scholarly journals A Comparative Study of Point-Based Deep Learning Techniques for Semantic Classification in Search and Rescue Arenas

2021 ◽  
Author(s):  
Kaya TURGUT ◽  
Burak KALECİ
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Search And Rescue ◽  
Semantic Classification ◽  
Learning Techniques

Comparative Study of Deep Learning Techniques Used for Speech Enhancement

2021 ◽  
Author(s):  
Ajay S ◽  
Manisha R ◽  
Pranav Maheshkumar Nivarthi ◽  
Sai Harsha Nadendla ◽  
C Santhosh Kumar
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Speech Enhancement ◽  
Learning Techniques

A Comparative Study of Deep Learning Techniques for Financial Indices Prediction

2021 ◽  
pp. 297-308
Author(s):  
Argyrios P. Ketsetsis ◽  
Konstantinos M. Giannoutakis ◽  
Georgios Spanos ◽  
Nikolaos Samaras ◽  
Dimitrios Hristu-Varsakelis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Learning Techniques ◽  
Financial Indices

scholarly journals A Comparative Study of Deep Learning Techniques on Frame-Level Speech Data Classification

2019 ◽  
Vol 38 (8) ◽  
pp. 3501-3520 ◽  
Author(s):  
Abdolreza Sabzi Shahrebabaki ◽  
Ali Shariq Imran ◽  
Negar Olfati ◽  
Torbjørn Svendsen
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Data Classification ◽  
Learning Techniques ◽  
Speech Data

scholarly journals Multiclass semantic segmentation for digitisation of movable heritage using deep learning techniques

2021 ◽  
Vol 12 (25) ◽  
pp. 85
Author(s):  
Giacomo Patrucco ◽  
Francesco Setragno
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Semantic Classification ◽  
Object Categories ◽  
Network Training ◽  
Acquisition Strategies ◽  
Learning Techniques ◽  
High Degree

<p class="VARAbstract">Digitisation processes of movable heritage are becoming increasingly popular to document the artworks stored in our museums. A growing number of strategies for the three-dimensional (3D) acquisition and modelling of these invaluable assets have been developed in the last few years. Their objective is to efficiently respond to this documentation need and contribute to deepening the knowledge of the masterpieces investigated constantly by researchers operating in many fieldworks. Nowadays, one of the most effective solutions is represented by the development of image-based techniques, usually connected to a Structure-from-Motion (SfM) photogrammetric approach. However, while images acquisition is relatively rapid, the processes connected to data processing are very time-consuming and require the operator’s substantial manual involvement. Developing deep learning-based strategies can be an effective solution to enhance the automatism level. In this research, which has been carried out in the framework of the digitisation of a wooden maquettes collection stored in the ‘Museo Egizio di Torino’, using a photogrammetric approach, an automatic masking strategy using deep learning techniques is proposed, to increase the level of automatism and therefore, optimise the photogrammetric pipeline. Starting from a manually annotated dataset, a neural network was trained to automatically perform a semantic classification to isolate the maquettes from the background. The proposed methodology allowed the researchers to obtain automatically segmented masks with a high degree of accuracy. The workflow is described (as regards acquisition strategies, dataset processing, and neural network training). In addition, the accuracy of the results is evaluated and discussed. Finally, the researchers proposed the possibility of performing a multiclass segmentation on the digital images to recognise different object categories in the images, as well as to define a semantic hierarchy to perform automatic classification of different elements in the acquired images.</p><p><strong>Highlights:</strong></p><ul><li><p>In the framework of movable heritage digitisation processes, many procedures are very time-consuming, and they still require the operator’s substantial manual involvement.</p></li><li><p>This research proposes using deep learning techniques to enhance the automatism level in the generation of exclusion masks, improving the optimisation of the photogrammetric procedures.</p></li><li><p>Following this strategy, the possibility of performing a multiclass semantic segmentation (on the 2D images and, consequently, on the 3D point cloud) is also discussed, considering the accuracy of the obtainable results.</p></li></ul>

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