Abstract
In this paper, multispectral image segmentation using a rough neural network based on an annealed strategy with a cooling schedule is created. The main purpose is to embed an annealed cooling schedule into the rough neural network to construct a segmentation system named annealed rough neural net (ARNN). The classification system is a paradigm for the implementation of annealed reasoning and rough systems in neural network architecture. Instead of all the information in the image are fed into the neural network, the upper- and lower-bound gray level, captured from a training vector in a multispectral image, were fed into a rough neuron in the ARNN. Therefore, only 2-channel images are selected as the training samples if an N-dimensional multispectral image was used. In the simulation results, the proposed network not only reduces the consuming time but also reserves the classification performance.
| Original language | English |
|---|---|
| Pages (from-to) | 911-919 |
| Number of pages | 9 |
| Journal | Neural Computing and Applications |
| Volume | 21 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2012 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
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Segmentation of multispectral MR images through an annealed rough neural network. / Chang, Yi Ying; Tai, Shen-Chuan; Lin, Jzau Sheng.
In: Neural Computing and Applications, Vol. 21, No. 5, 01.07.2012, p. 911-919.Research output: Contribution to journal › Article
TY - JOUR
T1 - Segmentation of multispectral MR images through an annealed rough neural network
AU - Chang, Yi Ying
AU - Tai, Shen-Chuan
AU - Lin, Jzau Sheng
PY - 2012/7/1
Y1 - 2012/7/1
N2 - In this paper, multispectral image segmentation using a rough neural network based on an annealed strategy with a cooling schedule is created. The main purpose is to embed an annealed cooling schedule into the rough neural network to construct a segmentation system named annealed rough neural net (ARNN). The classification system is a paradigm for the implementation of annealed reasoning and rough systems in neural network architecture. Instead of all the information in the image are fed into the neural network, the upper- and lower-bound gray level, captured from a training vector in a multispectral image, were fed into a rough neuron in the ARNN. Therefore, only 2-channel images are selected as the training samples if an N-dimensional multispectral image was used. In the simulation results, the proposed network not only reduces the consuming time but also reserves the classification performance.
AB - In this paper, multispectral image segmentation using a rough neural network based on an annealed strategy with a cooling schedule is created. The main purpose is to embed an annealed cooling schedule into the rough neural network to construct a segmentation system named annealed rough neural net (ARNN). The classification system is a paradigm for the implementation of annealed reasoning and rough systems in neural network architecture. Instead of all the information in the image are fed into the neural network, the upper- and lower-bound gray level, captured from a training vector in a multispectral image, were fed into a rough neuron in the ARNN. Therefore, only 2-channel images are selected as the training samples if an N-dimensional multispectral image was used. In the simulation results, the proposed network not only reduces the consuming time but also reserves the classification performance.
UR - http://www.scopus.com/inward/record.url?scp=84865616390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865616390&partnerID=8YFLogxK
U2 - 10.1007/s00521-011-0724-1
DO - 10.1007/s00521-011-0724-1
M3 - Article
VL - 21
SP - 911
EP - 919
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 5
ER -