CLUSTER-BASED IMAGE SEGMENTATION USING FUZZY MARKOV RANDOM FIELD

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

RASOULI PEYMAN; MEYBODI MOHAMMAD REZA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF COMPUTER AND ROBOTICS Year:2016 | Volume:9 | Issue:2 Page(s): 1-9

Download Full-Text

View:

407

Download:

145

Cites:

Information Journal Paper

Title

CLUSTER-BASED IMAGE SEGMENTATION USING FUZZY MARKOV RANDOM FIELD

Author(s)

RASOULI PEYMAN | MEYBODI MOHAMMAD REZA | Issue Writer Certificate

Keywords

CLUSTERING

IMAGE SEGMENTATION

MARKOV RANDOM FIELD

FUZZY MARKOV RANDOM FIELD

CELLULAR LEARNING AUTOMATA

Abstract

Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. MARKOV RANDOM FIELD (MRF) is a tool for modeling statistical and structural information at the same time. Fuzzy MARKOV RANDOM FIELD (FMRF) is a MRF in fuzzy space which handles fuzziness and randomness of data simultaneously. This paper propose a new method called FMRFC which is model CLUSTERING using FMRF and applying it in application of IMAGE SEGMENTATION. Due to the similarity of FMRF model structure and image neighbourhood structure, exploiting FMRF in IMAGE SEGMENTATION makes results in acceptable levels. One of the important tools is CELLULAR LEARNING AUTOMATA (CLA) for suitable initial labelling of FMRF. The reason for choosing this tool is the similarity of CLA to FMRF and image structure. We compared the proposed method with several approaches such as Kmeans, FCM, and MRF and results demonstratably show the good performance of our method in terms of tanimoto, mean square error and energy minimization metrics.

Multimedia

No record.

Cites

No record.

References

COLOR IMAGE SEGMENTATION USING K-MEANS CLUSTERING AND OTSU’S ADAPTIVE THRESHOLDING

Cite

APA: Copy

RASOULI, PEYMAN, & MEYBODI, MOHAMMAD REZA. (2016). CLUSTER-BASED IMAGE SEGMENTATION USING FUZZY MARKOV RANDOM FIELD. JOURNAL OF COMPUTER AND ROBOTICS, 9(2), 1-9. SID. https://sid.ir/paper/685329/en

Vancouver: Copy

RASOULI PEYMAN, MEYBODI MOHAMMAD REZA. CLUSTER-BASED IMAGE SEGMENTATION USING FUZZY MARKOV RANDOM FIELD. JOURNAL OF COMPUTER AND ROBOTICS[Internet]. 2016;9(2):1-9. Available from: https://sid.ir/paper/685329/en

IEEE: Copy

PEYMAN RASOULI, and MOHAMMAD REZA MEYBODI, “CLUSTER-BASED IMAGE SEGMENTATION USING FUZZY MARKOV RANDOM FIELD,” JOURNAL OF COMPUTER AND ROBOTICS, vol. 9, no. 2, pp. 1–9, 2016, [Online]. Available: https://sid.ir/paper/685329/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops