scholarly journals Review of computer-based methods for archaeological ceramic sherds reconstruction

2020 ◽  
Vol 11 (23) ◽  
pp. 34 ◽  
Author(s):  
Dariush Eslami ◽  
Luca Di Angelo ◽  
Paolo Di Stefano ◽  
Caterina Pane
Keyword(s):  
Feature Recognition ◽  
Routine Activities ◽  
Manual Method ◽  
Computer Methods ◽  
Skilled Operator ◽  
Automatic Feature Recognition ◽  
Computer Based ◽  
Archaeological Ceramic ◽  
A Site ◽  
Matching Techniques

<p class="VARAbstract">Potteries are the most numerous finds found in archaeological excavations; they are often used to get information about the history, economy, and art of a site. Archaeologists rarely find complete vases but, generally, damaged and in fragments, often mixed with other pottery groups. By using the traditional manual method, the analysis and reconstruction of sherds are performed by a skilled operator. Reviewed papers provided evidence that the traditional method is not reproducible, not repeatable, time-consuming and its results have great uncertainties. To overcome the aforementioned limits, in the last years, researchers have made efforts to develop computer-based methods for archaeological ceramic sherds analysis, aimed at their reconstruction. To contribute to this field of study, in this paper, a comprehensive analysis of the most important available publications until the end of 2019 is presented. This study, focused on pottery fragments only, is performed by collecting papers in English by the Scopus database using the following keywords: “computer methods in archaeology", "3D archaeology", "3D reconstruction", "automatic feature recognition and reconstruction", "restoration of pottery shape relics”. The list is completed by additional references found through the reading of selected papers. The 53 selected papers are divided into three periods of time. According to a detailed review of the performed studies, the key elements of each analyzed method are listed based on data acquisition tools, features extracted, classification processes, and matching techniques. Finally, to overcome the actual gaps some recommendations for future researches are proposed.</p><p>Highlights:</p><ul><li><p>The traditional manual method for reassembling sherds is very time-consuming and costly; it also requires a great deal effort from skilled archaeologists in repetitive and routine activities.</p></li><li><p>Computer-based methods for archaeological ceramic sherds reconstruction can help archaeologists in the above-mentioned repetitive and routine activities.</p></li><li><p>In this paper, the state-of-the-art computer-based methods for archaeological ceramic sherds reconstruction are reviewed, and some recommendations for future researches are proposed.</p></li></ul>

Using Automatic Feature Recognition to Interface CAD to CAPP

1992 ◽  
Author(s):  
Eric Wang
Keyword(s):  
Pattern Matching ◽  
Feature Recognition ◽  
Scale Up ◽  
Fundamental Difference ◽  
Computer Integrated Manufacturing ◽  
Semantic Gap ◽  
Recognition Method ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Automatic Feature Recognition

Abstract Interfacing CAD to CAPP (computer-aided process planning) is crucial to the eventual success of a fully-automated computer-integrated manufacturing (CIM) environment. Current CAD and CAPP systems are separated by a “semantic gap” that represents a fundamental difference in the ways in which they represent information. This semantic gap makes the interfacing of CAD to CAPP a non-trivial task. This paper argues that automatic feature recognition is an indispensable technique in interfacing CAD to CAPP. It then surveys the current literature on automatic feature recognition methods and systems, and analyzes their suitability as CAD/CAPP interfaces. It also describes a relatively recent automatic feature recognition method based on volumetric decomposition, using Kim’s alternating sum of volumes with partitioning (ASVP) algorithm. The paper’s main theses are: (1) that most previous automatic feature recognition approaches are ultimately based on pattern-matching; (2) that pattern-matching approaches are unlikely to scale up to the real world; and (3) that volumetric decomposition is an alternative to pattern-matching that avoids its shortcomings. The paper concludes that automatic feature recognition by volumetric decomposition is a promising approach to the interfacing of CAD to CAPP.

Is a computer-based measurement method superior to a recommended manual method by the ROHO®Group to assess pressure in the sitting position?

