N-dimension geometry used in the design of a dynamic neural-network pattern-recognition system

Chialun John Hu

doi:10.1117/12.805728

19 January 2009 N-dimension geometry used in the design of a dynamic neural-network pattern-recognition system

Chialun John Hu

Proceedings Volume 7252, Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques; 72520T (2009) https://doi.org/10.1117/12.805728
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

If the main features, or the skeleton (e.g., the corner points and the boundary lines,) of a 3D moving object can be represented by an ND analog vector, then the whole history of movement (rotation, translation, deformation, etc.) of this object can be described by an ND curve in the ND state space. Each point on the curve corresponds to a snap-shot of the 3D object at a certain time during the course of movement. We can approximate this ND curve by an ND broken line just like the linearization of a 2D curve by a 2D broken line. But the linearization of a branch of an ND curve is just to apply the ND convex operation to the two end points of this branch. Therefore remembering all the end points (or all the extreme points) in the ND curve will allow us to approximately reconstruct the ND curve, or the whole 3D object's moving history, by means of the simple mathematical operation, the ND convex operation. Based on this ND geometry principle, a very simple, yet very robust, and very accurate dynamic neural network system (a computer graphic program) is proposed for recognizing any moving object not only by its static images, but also by the special way this object moves.

Citation Download Citation

Chialun John Hu "N-dimension geometry used in the design of a dynamic neural-network pattern-recognition system", Proc. SPIE 7252, Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques, 72520T (19 January 2009); https://doi.org/10.1117/12.805728

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available