Automatic feature recognition and morphology generation

1993 ◽  
Author(s):  
M. P. Gough
Keyword(s):  
Feature Recognition ◽  
Automatic Feature Recognition

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.

Learning and Reasoning Techniques for Automatic Feature Recognition from CAD Models

2006 ◽  
pp. 77-82 ◽  
Author(s):  
E.B. Brousseau ◽  
S.S. Dimov ◽  
R.M. Setchi
Keyword(s):  
Feature Recognition ◽  
Cad Models ◽  
Automatic Feature Recognition

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

scholarly journals A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene

2018 ◽  
Vol 7 (1) ◽  
pp. 28 ◽  
Author(s):  
Xu-Feng Xing ◽  
Mir-Abolfazl Mostafavi ◽  
Seyed Chavoshi
Keyword(s):  
Knowledge Base ◽  
Feature Recognition ◽  
Point Clouds ◽  
Automatic Feature Recognition ◽  
Urban Scene

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 and tool path strategies for enhancing accuracy in double sided incremental forming

2016 ◽  
Vol 88 (5-8) ◽  
pp. 1639-1655 ◽  
Author(s):  
R. Lingam ◽  
Om Prakash ◽  
J. H. Belk ◽  
N. V. Reddy
Keyword(s):  
Feature Recognition ◽  
Tool Path ◽  
Incremental Forming ◽  
Automatic Feature Recognition

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.

The data explosion: tackling the taboo of automatic feature recognition in airborne survey data

Antiquity ◽  
2014 ◽  
Vol 88 (341) ◽  
pp. 896-905 ◽  
Author(s):  
Rebecca Bennett ◽  
Dave Cowley ◽  
Véronique De Laet
Keyword(s):  
Feature Detection ◽  
Feature Recognition ◽  
Remote Sensing Data ◽  
New Wave ◽  
Computer Aided ◽  
Automatic Feature Recognition ◽  
Photographic Survey ◽  
Types Of Information ◽  
Airborne Survey ◽  
New Generation

The increasing availability of multi-dimensional remote-sensing data covering large geographical areas is generating a new wave of landscape-scale research that promises to be as revolutionary as the application of aerial photographic survey during the twentieth century. Data are becoming available to historic environment professionals at higher resolution, greater frequency of acquisition and lower cost than ever before. To take advantage of this explosion of data, however, a paradigm change is needed in the methods used routinely to evaluate aerial imagery and interpret archaeological evidence. Central to this is a fuller engagement with computer-aided methods of feature detection as a viable way to analyse airborne and satellite data. Embracing the new generation of vast datasets requires reassessment of established workflows and greater understanding of the different types of information that may be generated using computer-aided methods.

Ontology based automatic feature recognition framework

2014 ◽  
Vol 65 (7) ◽  
pp. 1041-1052 ◽  
Author(s):  
Qingmai Wang ◽  
Xinghuo Yu
Keyword(s):  
Feature Recognition ◽  
Automatic Feature Recognition

Automatic feature recognition for plastic injection moulded part design

2005 ◽  
Vol 27 (11-12) ◽  
pp. 1058-1070 ◽  
Author(s):  
C.K. Mok ◽  
F.S.Y. Wong
Keyword(s):  
Feature Recognition ◽  
Automatic Feature Recognition ◽  
Plastic Injection
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