Three-dimensional object feature extraction and classification with computational holographic imaging

2004 ◽  
Vol 43 (2) ◽  
pp. 442 ◽  
Author(s):  
Sekwon Yeom ◽  
Bahram Javidi
1993 ◽  
Vol 5 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Nathan Intrator ◽  
Joshua I. Gold

We propose an object recognition scheme based on a method for feature extraction from gray level images that corresponds to recent statistical theory, called projection pursuit, and is derived from a biologically motivated feature extracting neuron. To evaluate the performance of this method we use a set of very detailed psychophysical three-dimensional object recognition experiments (Bülthoff and Edelman 1992).


Author(s):  
Elrnar Zeitler

Considering any finite three-dimensional object, a “projection” is here defined as a two-dimensional representation of the object's mass per unit area on a plane normal to a given projection axis, here taken as they-axis. Since the object can be seen as being built from parallel, thin slices, the relation between object structure and its projection can be reduced by one dimension. It is assumed that an electron microscope equipped with a tilting stage records the projectionWhere the object has a spatial density distribution p(r,ϕ) within a limiting radius taken to be unity, and the stage is tilted by an angle 9 with respect to the x-axis of the recording plane.


Sign in / Sign up

Export Citation Format

Share Document