Enhancement of a genetic algorithm for affine invariant planar object shape matching using the migrant principle

2003 ◽  
Vol 150 (2) ◽  
pp. 107 ◽  
Author(s):  
P.W.M. Tsang
Keyword(s):  
Genetic Algorithm ◽  
Shape Matching ◽  
Affine Invariant ◽  
Object Shape

A genetic algorithm for affine invariant object shape recognition

1997 ◽  
Vol 211 (5) ◽  
pp. 385-392 ◽  
Author(s):  
P W M Tsang
Keyword(s):  
Genetic Algorithm ◽  
Affine Transformation ◽  
Shape Recognition ◽  
Affine Invariant ◽  
Object Shape ◽  
Novel Technique ◽  
Dominant Point ◽  
Camera Position ◽  
Object Shapes ◽  
Noise Contamination

In this paper, a novel technique for matching images of object shapes which have been subject to affine transformation caused by variations in the camera position is reported. The method is based on the genetic algorithm, and is more efficient and reliable than conventional approaches that rely on corresponding dominant point pairs to determine the best alignment between object boundaries. Experimental results are presented to demonstrate the feasibility of the approach and its capability in identifying object shapes that had been distorted with heavy noise contamination

scholarly journals Adaptive locally affine-invariant shape matching

2018 ◽  
Vol 29 (4) ◽  
pp. 553-572 ◽  
Author(s):  
Smit Marvaniya ◽  
Raj Gupta ◽  
Anurag Mittal
Keyword(s):  
Shape Matching ◽  
Affine Invariant ◽  
Invariant Shape

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.

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