Semi-automatic feature point extraction using one seed point

This study proposes a new semi-automatic feature detection algorithm using one seed point to provide precise searching for feature points. The proposed method is essentially composed of two steps: building search information and searching process. A search graph, containing nodes and its access rela...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 51; no. 1-4; pp. 277 - 295
Main Authors Hsu, Sheng-Han, Lai, Jiing-Yih
Format Journal Article
LanguageEnglish
Published London Springer-Verlag 01.11.2010
Springer Nature B.V
Subjects
Algorithms
CAE) and Design
Computer-Aided Engineering (CAD
Curvature
Engineering
Feature extraction
Industrial and Production Engineering
Mechanical Engineering
Media Management
Nodes
Original Article
Search process
Ravine
Feature detection
Reverse engineering
Uniform-cost search
Triangular meshes
Ridge
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Summary:This study proposes a new semi-automatic feature detection algorithm using one seed point to provide precise searching for feature points. The proposed method is essentially composed of two steps: building search information and searching process. A search graph, containing nodes and its access relationship, provides the candidate points for the search process. A bi-directional, multi-segment search strategy is then proposed to determine the optimized feature path. The cost function is essentially composed of four terms, in which the first two terms are employed to track the nodes of similar maximum curvatures and directions of minimum curvature variation, while the last two terms are employed to stabilize the path. Each of the costs is explained in detail, and examples are presented to show the effect of each of them. In addition, several examples are presented to demonstrate the feasibility of the proposed approach.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-010-2617-3