Efficient, parallel, and invariant shape recognition algorithms for complex industrial tasks

2002 ◽  
Author(s):  
Hanjin Lee ◽  
D. Sinha
Keyword(s):  
Shape Recognition ◽  
Recognition Algorithms ◽  
Invariant Shape

scholarly journals EFFECT OF HIGH CURVATURE POINT DELETION ON THE PERFORMANCE OF TWO CONTOUR BASED SHAPE RECOGNITION ALGORITHMS

2006 ◽  
Vol 20 (06) ◽  
pp. 913-924 ◽  
Author(s):  
ANARTA GHOSH ◽  
NICOLAI PETKOV
Keyword(s):  
Shape Recognition ◽  
Test Procedure ◽  
Recognition Rate ◽  
Shape Context ◽  
Human Visual Perception ◽  
Local Shape ◽  
Object Play ◽  
Recognition Algorithms ◽  
Local Shape Descriptors ◽  
Degradation Effect

Psychophysical researches on the human visual system have shown that the points of high curvature on the contour of an object play an important role in the recognition process. Inspired by these studies we propose: (i) a novel algorithm to select points of high curvature on the contour of an object which can be used to construct a recognizable polygonal approximation, (ii) a test which evaluates the effect of deletion of contour segments containing such points on the performance of contour based object recognition algorithms. We use complete contour representations of objects as a reference (training) set. Incomplete contour representations of the same objects are used as a test set. The performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained. We consider two types of contour incompleteness obtained by deletion of contour segments of high or low curvature. We illustrate the test procedure using two shape recognition algorithms that deploy a shape context and a distance multiset as local shape descriptors. Both algorithms qualitatively mimic human visual perception in that the deletion of segments of high curvature has a stronger performance degradation effect than the deletion of other parts of the contour. This effect is more pronounced in the performance of the shape context method.

Affine-invariant Shape Recognition Using Grassmann Manifold

2012 ◽  
Vol 38 (2) ◽  
pp. 248-258 ◽  
Author(s):  
Yun-Peng LIU ◽  
Guang-Wei LI ◽  
Ze-Lin SHI
Keyword(s):  
Grassmann Manifold ◽  
Shape Recognition ◽  
Affine Invariant ◽  
Invariant Shape
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