2012 ◽  
pp. n/a-n/a
Author(s):  
Jane Andreasen ◽  
Christian Gammelgaard Olesen ◽  
John Rasmussen ◽  
Susanne Kaasgaard Nielsen ◽  
Lone Nguyen ◽  
...  
Keyword(s):  
Measurement Method ◽  
Sitting Position ◽  
Manual Method ◽  
Computer Based

scholarly journals Computer Methods for Thematic Modeling of Textbooks Collection in Natural Language

2020 ◽  
pp. 68-71
Author(s):  
N.S. Babkina ◽  
A.B. Nugumanova ◽  
N.M. Oskorbin ◽  
O.N. Polovikova ◽  
L.L. Smolyakova
Keyword(s):  
Thematic Analysis ◽  
Information Technologies ◽  
Educational Process ◽  
State University ◽  
Educational Materials ◽  
Text Data ◽  
Computer Methods ◽  
Textual Data ◽  
Computer Based ◽  
Analysis Errors

The paper presents the development results of computer methods for analyzing text data and assessing classification inaccuracies at the stages of thematic modeling. This study uses as an example the task to process textual data of a collection of graduate qualification works prepared and defended by students of Altai State University, Faculty of Mathematics and IT in recent years. The main results obtained in the paper are listed as follows. Relevant application areas and directions for computer methods and thematic modeling in the educational process are identified. Justification of the general algorithm for solving the problem of the thematic analysis of collections of educational materials is carried out. Information technologies for thematic modeling are developed, and estimation of analysis errors on a set of test documents is obtained. It is shown that computer-based methods of thematic modeling and information technology to support them can be used both in the educational process and in the development of educational and methodological documents.

scholarly journals MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Shuhui Ding ◽  
Qiang Feng ◽  
Zhaoyang Sun ◽  
Fai Ma
Keyword(s):  
Feature Recognition ◽  
Cad Model ◽  
3D Cad ◽  
Automatic Feature Recognition

Evaluation of HER-2 expression in breast cancer using image analysis incorporating automatic feature recognition

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 20007-20007
Author(s):  
C. Murray ◽  
J. Maddison ◽  
C. Anderson ◽  
D. Challender ◽  
S. Palmer ◽  
...  
Keyword(s):  
Image Analysis ◽  
Protein Expression ◽  
Feature Recognition ◽  
Dynamic Range ◽  
In Situ Hybridisation ◽  
Automated Image Analysis ◽  
City Hospital ◽  
Breast Cancers ◽  
Automatic Feature Recognition ◽  
Her 2

20007 The currently favoured method for evaluation of HER-2 in routine clinical practice and research studies is immunohistochemistry (IHC). As standardised scoring of protein expression, using a scale of 0 - 3+, generates a significant number of false positives, fluorescent in situ hybridisation (FISH) is used to confirm the presence of gene amplification. Both techniques are laborious, and in the case of by-eye scoring of IHC, semi-quantitative at best. We have developed a high-throughput platform for the quantitative analysis of immunostained slides, based on fast, high-resolution scanning followed by analysis of digitised images (IA) using proprietary software, and in this study compared results obtained using this platform with those obtained using conventional methods. Archival sections of primary breast cancers collected at Nottingham City Hospital in 2004 and 2005 were stained for HER-2 (Herceptest, Dako), and evaluated by eye. Equal numbers of slides from scoring categories 0–3+ were then selected for further image analysis. The digitised images were subjected to automatic delineation to define areas of tumour parenchyma, and these areas further analysed using colour segmentation. Between 100 and 2000 fields were quantified on each section. Staining was expressed as a product of field fraction of coloured pixels and optical density. The results of IA demonstrate a continuum of staining values over the four conventional by-eye categories, with a non-linear correlation to by-eye scores. Inter-sample variation was greatest in the 3+ category, although the mean was much higher than that of the 2+ samples. The 2+ samples showed some variation, with several values not rising above baseline. To further investigate the relationship between FISH scores and IA results in the 2+ category, we analysed an additional set of slides in this group, and found a correlation between FISH and automated IA scores. We conclude that automated image analysis is sensitive to small differences in protein expression, has a wide dynamic range, and provides data superior to conventional by-eye scoring. In undecided cases HER-2 protein expression correlates with FISH data; therefore ultimately IA of HER-2 protein expression alone may provide a basis for clinical decisions. [Table: see text]

Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems

2010 ◽  
Vol 25 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Bojan R. Babić ◽  
Nenad Nešić ◽  
Zoran Miljković
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Feature Recognition ◽  
System Development ◽  
Planning System ◽  
Computer Aided Process Planning ◽  
Design And Manufacturing ◽  
Automatic Feature Recognition ◽  
Feature Technology ◽  
Artificial Neural

AbstractFeature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.

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