Active Contours and Image Segmentation: The Current State Of the Art

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D. Baswaraj
D. Baswaraj
σ
Dr. A. Govardhan
Dr. A. Govardhan
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Dr. P. Premchand
Dr. P. Premchand
α Jawaharlal Nehru Technological University, Hyderabad

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Active Contours and Image Segmentation: The Current State Of the Art

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Abstract

Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

D. Baswaraj. 2012. \u201cActive Contours and Image Segmentation: The Current State Of the Art\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 12 (GJCST Volume 12 Issue F11): .

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GJCST Volume 12 Issue F11
Pg. 1- 12
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

Issue date

July 31, 2012

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en
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Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours.

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Active Contours and Image Segmentation: The Current State Of the Art

D. Baswaraj
D. Baswaraj Jawaharlal Nehru Technological University, Hyderabad
Dr. A. Govardhan
Dr. A. Govardhan
Dr. P. Premchand
Dr. P. Premchand

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