eprintid: 2651
rev_number: 12
eprint_status: archive
userid: 318
dir: disk0/00/00/26/51
datestamp: 2017-12-08 04:59:52
lastmod: 2017-12-08 04:59:52
status_changed: 2017-12-08 04:59:52
type: conference_item
metadata_visibility: show
creators_name: Ma Thi, Chau
creators_name: Park, Chang-soo
creators_name: Suthunyatanakit, Kittichai
creators_name: Oh, Min-jae
creators_name: Kim, Tae-wan
creators_name: Kang, Myung-joo
creators_name: Bui, The Duy
creators_id: duybt@vnu.edu.vn
title: Features Detection from Industrial Noisy 3D CT Data for Reverse Engineering
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: To detect features are significantly important for reconstructing a model in reverse engineering. In general, it is too difficult to find the features from the original industrial 3D CT data because the data have many noises. So it is necessary to reduce the noises for detecting features. This paper proposes a new method for detecting corner features and edge features from noisy 3D CT scanned data. First, we applied the level set method[18] to CT scanned image in order to segment the data. Next, in order to reduce noises, we exploited nonlocal means method[19] to the segmented surface. This helps to detect the edges and corners more accurately. Finally, corners and sharp edges are detected and extracted from the boundary of the shape. The corners are detected based on Sobel-like mask convolution processing with a marching cube. The sharp edges are detected based on Canny-like mask convolution with SUSAN method[13], which is for noises removal. In the paper, the result of detecting both features is presented.
date: 2012
date_type: published
full_text_status: none
pres_type: paper
pagerange: 89-101
event_title: Multimedia, Computer Graphics and Broadcasting
event_type: conference
refereed: TRUE
related_url_url: https://link.springer.com/chapter/10.1007/978-3-642-28670-4_8
citation:   Ma Thi, Chau and Park, Chang-soo and Suthunyatanakit, Kittichai and Oh, Min-jae and Kim, Tae-wan and Kang, Myung-joo and Bui, The Duy  (2012) Features Detection from Industrial Noisy 3D CT Data for Reverse Engineering.  In: Multimedia, Computer Graphics and Broadcasting.