A generalized framework for non-sinusoidal fringe analysis using deep learning

Mo.Se.: Mosaic image segmentation based on deep cascading learning

Virtual Archaeology Review ◽

10.4995/var.2021.14179 ◽

2021 ◽

Vol 12 (24) ◽

pp. 25

Author(s):

Andrea Felicetti ◽

Marina Paolanti ◽

Primo Zingaretti ◽

Roberto Pierdicca ◽

Eva Savina Malinverni

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

State Of The Art ◽

Automatic Segmentation ◽

Archaeological Site ◽

Visual Observation ◽

Digital Version ◽

Generalized Framework ◽

Close Range ◽

Processing Techniques

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p class="VARAbstract">Mosaic is an ancient type of art used to create decorative images or patterns combining small components. A digital version of a mosaic can be useful for archaeologists, scholars and restorers who are interested in studying, comparing and preserving mosaics. Nowadays, archaeologists base their studies mainly on manual operation and visual observation that, although still fundamental, should be supported by an automatized procedure of information extraction. In this context, this research explains improvements which can change the manual and time-consuming procedure of mosaic tesserae drawing. More specifically, this paper analyses the advantages of using Mo.Se. (Mosaic Segmentation), an algorithm that exploits deep learning and image segmentation techniques; the methodology combines U-Net 3 Network with the Watershed algorithm. The final purpose is to define a workflow which establishes the steps to perform a robust segmentation and obtain a digital (vector) representation of a mosaic. The detailed approach is presented, and theoretical justifications are provided, building various connections with other models, thus making the workflow both theoretically valuable and practically scalable for medium or large datasets. The automatic segmentation process was tested with the high-resolution orthoimage of an ancient mosaic by following a close-range photogrammetry procedure. Our approach has been tested in the pavement of St. Stephen's Church in Umm ar-Rasas, a Jordan archaeological site, located 30 km southeast of the city of Madaba (Jordan). Experimental results show that this generalized framework yields good performances, obtaining higher accuracy compared with other state-of-the-art approaches. Mo.Se. has been validated using publicly available datasets as a benchmark, demonstrating that the combination of learning-based methods with procedural ones enhances segmentation performance in terms of overall accuracy, which is almost 10% higher. This study’s ambitious aim is to provide archaeologists with a tool which accelerates their work of automatically extracting ancient geometric mosaics.</p><p><strong>Highlights:</strong></p><ul><li><p>A Mo.Se. (Mosaic Segmentation) algorithm is described with the purpose to perform robust image segmentation to automatically detect tesserae in ancient mosaics.</p></li><li><p>This research aims to overcome manual and time-consuming procedure of tesserae segmentation by proposing an approach that uses deep learning and image processing techniques, obtaining a digital replica of a mosaic.</p></li><li><p>Extensive experiments show that the proposed framework outperforms state-of-the-art methods with higher accuracy, even compared with publicly available datasets.</p></li></ul></div></div></div>

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Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach

Journal of Physics Conference Series ◽

10.1088/1742-6596/2128/1/012011 ◽

2021 ◽

Vol 2128 (1) ◽

pp. 012011

Author(s):

Wessam M. Salama ◽

Moustafa H. Aly ◽

Azza M. Elbagoury

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Data Augmentation ◽

Lung Nodule ◽

Wiener Filter ◽

Ct Images ◽

Computational Time ◽

Image Size ◽

National Lung Screening Trial ◽

Generalized Framework

Abstract Lung cancer became a significant health problem worldwide over the past decades. This paper introduces a new generalized framework for lung cancer detection where many different strategies are explored for the classification. The ResNet50 model is applied to classify CT lung images into benign or malignant. Also, the U-Net, which is one of the most used architectures in deep learning for image segmentation, is employed to segment CT images before classification to increase system performance. Moreover, Image Size Dependent Normalization Technique (ISDNT) and Wiener filter are utilized as the preprocessing phase to enhance the images and suppress the noise. Our proposed framework which comprises preprocessing, segmentation and classification phases, is applied on two databases: Lung Nodule Analysis 2016 (Luna 16) and National Lung Screening Trial (NLST). Data augmentation technique is applied to solve the problem of lung CT images deficiency, and consequently, the overfitting of deep models will be avoided. The classification results show that the preprocessing for the CT lung image as the input for ResNet50-U-Net hybrid model achieves the best performance. The proposed model achieves 98.98% accuracy (ACC), 98.65% area under the ROC curve (AUC), 98.99% sensitivity (Se), 98.43% precision (Pr), 98.86% F1- score and 1.9876 s computational time.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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