Image Inpainting: A Review

Elharrouss, Omar; Almaadeed, Noor; Al‐Maadeed, Somaya; Akbari, Younes

doi:10.1007/s11063-019-10163-0

Cited by 247 publications

(125 citation statements)

References 105 publications

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“…Networks (DLNN) is an advanced version of MLFFNN [24][25][26]. In this structure, everything including feature extraction, size reduction, and filtering processes is done automatically [27,28].…”

Section: Deep Learning Neural Network Deep Learning Neuralmentioning

confidence: 99%

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The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets

Uçar

Nour

Sindi

et al. 2020

Mathematical Problems in Engineering

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Training and testing process for the classification of biomedical datasets in machine learning is very important. The researcher should choose carefully the methods that should be used at every step. However, there are very few studies on method choices. The studies in the literature are generally theoretical. Besides, there is no useful model for how to select samples in the training and testing process. Therefore, there is a need for resources in machine learning that discuss the training and testing process in detail and offer new recommendations. This article provides a detailed analysis of the training and testing process in machine learning. The article has the following sections. The third section describes how to prepare the datasets. Four balanced datasets were used for the application. The fourth section describes the rate and how to select samples at the training and testing stage. The fundamental sampling theorem is the subject of statistics. It shows how to select samples. In this article, it has been proposed to use sampling methods in machine learning training and testing process. The fourth section covers the theoretic expression of four different sampling theorems. Besides, the results section has the results of the performance of sampling theorems. The fifth section describes the methods by which training and pretest features can be selected. In the study, three different classifiers control the performance. The results section describes how the results should be analyzed. Additionally, this article proposes performance evaluation methods to evaluate its results. This article examines the effect of the training and testing process on performance in machine learning in detail and proposes the use of sampling theorems for the training and testing process. According to the results, datasets, feature selection algorithms, classifiers, training, and test ratio are the criteria that directly affect performance. However, the methods of selecting samples at the training and testing stages are vital for the system to work correctly. In order to design a stable system, it is recommended that samples should be selected with a stratified systematic sampling theorem.

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Section: Deep Learning Neural Network Deep Learning Neuralmentioning

confidence: 99%

“…In this structure, everything including feature extraction, size reduction, and filtering processes is done automatically [27,28]. It is often used in image processing [24]. However, if a signal can be converted into an image, DLNN can be used comfortably [14,29].…”

Section: Deep Learning Neural Network Deep Learning Neuralmentioning

confidence: 99%

The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets

Uçar

Nour

Sindi

et al. 2020

Mathematical Problems in Engineering

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“…Thus, RGBD data of a subject wearing a mobile eye tracker are recorded. To avoid changes in the user appearance due to the presence of glasses, the authors used semantic inpainting [ 175 , 176 ] of the regions covered by the eye tracking glasses.…”

Section: Publicly Available Datasetsmentioning

confidence: 99%

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

Cazzato

Leo

Distante

et al. 2020

Sensors

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The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.

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“…Sometimes, due to lack of facilities as well as time the disease spreads so vastly that it becomes fatal and incurable. [1] There is a need for accurate as well as reliable systems that can help not only clinicians but as well persons to detect types of lesions at early stages. This proposed skin-cancer detection system is developed as a clinical decision support tool that uses computer vision which can help doctors to diagnose the skin cancer types by simply using images with good accuracy.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision for Skin Cancer Detection and Diagnosis

Kaur¹,

Boddu²,

Fiaidhi³

2020

Preprint

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Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesions. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are preprocessed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.

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Image Inpainting: A Review

Cited by 247 publications

References 105 publications

The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets

The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

Computer Vision for Skin Cancer Detection and Diagnosis

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