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Machine Learning and Data Analysis for Image Processing

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A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 23048

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Special Issue Editors

Dr. Moulay A. Akhloufi

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Guest Editor

Perception, Robotics, and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada
Interests: machine learning; deep learning; computer vision; robotics; medical imaging
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mustapha Kardouchi

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Guest Editor

Department of Computer Science, University of Moncton, Moncton, NB E1A 3E9, Canada
Interests: image processing; medical imaging; motion analysis; machine learning

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed impressive results in the area of machine learning and deep learning applied to image data. The progress in the area of artificial intelligence has sparked innovations in techniques, algorithms, and approaches and led to results that were impossible to achieve until recently. Various areas rely on the analysis of images to make decisions. These areas have been greatly impacted by this progress.

The aim of this Special Issue is to bring contributions and examples from researchers working in these fields. We are seeking new innovative algorithms and approaches using machine learning, deep learning, and data analysis applied to images. Application areas are various, and contributions are accepted in different areas such as computer vision, robot vision, medical imaging, defense and security, remote sensing, image processing, image classification, NDT, etc. Works dealing with other modalities such as visible spectrum imaging, infrared imaging, multispectral and hyperspectral imaging, X-ray, THz, multimodal fusion techniques, etc. are welcome. Contributions can be in a form of research papers, review papers, or comparative analysis.

Prof. Dr. Moulay A. Akhloufi
Prof. Dr. Mustapha Kardouchi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

machine learning
deep learning
data analysis
big data analytics
image processing: detection, recognition, classification
computer vision
face recognition
robot vision
medical imaging
defense and security
remote sensing
nondestructive testing and evaluation (NDT/E.)
single or multiple modalities: visible spectrum, 3D, infrared, THz, X-ray, etc.
multispectral and hyperspectral imaging
data fusion

Published Papers (4 papers)

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Research

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23 pages, 4422 KiB

Open AccessEditor’s ChoiceArticle

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

by Maha Gharaibeh, Mothanna Almahmoud, Mostafa Z. Ali, Amer Al-Badarneh, Mwaffaq El-Heis, Laith Abualigah, Maryam Altalhi, Ahmad Alaiad and Amir H. Gandomi

Big Data Cogn. Comput. 2022, 6(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc6010002 - 28 Dec 2021

Cited by 21 | Viewed by 6033

Abstract

Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most important steps that are needed by medical staff to diagnose their patients early by investigating the indicators of different neuroimaging types. Early diagnosis of Alzheimer’s disease is of great importance in preventing the deterioration of the patient’s situation. In this research, a novel approach was devised based on a digital subtracted angiogram scan that provides sufficient features of a new biomarker cerebral blood flow. The used dataset was acquired from the database of K.A.U.H hospital and contains digital subtracted angiograms of participants who were diagnosed with Alzheimer’s disease, besides samples of normal controls. Since each scan included multiple frames for the left and right ICA’s, pre-processing steps were applied to make the dataset prepared for the next stages of feature extraction and classification. The multiple frames of scans transformed from real space into DCT space and averaged to remove noises. Then, the averaged image was transformed back to the real space, and both sides filtered with Meijering and concatenated in a single image. The proposed model extracts the features using different pre-trained models: InceptionV3 and DenseNet201. Then, the PCA method was utilized to select the features with 0.99 explained variance ratio, where the combination of selected features from both pre-trained models is fed into machine learning classifiers. Overall, the obtained experimental results are at least as good as other state-of-the-art approaches in the literature and more efficient according to the recent medical standards with a 99.14% level of accuracy, considering the difference in dataset samples and the used cerebral blood flow biomarker. Full article

(This article belongs to the Special Issue Machine Learning and Data Analysis for Image Processing)

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19 pages, 2049 KiB

Open AccessEditor’s ChoiceArticle

Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks

by Imen Chebbi, Nedra Mellouli, Imed Riadh Farah and Myriam Lamolle

Big Data Cogn. Comput. 2021, 5(2), 21; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc5020021 - 05 May 2021

Cited by 13 | Viewed by 5521

Abstract

Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods. Full article

(This article belongs to the Special Issue Machine Learning and Data Analysis for Image Processing)

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21 pages, 7004 KiB

Open AccessEditor’s ChoiceArticle

Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

by Qiang Fang, Clemente Ibarra-Castanedo and Xavier Maldague

Big Data Cogn. Comput. 2021, 5(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc5010009 - 26 Feb 2021

Cited by 30 | Viewed by 5140

Abstract

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment. Full article

(This article belongs to the Special Issue Machine Learning and Data Analysis for Image Processing)

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Review

Jump to: Research

21 pages, 4230 KiB

Open AccessReview

Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation

by Rafik Gouiaa, Moulay A. Akhloufi and Mozhdeh Shahbazi

Big Data Cogn. Comput. 2021, 5(4), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc5040050 - 28 Sep 2021

Cited by 9 | Viewed by 4353

Abstract

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provide a comprehensive survey of the recent CNNs based crowd counting techniques. We briefly review detection-based, regression-based, and traditional density estimation based approaches. Then, we delve into detail regarding the deep learning based density estimation approaches and recently published datasets. In addition, we discuss the potential applications of crowd counting and in particular its applications using unmanned aerial vehicle (UAV) images. Full article

(This article belongs to the Special Issue Machine Learning and Data Analysis for Image Processing)

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