Cellular neural network architecture for Gibbs random-field-based image segmentation

1998 ◽  
Vol 7 (1) ◽  
pp. 45 ◽  
Author(s):  
Lulin Chen
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Random Field ◽  
Network Architecture ◽  
Cellular Neural Network ◽  
Neural Network Architecture ◽  
Gibbs Random Field

scholarly journals Human Perception Based Color Image Segmentation

2013 ◽  
Vol 2 (3) ◽  
Author(s):  
Neeta Pradeep Gargote ◽  
Savitha Devaraj ◽  
Shravani Shahapure
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Color Image ◽  
Color Space ◽  
Human Perception ◽  
Activation Function ◽  
Color Image Segmentation ◽  
Neural Network Architecture ◽  
Hsi Color Space

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images.

Range image segmentation by dynamic neural network architecture

1996 ◽  
Vol 29 (2) ◽  
pp. 315-329 ◽  
Author(s):  
V. Chandrasekaran ◽  
M. Palaniswami ◽  
Terry M. Caelli
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Range Image ◽  
Neural Network Architecture ◽  
Dynamic Neural Network

TEMPLATE LEARNING OF CELLULAR NEURAL NETWORK USING GENETIC PROGRAMMING

2004 ◽  
Vol 14 (04) ◽  
pp. 247-256 ◽  
Author(s):  
ELSAYED RADWAN ◽  
EIICHIRO TAZAKI
Keyword(s):  
Neural Network ◽  
Genetic Programming ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Good Control ◽  
Cellular Neural Network ◽  
Current Approach ◽  
Neural Network Architecture ◽  
Stable Domain ◽  
New Learning

A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.

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