scholarly journals Shape Similarity Measurement for Boundary Based Features

2005 ◽  
pp. 431-438 ◽  
Author(s):  
Nafiz Arica ◽  
Fatos T. Yarman Vural
Keyword(s):  
Similarity Measurement ◽  
Shape Similarity

scholarly journals Shape Similarity Measurement for Known-Object Localization: A New Normalized Assessment

2019 ◽  
Vol 5 (10) ◽  
pp. 77
Author(s):  
Baptiste Magnier ◽  
Behrang Moradi
Keyword(s):  
Shape Matching ◽  
Performance Measure ◽  
Object Localization ◽  
Similarity Measurement ◽  
Shape Similarity ◽  
Image Normalization ◽  
Object Shape ◽  
Binary Images ◽  
Candidate Image ◽  
Matching Estimation

This paper presents a new, normalized measure for assessing a contour-based object pose. Regarding binary images, the algorithm enables supervised assessment of known-object recognition and localization. A performance measure is computed to quantify differences between a reference edge map and a candidate image. Normalization is appropriate for interpreting the result of the pose assessment. Furthermore, the new measure is well motivated by highlighting the limitations of existing metrics to the main shape variations (translation, rotation, and scaling), by showing how the proposed measure is more robust to them. Indeed, this measure can determine to what extent an object shape differs from a desired position. In comparison with 6 other approaches, experiments performed on real images at different sizes/scales demonstrate the suitability of the new method for object-pose or shape-matching estimation.

Evaluation of shape similarity measurement methods for spine X-ray images

2004 ◽  
Vol 15 (3) ◽  
pp. 285-302 ◽  
Author(s):  
Sameer Antani ◽  
D.J. Lee ◽  
L. Rodney Long ◽  
George R. Thoma
Keyword(s):  
Measurement Methods ◽  
Similarity Measurement ◽  
Shape Similarity ◽  
X Ray

scholarly journals A Moment-Based Shape Similarity Measurement for Areal Entities in Geographical Vector Data

2018 ◽  
Vol 7 (6) ◽  
pp. 208 ◽  
Author(s):  
Zhongliang Fu ◽  
Liang Fan ◽  
Zhiqiang Yu ◽  
Kaichun Zhou
Keyword(s):  
Similarity Measurement ◽  
Shape Similarity ◽  
Vector Data

Correlators for rank order shape similarity measurement

2002 ◽  
Author(s):  
Jason B. Gregga ◽  
Gregory J. Power ◽  
Khan M. Iftekharuddin
Keyword(s):  
Rank Order ◽  
Similarity Measurement ◽  
Shape Similarity

Feature Based Shape Similarity Measurement for Retrieval of Mechanical Parts

2001 ◽  
Vol 1 (3) ◽  
pp. 245-256 ◽  
Author(s):  
Madhumati Ramesh ◽  
Derek Yip-Hoi ◽  
Debasish Dutta
Keyword(s):  
Process Planning ◽  
Decomposition Method ◽  
Manufacturing Industry ◽  
Similarity Measurement ◽  
Shape Similarity ◽  
Machining Features ◽  
Mechanical Parts ◽  
Shape Comparison ◽  
Feature Based ◽  
Minimal Cells

Exploiting shape similarities amongst parts for applications such as variant process planning is well known in the manufacturing industry. This particular application requires a mechanism for retrieval of similar parts from a part database which in turn requires a method for shape similarity measurement. In this paper, such a method is presented. First, the part is decomposed into simpler shapes resembling machining features. The decomposition method makes use of primitives to generate the shapes directly unlike previous methods in which the shapes are produced by combining minimal cells. Next, part characteristics that capture the spatial and dimensional relationships amongst features are used to measure the similarity. These characteristics are relevant to machining and they complement the characteristics such as feature type and feature intersections that are used by the previous shape comparison techniques. Implementation and examples are also included.

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