Cortical dynamics of attentive object recognition, scene understanding, and decision making.

2013 ◽  
pp. 165-191 ◽  
Author(s):  
Stephen Grossberg
Keyword(s):  
Decision Making ◽  
Object Recognition ◽  
Scene Understanding ◽  
Cortical Dynamics

Nonparametric Object Recognition With a New Tactile Sensor

1992 ◽  
Author(s):  
S. Unsal ◽  
A. Shirkhodaie ◽  
A. H. Soni
Keyword(s):  
Decision Making ◽  
Object Recognition ◽  
Sensory Information ◽  
Tactile Sensor ◽  
Operating Environment ◽  
Tactile Sensors ◽  
Binary Array ◽  
Software And Hardware ◽  
Robotic Applications

Abstract Adding sensing capability to a robot provides the robot with intelligent perception capability and flexibility of decision making. To perform intelligent tasks, robots are highly required to perceive their operating environment, and react accordingly. With this regard, tactile sensors offer to extend the scope of intelligence of a robot for performing tasks which require object touching, recognition, and manipulation. This paper presents the design of an inexpensive pneumatic binary-array tactile sensor for such robotic applications. The paper describes some of the techniques implemented for object recognition from binary sensory information. Furthermore, it details the development of software and hardware which facilitate the sensor to provide useful information to a robot so that the robot perceives its operating environment during manipulation of objects.

A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images

2013 ◽  
Vol 2 (2) ◽  
pp. 66-79 ◽  
Author(s):  
Onsy A. Abdel Alim ◽  
Amin Shoukry ◽  
Neamat A. Elboughdadly ◽  
Gehan Abouelseoud
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Object Recognition ◽  
Probabilistic Neural Network ◽  
Training Procedure ◽  
Generalization Capability ◽  
Mine Detection ◽  
Recognition Of Objects

In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The proposed module takes advantage of both the generalization capability of neural networks and the possibility of manipulating 3-D images to generate views at different poses of the object that is to be recognized. This allows the construction of a robust 3-D object recognition module that can find use in various applications including military, biomedical and mine detection applications. The paper proposes an efficient training procedure and decision making strategy for the suggested neural network. Sample results of testing the module on 3-D images of several objects are also included along with an insightful discussion of the implications of the results.

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