Object Recognition based on a Simplified PCNN

Title
Object Recognition based on a Simplified PCNN
Authors
율리첸이다마김동환박성기
Keywords
computer vision; object recognition; 2D neural network; Region- based Matching; Simplified Pulse Coupled Neural Network (SPCNN); Image Segmentation
Issue Date
2012-08
Publisher
International Conference on informatics in control, automation and robotics
Citation
, 223-229
Abstract
The aim of the paper is to propose a region-based object recognition method to identify objects from complex real-world scenes. The proposed method firstly performs a colour image segmentation by a simplified pulse coupled neural network (SPCNN) model, and the parameters of the SPCNN are automatically set by our previously proposed parameter setting method. Subsequently, the proposed method performs a region-based matching between a model object image and a test image. A large number of object recognition experiments have proved that the proposed method is robust against the variations in translation, rotation, scale and illumination, even under partial occlusion and highly clutter backgrounds. Also it shows a good performance in identifying less-textured objects, which significantly outperforms most feature-based methods.
URI
http://pubs.kist.re.kr/handle/201004/43863
Appears in Collections:
KIST Publication > Conference Paper
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