Improving nonlinear up-scaling by adapting to the local edge orientation

2004 ◽  
Author(s):  
Jeroen A. P. Tegenbosch ◽  
Paul M. Hofman ◽  
Marco K. Bosma
Keyword(s):  
Edge Orientation ◽  
Local Edge ◽  
Up Scaling

On the role of edge orientation in stereo vision

2019 ◽  
Vol 2019 (12) ◽  
pp. 209-1-209-6
Author(s):  
Alfredo Restrepo ◽  
Julian Quiroga
Keyword(s):  
Stereo Vision ◽  
Edge Orientation

Machine Learning Based Assembly of Fragments of Ancient Papyrus

2021 ◽  
Vol 14 (3) ◽  
pp. 1-21
Author(s):  
Roy Abitbol ◽  
Ilan Shimshoni ◽  
Jonathan Ben-Dov
Keyword(s):  
Machine Learning ◽  
Spatial Information ◽  
Real Life ◽  
Dead Sea ◽  
Machine Learning Techniques ◽  
Automated Classification ◽  
Learning Techniques ◽  
Test Batch ◽  
Validation Set ◽  
Local Edge

The task of assembling fragments in a puzzle-like manner into a composite picture plays a significant role in the field of archaeology as it supports researchers in their attempt to reconstruct historic artifacts. In this article, we propose a method for matching and assembling pairs of ancient papyrus fragments containing mostly unknown scriptures. Papyrus paper is manufactured from papyrus plants and therefore portrays typical thread patterns resulting from the plant’s stems. The proposed algorithm is founded on the hypothesis that these thread patterns contain unique local attributes such that nearby fragments show similar patterns reflecting the continuations of the threads. We posit that these patterns can be exploited using image processing and machine learning techniques to identify matching fragments. The algorithm and system which we present support the quick and automated classification of matching pairs of papyrus fragments as well as the geometric alignment of the pairs against each other. The algorithm consists of a series of steps and is based on deep-learning and machine learning methods. The first step is to deconstruct the problem of matching fragments into a smaller problem of finding thread continuation matches in local edge areas (squares) between pairs of fragments. This phase is solved using a convolutional neural network ingesting raw images of the edge areas and producing local matching scores. The result of this stage yields very high recall but low precision. Thus, we utilize these scores in order to conclude about the matching of entire fragments pairs by establishing an elaborate voting mechanism. We enhance this voting with geometric alignment techniques from which we extract additional spatial information. Eventually, we feed all the data collected from these steps into a Random Forest classifier in order to produce a higher order classifier capable of predicting whether a pair of fragments is a match. Our algorithm was trained on a batch of fragments which was excavated from the Dead Sea caves and is dated circa the 1st century BCE. The algorithm shows excellent results on a validation set which is of a similar origin and conditions. We then tried to run the algorithm against a real-life set of fragments for which we have no prior knowledge or labeling of matches. This test batch is considered extremely challenging due to its poor condition and the small size of its fragments. Evidently, numerous researchers have tried seeking matches within this batch with very little success. Our algorithm performance on this batch was sub-optimal, returning a relatively large ratio of false positives. However, the algorithm was quite useful by eliminating 98% of the possible matches thus reducing the amount of work needed for manual inspection. Indeed, experts that reviewed the results have identified some positive matches as potentially true and referred them for further investigation.

Numerical Simulation of the Thermo-catalytic Reforming Process: Up-scaling Study

2021 ◽  
Vol 60 (12) ◽  
pp. 4682-4692
Author(s):  
Mohamed Elmously ◽  
Johannes Neidel ◽  
Andreas Apfelbacher ◽  
Robert Daschner ◽  
Andreas Hornung
Keyword(s):  
Numerical Simulation ◽  
Catalytic Reforming ◽  
Up Scaling

scholarly journals Glossary of terms used in photocatalysis and radiation catalysis (IUPAC Recommendations 2011)

2011 ◽  
Vol 83 (4) ◽  
pp. 931-1014 ◽  
Author(s):  
Silvia E. Braslavsky ◽  
André M. Braun ◽  
Alberto E. Cassano ◽  
Alexei V. Emeline ◽  
Marta I. Litter ◽  
...  
Keyword(s):  
Electromagnetic Radiation ◽  
Fast Moving ◽  
Quantitative Description ◽  
Radiation Chemistry ◽  
Industrial Applications ◽  
Clear Distinction ◽  
Photocatalytic Processes ◽  
Up Scaling

This glossary of terms covers phenomena considered under the very wide terms photocatalysis and radiation catalysis. A clear distinction is made between phenomena related to either photochemistry and photocatalysis or radiation chemistry and radiation catalysis. The term “radiation” is used here as embracing electromagnetic radiation of all wavelengths, but in general excluding fast-moving particles. Consistent definitions are given of terms in the areas mentioned above, as well as definitions of the most important parameters used for the quantitative description of the phenomena. Terms related to the up-scaling of photocatalytic processes for industrial applications have been included. This Glossary should be used together with the Glossary of terms used in photochemistry, 3rd edition, IUPAC Recommendations 2006: (doi:10.1351/pac200779030293) as well as with the IUPAC Compendium of Chemical Terminology, 2nd ed. (the “Gold Book”, 2006– doi:10.1351/goldbook) because many terms used in photocatalysis are defined in these documents.

Flexible organic tandem solar modules: a story of up-scaling

2014 ◽  
Author(s):  
George D. Spyropoulos ◽  
Peter Kubis ◽  
Ning Li ◽  
Luca Lucera ◽  
Michael Salvador ◽  
...  
Keyword(s):  
Up Scaling
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