Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
%0 Generic
%1 s2013fourth
%B An Automatic Medical Image Segmentation using Teaching Learning Based Optimization
%D 2013
%E S, Dr. Harish B
%E Das, Dr. Vinu V
%K Algorithm Genetic optimization portfolio
%T 2013 Fourth International Conference on Advances in Computer Science
%U http://searchdl.org/public/conference/2013/AETACS/99.pdf
%X Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
@conference{s2013fourth,
abstract = { Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.},
added-at = {2014-02-04T07:48:44.000+0100},
biburl = {https://www.bibsonomy.org/bibtex/2b6f2586e735ee204495721cd4093c491/idescitation},
booktitle = {An Automatic Medical Image Segmentation using Teaching Learning Based Optimization},
editor = {S, Dr. Harish B and Das, Dr. Vinu V},
interhash = {d0a9093dbf38877dab03dbd8e80a7e99},
intrahash = {b6f2586e735ee204495721cd4093c491},
keywords = {Algorithm Genetic optimization portfolio},
organization = {Institute of Doctors Engineers and Scientists},
timestamp = {2014-02-04T07:48:44.000+0100},
title = {2013 Fourth International Conference on Advances in Computer Science
},
url = {http://searchdl.org/public/conference/2013/AETACS/99.pdf},
year = 2013
}