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

OBJECT-SHAPE RECOGNITION BY TACTILE IMAGE ANALYSIS USING SUPPORT VECTOR MACHINE

2014 ◽  
Vol 28 (04) ◽  
pp. 1450011 ◽  
Author(s):  
ANWESHA KHASNOBISH ◽  
ARINDAM JATI ◽  
GARIMA SINGH ◽  
AMIT KONAR ◽  
D. N. TIBAREWALA
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
Shape Recognition ◽  
Tactile Sensor ◽  
Computational Time ◽  
Support Vector ◽  
Object Shape ◽  
Tactile Image ◽  
Misclassification Rates ◽  
Object Shapes

The sense of touch is important to human to understand shape, texture, and hardness of the objects. An object under grip, i.e. object exploration by enclosure, provides a unique pressure distribution on the different regions of palm depending on its shape. This paper utilizes the above experience for recognition of object shapes by tactile image analysis. The high pressure regions (HPRs) are segmented and analyzed for object shape recognition rather than analyzing the entire image. Tactile images are acquired by capacitive tactile sensor while grasping a particular object. Geometrical features are extracted from the chain codes obtained by polygon approximation of the contours of the segmented HPRs. Two-level classification scheme using linear support vector machine (LSVM) is employed to classify the input feature vector in respective object shape classes with an average classification accuracy of 93.46% and computational time of 1.19 s for 12 different object shape classes. Our proposed two-level LSVM reduces the misclassification rates, thus efficiently recognizes various object shapes from the tactile images.

A ROBUST AFFINE INVARIANT METRIC ON BOUNDARY PATTERNS

1999 ◽  
Vol 13 (08) ◽  
pp. 1151-1163 ◽  
Author(s):  
MICHIEL HAGEDOORN ◽  
REMCO C. VELTKAMP
Keyword(s):  
Algebraic Surface ◽  
Shape Recognition ◽  
Large Family ◽  
Finite Union ◽  
Connected Components ◽  
Affine Invariant ◽  
Invariant Metric ◽  
Pattern Metrics ◽  
Surface Patches

Affine invariant pattern metrics are useful for shape recognition. It is important that such a metric is robust for various defects. We formalize these types of robustness using four axioms. Then, we present the reflection metric. This is an affine invariant metric defined for the large family of "boundary patterns". A boundary pattern is a finite union of n-1 dimensional algebraic surface patches in ℝn. Such a pattern may have multiple connected components. We prove that the reflection metric satisfies the four robustness axioms.

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