SMALS: a novel database for two-dimensional object location

1990 ◽  
Vol 9 (1) ◽  
pp. 57-65 ◽  
Author(s):  
A.D. Sherer ◽  
B.S. Stanojevich ◽  
R.J. Bowman
Keyword(s):  
Object Location ◽  
Two Dimensional ◽  
Dimensional Object

A Robot Vision System Based on Two-Dimensional Object-Oriented Models

1986 ◽  
Vol 16 (4) ◽  
pp. 582-589 ◽  
Author(s):  
Lorenz A. Schmitt ◽  
William A. Gruver ◽  
Assad Ansari
Keyword(s):  
Vision System ◽  
Robot Vision ◽  
Object Oriented ◽  
Two Dimensional ◽  
Dimensional Object

Applications of PNC in Numerical Methods

2017 ◽  
pp. 235-268
Keyword(s):  
Probability Distribution ◽  
Shape Representation ◽  
Mathematical Problem ◽  
Distribution Functions ◽  
Two Dimensional ◽  
Key Points ◽  
Characteristic Points ◽  
Curve Modeling ◽  
Curve Contour ◽  
Dimensional Object

Nodes are treated as characteristic points of data for modeling and analyzing. The model of data can be built by choice of probability distribution function and nodes combination. Two-dimensional object is extrapolated and interpolated via nodes combination and different functions as discrete or continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function. Curve interpolation represents one of the most important problems in mathematics and computer science: how to model the curve via discrete set of two-dimensional points? Also the matter of shape representation (as closed curve - contour) and curve parameterization is still opened. For example pattern recognition, signature verification or handwriting identification problems are based on curve modeling via the choice of key points. So interpolation is not only a pure mathematical problem but important task in computer vision and artificial intelligence.

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