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The computerization of archaeology: survey on AI techniques [article]

Lorenzo Mantovan, Loris Nanni
2020 arXiv   pre-print
analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites,  ...  of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological  ...  based on the appearance is obtained using a Convolutional Neural Network (CNN) [32] .  ... 
arXiv:2005.02863v2 fatcat:b6cue5u2bbdrpmew4coelmcytq

The Computerization of Archaeology: Survey on Artificial Intelligence Techniques

Lorenzo Mantovan, Loris Nanni
2020 SN Computer Science  
the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites  ...  of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; (2) the analysis of methods for the classification of fragments found in archaeological  ...  based on the appearance is obtained using a convolutional neural network (CNN) [31] .  ... 
doi:10.1007/s42979-020-00286-w fatcat:nlaiijctrbbd5j42dqna5xcmwe

AUTOMATIC IDENTIFICATION OF ARCHAEOLOGICAL ARTIFACTS ON THE EXCAVATION SITE

E. Alby, V. Desbiolles, M. Lecocq
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
It is an image-based network. What is sought here is the ability to recognize an object for a neural network.  ...  The hypothesis formulated here is that excavation reports can be used as a source for creating learning data sets of neural networks dedicated to the recognition of archaeological objects on site.  ...  ceramic shards engraved with a particular reason (Chetouani et al., 2018) .  ... 
doi:10.5194/isprs-archives-xliii-b2-2020-1347-2020 fatcat:7h6ibavayfhnriq7wkomyr4v4q

ICA Mixtures Applied to Ultrasonic Nondestructive Classification of Archaeological Ceramics

Addisson Salazar, Luis Vergara
2010 EURASIP Journal on Advances in Signal Processing  
We consider a classifier based on Independent Component Analysis Mixture Modelling (ICAMM) to model the feature jointprobability density.  ...  This classifier is applied to a challenging novel application: classification of archaeological ceramics.  ...  In this paper, we consider a method to sort archaeological ceramic shards based on ultrasonic nondestructive evaluation.  ... 
doi:10.1155/2010/125201 fatcat:o3lkb2t4mbgdtmuggdronuvsjm

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  , uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4598227 fatcat:hm2ksetmsvf37adjjefmmbakvq

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  , uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4591029 fatcat:zn2hvfyupvdwlnvsscdgswayci

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  , uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4399748 fatcat:63ggmnviczg6vlnqugbnrexsgy

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  , uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4413249 fatcat:35qbhenysfhvza2roihx52afuy

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  , uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4429792 fatcat:qs6yuapx4vdbdmwna7ix7nnwty

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  , uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4415407 fatcat:6dejwzzpmfegnfuktrld6zgpiq

Detection of pyrrolizidine alkaloid containing herbs using hyperspectral imaging in the short-wave infrared

Julius Krause, Nanina Tron, Georg Maier, Andrea Krähmer, Robin Gruna, Thomas Längle, Jürgen Beyerer
2021
By using a convolutional neural network, a mean F 1 score of 0.89 was obtained for the classification of all four plant products based on the individual spectra.  ...  The detection of these plants or their components using hyperspectral imaging was investigated, with focus on application in sensor-based sorting.  ...  (FNR) (FKZ 220132165) on the basis of a resolution of the German Bundestag and the Fraunhofer Center for Machine Learning within the Fraunhofer Cluster of Excellence Cognitive Internet Technologies CCIT  ... 
doi:10.5445/ir/1000130827 fatcat:pry4wxnk25dwvehs7zjgkl6r64

Micro-Thermal Analysis and Related Techniques [chapter]

Duncan M. Price
2008 Handbook of Thermal Analysis and Calorimetry  
The training set for the neural network comprises images acquired on materials of homogeneous thermal properties with a range of surfaces roughnesses.  ...  Illustration of the convolution of surface roughness on the apparent thermal conductivity (k) measured by the tip during a line scan.  ... 
doi:10.1016/s1573-4374(08)80006-4 fatcat:3qna2skxojbb5ipdgoqnp2f2zy

Spatiotemporal enabled Content-based Image Retrieval

Mariana Belgiu, Martin Sudmanns, Tiede Dirk, Andrea Baraldi, Stefan Lang
2016 International Conference on GIScience Short Paper Proceedings  
In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment.  ...  The efficiency of the coverage of a sensor network depends on optimal position of each sensor node within the network.  ...  Acknowledgements This collaborative research was funded by British Academy Newton Advanced Fellowships NG150097, Developing a housing model based on the status-quality trade off theory.  ... 
doi:10.21433/b311729295dw fatcat:fulw4pw3kfh5nmfzcsy3pkisvm

Stature Estimation [chapter]

2005 Fundamentals of Forensic Anthropology  
Simpson trial, a plethora of documentary-style forensic programs appeared on several networks.  ...  The wound is larger on the internal surface of the hole than on the external surface. This internal beveling is produced by shards of bone knocked free from the internal surface.  ... 
doi:10.1002/0470007729.ch7 fatcat:yq7zg4lo3zc5npgmplpg6rtzfm

New Home Cemetery (41FB334): Archaeological Search Exhumation, and Reinterment of Multiple Historic Graves along FM 1464, Sugar Land, Fort Bend County, Texas

Mary Hill, Jeremy Pye
2012 Index of Texas Archaeology Open Access Grey Literature from the Lone Star State  
shells, a mason jar, fragments of broken glass, fragments of necks and bases of bottles, animal bones, ceramic tiles, fragments of ceramic plates and saucers, a coffee sieve, a roll of movie film, a fragment  ...  Views of ceramic plate associated with Burial 8 Figure 21 . Views of ceramic plate associated with Burial 8. The individual was represented by elements of both legs, plus lower left arm.  ... 
doi:10.21112/ita.2012.1.1 fatcat:afc2phdup5a4lhuyfxwgiboj4y
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