Title :
Overlapping object recognition: a paradigm for multiple sensor fusion
Author :
Kim, Intaek ; Vachtsevanos, George
Author_Institution :
Dept. of Control & Instrum. Eng., Myongji Univ., Yongin, South Korea
fDate :
9/1/1998 12:00:00 AM
Abstract :
Recognizing and identifying overlapping or occluded objects is a problem typically encountered in a manufacturing setting. The resultant image distortion tends to limit the applicability of current recognition systems in that case. The proposed recognition scheme involves the utility of an appropriate suite of complementary sensors and is based upon a systematic methodology that addresses the modeling problem through a polygonal approximation and the matching task between the sensor data and stored templates through a construction, called the intervertex matrix. An example is included to illustrate the simplicity and flexibility of the proposed approach
Keywords :
approximation theory; computational geometry; content-addressable storage; feature extraction; image matching; matrix algebra; neural nets; object recognition; sensor fusion; Dempster shafer theory; associative memory; feature extraction; image distortion; image matching; intervertex matrix; multiple sensor fusion; neural nets; occluded objects; overlapping object recognition; polygonal approximation; Application software; Image analysis; Image segmentation; Manufacturing; Military computing; Object recognition; Robotics and automation; Sensor fusion; Sensor systems; Shape;
Journal_Title :
Robotics & Automation Magazine, IEEE