A random patches-based edge-preserving network (RPEP) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification in this paper. An initial spatial feature extraction is firstly done using the transform domain recursive filtering. From the filtered images, several random patches are chosen and used as convolutional kernels. The designed random patches-based network uses these fix kernels without doing any training process. The multi-scale features extracted by both shallow and deep layers are given to a support vector machine to get an initial classification map. The binary probability maps obtained from the initial classification map are then smoothed using the guided filter as an edge preserving filter. The final c

    Integration of the k-nearest neighbours and patch-based features for PolSAR image classification by using a two-branch residual network

    M Imani
    Journal Paper , , {Pages }

    Abstract

    Heart Abnormality Classification by Phonocardiogram Analysis Using Fusion in Feature and Decision Levels

    H Rahmati, H Ghassemian, M Imani
    Journal Paper , , {Pages }

    Abstract

    Contextual Based Locality Preserving Projection for Classification of SAR Images with Multiple Polarizations

    M Imani
    Journal Paper , , {Pages }

    Abstract

    Contextual and Spectral Feature Fusion Using Local Binary Graph for Hyperspectral Images Classification

    ZF Farahani, H Ghassemian, M Imani
    Journal Paper , , {Pages }

    Abstract

    Sparse and collaborative representation-based anomaly detection

    Maryam Imani
    Journal PaperSignal, Image and Video Processing , 2020 January , {Pages }

    Abstract

    Random Forest with Attribute Profile for Remote Sensing Image Classification

    Maryam Imani
    Conference Paper11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020) , 2020 January , {Pages }

    Abstract

    Customer Churn Prediction in Telecommunication Using Machine Learning: A Comparison Study

    Maryam Imani
    Journal PaperAUT Journal of Modeling and Simulation , 2020 June 6, {Pages }

    Abstract

    Telecommunication operators need to accurately predict the customer churn for surviving in the Telecom market. There is a huge volume of customer records such as calls, SMSs and the use of Internet. This data contains rich and valuable information about costumer behavior and his/her pattern consumption. Machine learning is a powerful tool for extraction of costumer information that can be useful for churn prediction. Although several researchers have studies some types of machine learning methods, but, there is not any work which assess different methods from various point of views. The aim of this work is to assess the performance of a wide range of machine learning methods for churn prediction in the form of a comparison study. In this pa

    Texture feed based convolutional neural network for pansharpening

    Maryam Imani
    Journal PaperNeurocomputing , 2020 February 24, {Pages }

    Abstract

    Fusion of panchromatic (PAN) and multispectral (MS) images, called pansharpening, is one of the main challenging problems in remote sensing. Convolutional neural network (CNN) based pansharpening has been introduced in some works recently. The represented frameworks often use a multi-layer CNN for fusion of MS and PAN images. Limited number of training samples and the high number of network weights to be determined may cause overfitting problem. To deal with this difficulty, a simple structure comprised from two single layer convolutional networks is proposed in this paper. The shortage of deleted layers is compensated by applying 3D Gabor filters and shearlet transform in addition to nonlinear kernel based principal component analysis. The

    Deep Learning Based Electricity Demand Forecasting in Different Domains

    M Imani
    Journal PaperIranian (Iranica) Journal of Energy & Environment , Volume 11 , Issue 1, 2020 March 1, {Pages 33-39 }

    Abstract

    Electricity demand forecasting is an important task in power grids. Most of researches on electrical load forecasting have been done in the time domain. But, the electrical time series has a non-stationary inherence that makes hard load prediction. Moreover, valuable information is hidden in the electrical load sequence which is not open in the time domain. To deal with these difficulties, a new electricity demand forecasting framework is proposed in this work. In the proposed framework, at first, a new feature space of electrical load sequence is composed. The provided domain involves complementary information about shape and variations of electrical load sequence. Then, the obtained load features are integrated with the original load valu

    An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges

    Maryam Imani, Hassan Ghassemian
    Journal PaperInformation fusion , Volume 59 , 2020 July 1, {Pages 59-83 }

    Abstract

    Hyperspectral images (HSIs) have a cube form containing spatial information in two dimensions and rich spectral information in the third one. The high volume of spectral bands allows discrimination between various materials with high details. Moreover, by utilizing the spatial features of image such as shape, texture and geometrical structures, the land cover discrimination will be improved. So, fusion of spectral and spatial information can significantly improve the HSI classification. In this work, the spectral-spatial information fusion methods are categorized into three main groups. The first group contains segmentation based methods where objects or super-pixels are used instead of pixels for classification or the obtained segmentation

    A New Composite Multimodality Image Fusion Method Based on Shearlet Transform and Retina Inspired Model

    Mohammadmahdi Sayadi, Hassan Ghassemian, Reza Naimi, Maryam Imani
    Conference Paper2020 International Conference on Machine Vision and Image Processing (MVIP) , 2020 February 18, {Pages 05-Jan }

    Abstract

    Medical imaging is a very important element in disease diagnosis. MRI image has structural information, while PET image has functional information. However, there is no medical imagery device that has both structural and functional information simultaneously. Thus, the image fusion technique is used. This work concentrates on PET and MRI fusion. It is based on the combination of retina-inspired model and Non-Subsampled shearlet transform. In the first step, the high-frequency component is obtained by applying the shearlet transform to the MRI image, which produces sub-images in several scales and directions, and by adding up these images together a single edge image is reconstructed. In the second step, the PET image is transferred from RGB

    A Retina-Inspired Multiresolution Analysis Framework for Pansharpening

    Mehran Maneshi, Hassan Ghassemian, Ghassem Khademi, Maryam Imani
    Conference Paper11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020) , 2020 January , {Pages }

    Abstract

    Technical limitations on the satellite sensors make a trade-off between the spectral and spatial resolution in remotely sensed images. To deal with this issue, pansharpening has been emerged to prepare a single image with the high spatial and spectral resolution, simultaneously. This paper presents a pansharpening approach based on the retina-inspired model and the multiresolution analysis (MRA) framework. The retina- inspired model is simplified by the difference of Gaussian (DoG) operator, and we apply it to the panchromatic image to extract the spatial details. Furthermore, the injection gains in the MRA framework are calculated through an iterative process where the gains at each iteration are updated based on the fusion result obtained

    A Multi-Scale Transform Method Based on Morphological Operators for Pansharpening

    Maryam Imani
    Journal PaperAUT Journal of Modeling and Simulation , 2020 January , {Pages }

    Abstract

    Current Teaching

    • Ph.D.

      Special Topics in Telecommunications (Statistical Pattern Recognition)

    Teaching History

    • Ph.D.

      Deep Learning in Signal Processing

    • 2021
      Bahmanabady, Ehsan
      fusion of contextual information from face images for emotion recognition
    • 2021
      Siahpoosh, Mahnaz
    • 2021
      Saheban maleki, Abolfazl
    • 2021
      Amiri, Kosar

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