Authors:
Ken'ichi Fujimoto
;
Mio Musashi
and
Tetsuya Yoshinaga
Affiliation:
The University of Tokushima, Japan
Keyword(s):
Dynamic image segmentation, Coupled system, Chaotic neurons, Gray scale image, Multi-scaling of gray levels.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this paper, we describe an image segmentation technique for a gray scale image by utilizing the nonlinear dynamics of two respective discrete-time dynamical systems. The authors have proposed a discrete-time dynamical system that consists of a global inhibitor and chaotic neurons that can generate oscillatory responses. By utilizing oscillatory responses, our system can perform dynamic image segmentation, which denotes segmenting image regions in an image and concurrently exhibiting segmented images in time series, for a binary image. In order that our system can work for a gray scale image, we introduce a multi-scaling system as a pre-processing unit of our system. It is also made of a discrete-time dynamical system and can find an image region composed of pixels with different gray levels by multi-scaling gray levels of pixels. In addition, it can compute the proximity between pixels based on their multi-scaled gray levels. Computed proximity becomes significant information for
designing parameters in our system. We demonstrated that our dynamic image segmentation system with the multi-scaling system works well for a gray scale image.
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