Search for: convolutional-neural-network
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    One‐dimensional convolutional neural networks for hyperspectral analysis of nitrogen in plant leaves

    , Article Applied Sciences (Switzerland) ; Volume 11, Issue 24 , 2021 ; 20763417 (ISSN) Pourdarbani, R ; Sabzi, S ; Rohban, M. H ; Hernández‐hernández, J. L ; Gallardo‐bernal, I ; Herrera‐miranda, I ; García‐mateos, G ; Sharif University of Technology
    MDPI  2021
    Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one‐dimensional convolutional neural networks (1D‐CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N30% (excess application of nitrogen fertilizer by 30%), N60% (60% overdose),... 

    Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks

    , Article Analytica Chimica Acta ; 2021 ; 00032670 (ISSN) Shariat, K ; Kirsanov, D ; Olivieri, A. C ; Parastar, H ; Sharif University of Technology
    Elsevier B.V  2021
    In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real... 

    MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography

    , Article Physica Medica ; Volume 67 , 2019 , Pages 58-69 ; 11201797 (ISSN) Moradi, S ; GhelichOghli, M ; Alizadehasl, A ; Shiri, I ; Oveisi, N ; Oveisi, M ; Maleki, M ; Dhooge, J ; Sharif University of Technology
    Associazione Italiana di Fisica Medica  2019
    Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging task due to ill-defined borders, and operator dependence issues (insufficient reproducibility). U-net, which is a well-known architecture in medical image segmentation, addressed this problem through an encoder-decoder path. Despite outstanding overall performance, U-net ignores the contribution of all semantic strengths in the segmentation procedure. In the present study, we have proposed a novel architecture to tackle this drawback. Feature maps in all levels of the decoder... 

    Time-invariant 3d human action recognition with positive and negative movement memory using convolutional neural networks

    , Article 4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019, 6 March 2019 through 7 March 2019 ; 2019 , Pages 26-31 ; 9781728116211 (ISBN) Khezeli, F ; Mohammadzade, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Developing time-invariant solutions for recognition of human action is still an important and open challenge. Three issues make time-invariant solutions so important: different speed of performing the same action by different people, latency in doing the actions and the existence of redundant frames in the recorded video. To overcome these problems, we propose a method based on the so-called memory of the joints to remember only the cumulative positive and negative movement of each joint. Hence, we transform action recognition from time-space to shape-space and the action recognition becomes the problem of shape classification. These shape features contain highly discriminative information... 

    Comparing performance of metaheuristic algorithms for finding the optimum structure of CNN for face recognition

    , Article International Journal of Nonlinear Analysis and Applications ; Volume 11, Issue 1 , 2020 , Pages 301-319 Rikhtegar, A ; Pooyan, M ; Manzuri, M. T ; Sharif University of Technology
    Semnan University, Center of Excellence in Nonlinear Analysis and Applications  2020
    Local and global based methods are two main trends for face recognition. Local approaches extract salient features by processing different parts of the image whereas global approaches find a general template for face of each person. Unfortunately, most global approaches work under controlled envi-ronments and they are sensitive to changes in the illumination. On the other hand, local approaches are more robust but finding their optimal parameters is a challenging task. This work proposes a new local-based approach that automatically tunes its parameters. The proposed method incorporates different techniques. In the first step, convolutional neural network (CNN) is employed as a trainable... 

    Efficient Implementation of Compressed Deep Convolutional Neural Networks

    , M.Sc. Thesis Sharif University of Technology Afshar, Mohammad (Author) ; Hashemi, Matin (Supervisor)
    Many mobile applications running on smartphones, wearable devices, tiny autonomous robots and IoT devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However,performance and energy consumption limitations make the execution of such computationally intensive algorithms on embedded mobile devices prohibitive.We present a GPU-accelerated engine, dubbed mCNN, for execution of trained deep CNNs on mobile platforms. The proposed solution takes the trained model as input and automatically optimizes its parallel implementation on the target mobile platform for efficient use of hardware resources such as mobile GPU threads and SIMD units.... 

    An Enhanced Algorithm for Concealed Object Detection in Millimeter Wave Imaging

    , M.Sc. Thesis Sharif University of Technology Rezaei, Vahid (Author) ; Shabany, Mahdi (Supervisor) ; Kavehvash, Zahra (Co-Supervisor)
    One of the most important alternative technologies in the field of security monitoring is millimeter-wave imaging technology, which is a good alternative both for performance and cost-effectiveness. Conventional security monitoring techniques use optical images or metal detectors to control people in crowded, sensitive places, but with the help of electromagnetic waves, these technologies can be obtained. This way, metal and non-metallic objects hidden inside clothing, bags or shoes can also be detected that are not identifiable by conventional security-control methods. This thesis examines the implementation of an improved algorithm for automatic detection of objects in millimeter-wave... 

    Detection of High Frequency Oscillations from ECoG Recordings in Epileptic Patients

    , M.Sc. Thesis Sharif University of Technology Gharebaghi Asl, Fatemeh (Author) ; Hajipour, Sepideh (Supervisor) ; Sinaei, Farnaz (Co-Supervisor)
    The processing of brain signals, including the electrocorticogram (ECoG) signal, is widely used in the investigation of neurological diseases. Conventionally, the ECoG signal has frequency components up to the range of 80 Hz. Studies have proven that in some conditions, such as epilepsy, the brain signal includes frequency components higher than 80 Hz, which are called high-frequency oscillations (HFO). Therefore, HFOs are recognized as a biomarker for epilepsy. The aim of this thesis is to review the previous methods of detecting HFOs and to present new methods with greater efficiency in the direction of diagnosis or treatment of epileptic patients. For this purpose, we used the ECoG data... 

    Esophageal gross tumor volume segmentation using a 3D convolutional neural network

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16 September 2018 through 20 September 2018 ; Volume 11073 LNCS , 2018 , Pages 343-351 ; 03029743 (ISSN); 9783030009366 (ISBN) Yousefi, S ; Sokooti, H ; Elmahdy, M. S ; Peters, F. P ; Manzuri Shalmani, M. T ; Zinkstok, R. T ; Staring, M ; Sharif University of Technology
    Springer Verlag  2018
    Accurate gross tumor volume (GTV) segmentation in esophagus CT images is a critical task in computer aided diagnosis (CAD) systems. However, because of the difficulties raised by the contrast similarity between esophageal GTV and its neighboring tissues in CT scans, this problem has been addressed weakly. In this paper, we present a 3D end-to-end method based on a convolutional neural network (CNN) for this purpose. We leverage design elements from DenseNet in a typical U-shape. The proposed architecture consists of a contractile path and an extending path that includes dense blocks for extracting contextual features and retrieves the lost resolution respectively. Using dense blocks leads to... 

    Cross platform web-based smart tourism using deep monument mining

    , Article 4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019, 6 March 2019 through 7 March 2019 ; 2019 , Pages 190-194 ; 9781728116211 (ISBN) Etaati, M ; Majidi, B ; Manzuri, M. T ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Tourism is one of the largest sources of economic revenue for many countries around the world. The historical and cultural treasures of Iran made it one the main destinations for international tourists. One of the biggest problems encountered by the tourists during the visit to monuments of Iran is the lack of information about the visited landmark. Given that cameras can be found in all of the smart phones, the use of the landmark's photos can be very important for obtaining information about the tourism sites. The detection of the landmarks in an image taken by the mobile phone camera can be a very complex task depending on the angle and the light situation in which the photo is taken. In... 

    Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning

    , Article Automation in Construction ; Volume 120 , 2020 Chen, J ; Zhang, D ; Huang, H ; Shadabfar, M ; Zhou, M ; Yang, T ; Sharif University of Technology
    Elsevier B.V  2020
    In this paper, an advanced integrated pixel-level method based on the deep convolutional neural network (DCNN) approach named DeepLabv3+ is proposed for weak interlayers detection and quantification. Furthermore, a database containing 32,040 images of limestone, dolomite, loess clay, and red clay is established to verify this method. The proposed model is then trained, validated, and tested via feeding multiple weak interlayers. Moreover, robustness and adaptability of the proposed model are evaluated, and the weak interlayers are extracted. Compared with the fully convolutional network (FCN)-based method and traditional image techniques, the proposed model provides higher accuracy in terms... 

    A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2

    , Article Informatics in Medicine Unlocked ; Volume 19 , 2020 Rahimzadeh, M ; Attar, A ; Sharif University of Technology
    Elsevier Ltd  2020
    In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best... 

    Scale equivariant CNNs with scale steerable filters

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP, 19 February 2020 through 20 February 2020 ; Volume 2020-February , 2020 ; ISSN: 21666776 ; ISBN: 9781728168326 Naderi, H ; Goli, L ; Kasaei, S ; Sharif University of Technology
    IEEE Computer Society  2020
    Convolution Neural Networks (CNNs), despite being one of the most successful image classification methods, are not robust to most geometric transformations (rotation, isotropic scaling) because of their structural constraints. Recently, scale steerable filters have been proposed to allow scale invariance in CNNs. Although these filters enhance the network performance in scaled image classification tasks, they cannot maintain the scale information across the network. In this paper, this problem is addressed. First, a CNN is built with the usage of scale steerable filters. Then, a scale equivariat network is acquired by adding a feature map to each layer so that the scale-related features are... 

    Automatic image annotation using tag relations and graph convolutional networks

    , Article 5th International Conference on Pattern Recognition and Image Analysis, IPRIA 2021, 28 April 2021 through 29 April 2021 ; 2021 ; 9781665426596 (ISBN) Lotfi, F ; Jamzad, M ; Beigy, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    Automatic image annotation is a mechanism to assign a list of appropriate tags that describe the visual content of a given image. Most methods only focus on the content of the images and ignore the relationship between the tags in vocabulary. In this work, we propose a new deep learning-based automatic image annotation architecture, which considers label dependencies in a graph convolution neural network structure and extracts tag descriptors to re-weight the output class scores based on their relationships. The proposed architecture has three main parts: feature extraction, graph convolutional network, and annotation. In graph convolutional network, we apply one layer convolution on... 

    Accuracy Improvement of Vision-Aided Gyroscope using Convolutional Neural Network

    , M.Sc. Thesis Sharif University of Technology Shadravan, Shayan (Author) ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
    The growth of the knowledge of image processing and machine vision in recent years has led to many applications in various fields. One of the most important applications in machine vision is automotive navigation of vehicles and robots. The effective use of visual sensors to detect obstacles, routing, detecting the position of the robot, and mapping the environment is one of the most important goals in ground robotics. Few methods using sensors such as accelerometers, gyroscopes and global positioning systems, suffer from problems such as high costs, accumulative errors, dependencies on external systems, and the inability to be used in closed spaces. But with the use of the visual sensors,... 

    Pitch Detection Using Deep Learning

    , M.Sc. Thesis Sharif University of Technology Khademhosseini, Mohammad (Author) ; Marvasti, Farrokh (Supervisor) ; Ghaemmaghami, Shahrokh (Co-Supervisor)
    Pitch frequency is one of the most important attributes of speech, which has been found to be quite challenging in noisy conditions. In this paper, we propose a pitch detection method based on separation of the low pitch from high pitch signals, depending on the pitch frequency below or over 200Hz, respectively, using a deep convolutional neural network. The pitch frequency is initially estimated, employing a conventional pitch detection method. From this initial estimation and using a deep convolutional neural network which determines the signals type (high-pitch or low-pitch), the pitch candidates are derived. To choose the true pitch values, we use three features in addition to soft... 

    Class Attention Map Distillation In Semantic Segmentation

    , M.Sc. Thesis Sharif University of Technology Karimi Bavandpour, Nader (Author) ; Kasaei, Shohreh (Supervisor)
    Semantic segmentation is the tash of labeling each pixel of an input image. It is one of the main problems in computer vision and plays an important role in scene understanding. State of the art methods of solving it are based on Convolutional Neural Networhs (CNNs). While many real world tasks like autodriving cars and robot navigation require fast and lightweight models, CNNs inherently tend to give beter accuracy when they are deeper and bigger, and this has raised interest in designing compact networks. Knowledge distillation is one of the popular methods of training compact networhs and helps to transfer a big and powerful network’s knowledge to a small and compact one. In this research... 

    Identity recognition based on convolutional neural networks using gait data

    , Article 26th International Computer Conference, Computer Society of Iran, CSICC 2021, 3 March 2021 through 4 March 2021 ; 2021 ; 9781665412414 (ISBN) Faraji, F ; Lotfi, F ; Majdolhosseini, M ; Jafarian, M ; Taghirad, H. D ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    As a critical part of any security system, identity recognition has become paramount among researchers. In this regard, several methods are presented while considering various sensors and data. In particular, gait data yields rich information about a person, including some exclusive moving patterns which can be utilized to distinguish between different individuals. On the other hand, convolutional neural networks are proved to be applicable for structured data, especially images. In this article, 12 markers are considered in gathering the gait data, each representing a lower-body joint location. Then, utilizing the gait data in a 2D tensor form, three different convolutional neural networks... 

    Vibration-based Structural Damage State Identification by Image-based Two-dimentional Convolutional Neural Network

    , M.Sc. Thesis Sharif University of Technology Daeizadeh, Mohammad javad (Author) ; Mohtasham Dolatshahi, Keyarash (Supervisor)
    This paper proposes a novel image-based two-dimensional convolutional neural network for identifying damage level of the structures after an earthquake. The acceleration of the structure is the input data that is converted into an image, and the corresponding damage level is the output of the network. The superiority of the proposed method in comparison to the signal-based one-dimensional convolutional neural network method is the incorporation of the high and low frequency of the input data into the kernel of the convolution. Rows of the input image show short period high frequency of the signal and the column represent long duration and low frequency of the response time history... 

    Improving the 3D Segmentation of Nodules in Lung CT Images

    , M.Sc. Thesis Sharif University of Technology Moradi, Puria (Author) ; Jamzad, Mansour (Supervisor) ; Beigy, Hamid (Co-Supervisor)
    Lung cancer is one of the most common types of cancers, and its early diagnosis can save many lives. Due to the high number of computed tomography (CT) images used to detect lung cancer, it is difficult to accurately and rapidly diagnose this disease. Doing so requires high expertise by radiologists. Therefore the demand for computer aided diagnosis systems in this area has been increased. The core of all lung cancer detection systems is the distinction between cancer and non-cancerous tissues. The main objective of this study is to present a new method based on 3D convolutional neural networks (CNN) that can perform false positives reduction operations while providing high sensitivity. In...