DocumentCode :
3415377
Title :
PMM Based Segmentation of Gray-Scale Images
Author :
Chawla, Karandeep Singh ; Bora, P.K.
Author_Institution :
Dept. of Electron. & Commun. Eng., Indian Inst. of Technol. Guwahati, Guwahati, India
fYear :
2009
fDate :
18-20 Dec. 2009
Firstpage :
1
Lastpage :
4
Abstract :
This paper proposes a new mixture-model-based image segmentation method that uses the Pearson system of distribution for representing the model´s different mixture components, thus creating the Pearson mixture model (PMM). Generally, mixture models used for image segmentation assume the component distributions to be Gaussian in nature giving rise to the Gaussian Mixture Models (GMMs). This normality assumption, which in turn reduces the preciseness of the segmentation results, is absent in the PMM. The PMM prepared is subjected to a hard-clustered training. The results obtained on the application of the PMM to image segmentation are compared with the corresponding results obtained by running a GMM technique under similar training. The hard-clustered training approach is adopted as, along with being simpler in application, the computational time taken for calculating the parameters is much less than the corresponding time taken by the soft-clustered training approach.
Keywords :
image segmentation; Pearson distribution system; Pearson mixture model; gray-scale image segmentation; hard-clustered training; soft-clustered training; Analysis of variance; Computer applications; Differential equations; Gaussian distribution; Gray-scale; Image analysis; Image segmentation; Paper technology; Performance analysis; Shape control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
India Conference (INDICON), 2009 Annual IEEE
Conference_Location :
Gujarat
Print_ISBN :
978-1-4244-4858-6
Electronic_ISBN :
978-1-4244-4859-3
Type :
conf
DOI :
10.1109/INDCON.2009.5409450
Filename :
5409450
Link To Document :
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