The unknown object problem in a sea with sloping bottom

2001 ◽  
Vol 78 (3-4) ◽  
pp. 497-511 ◽  
Author(s):  
R.P. Gilbert ◽  
Miao-Jung OU ◽  
Yongzhi S. XU
Keyword(s):  
Object Problem ◽  
Sloping Bottom ◽  
Unknown Object

DETERMINATION OF A BURIED OBJECT IN A TWO-LAYERED SHALLOW OCEAN

2001 ◽  
Vol 09 (03) ◽  
pp. 1025-1037 ◽  
Author(s):  
ROBERT P. GILBERT ◽  
MICHAEL WERBY ◽  
YONGZHI XU
Keyword(s):  
Born Approximation ◽  
Two Dimensional ◽  
Buried Object ◽  
Object Problem ◽  
Dimensional Object ◽  
Unknown Object

In this paper, a method for solving the unknown object problem is presented for a two-dimensional object buried in a fluid seabed lying over a rigid basement. The algorithm makes use of a regularized Born approximation. Several examples are provided which demonstrate the utility of the algorithm.

scholarly journals Algorithm for recognition of details in the machine vision systems at automation of assembly processes

2018 ◽  
Vol 224 ◽  
pp. 01088 ◽  
Author(s):  
Yaroslav Kulkov ◽  
Arkady Zhiznyakov ◽  
Denis Privezentsev
Keyword(s):  
Computer Vision ◽  
Experimental Research ◽  
Mean Square ◽  
Robot Arm ◽  
Vision Systems ◽  
Binary Images ◽  
Image Contour ◽  
Computer Vision Systems ◽  
Assembly Operations ◽  
Unknown Object

The aim is an experimental research on the flat objects recognition using dimensionless marks of the contours of their binary images and determining the possibility of applying this method in computer vision systems of assembly robots. The main problem with the automation of assembly operations is the recognition of parts for the subsequent picking up of the robot arm. The basis for the formation of attribute vectors is the characteristics of the image contour. Recognition of a class of an unknown object consists in receipt of its contour, calculation of primary parameters and forming of a vector of dimensionless marks. Further mean square deviations of its vector of dimensionless marks from all reference are calculated. The minimum value of a deviation will specify probable belonging to the corresponding class.

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