Information theoretic approach to model-based 3D object recognition using orthogonal transforms

1993 ◽  
Author(s):  
Inderpreet S. Khurana ◽  
Richard F. Vaz ◽  
David Cyganski ◽  
Charles R. Wright
Keyword(s):  
Object Recognition ◽  
3D Object Recognition ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
3D Object ◽  
Model Based ◽  
Orthogonal Transforms ◽  
Information Theoretic Approach

COMBINED MODEL-BASED 3D OBJECT RECOGNITION

2005 ◽  
Vol 19 (07) ◽  
pp. 839-852 ◽  
Author(s):  
SUNGHO KIM ◽  
GIJEONG JANG ◽  
WANG-HEON LEE ◽  
IN SO KWEON
Keyword(s):  
Object Recognition ◽  
Object Identification ◽  
Feature Binding ◽  
Human Vision ◽  
3D Object Recognition ◽  
Top Down ◽  
Combined Model ◽  
Bottom Up ◽  
3D Object ◽  
Model Based

This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

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