Two State of Art Image Segmentation Approaches for Outdoor Scenes

1
Mr. Jenopaul.P
Mr. Jenopaul.P
2
Anju.J.A
Anju.J.A
1 PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu

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GJCST Volume 13 Issue F2

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Two State of Art Image Segmentation Approaches for Outdoor Scenes Banner
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The main research objective of this paper is to detecting object boundaries in outdoor scenes of images solely based on some general properties of the real world objects. Here, segmentation and recognition should not be separated and treated as an interleaving procedure. In this project, an adaptive global clustering technique is developed that can capture the non-accidental structural relationships among the constituent parts of the structured objects which usually consist of multiple constituent parts. The background objects such as sky, tree, ground etc. are also recognized based on the color and texture information. This process groups them together accordingly without depending on a priori knowledge of the specific objects. The proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases and on various outdoor natural scene environments, this improves the segmentation quality. By using this clustering technique is to overcome strong reflection and over segmentation. This proposed work shows better performance and improve background identification capability.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Mr. Jenopaul.P. 2013. \u201cTwo State of Art Image Segmentation Approaches for Outdoor Scenes\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 13 (GJCST Volume 13 Issue F2): .

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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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April 16, 2013

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English

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The main research objective of this paper is to detecting object boundaries in outdoor scenes of images solely based on some general properties of the real world objects. Here, segmentation and recognition should not be separated and treated as an interleaving procedure. In this project, an adaptive global clustering technique is developed that can capture the non-accidental structural relationships among the constituent parts of the structured objects which usually consist of multiple constituent parts. The background objects such as sky, tree, ground etc. are also recognized based on the color and texture information. This process groups them together accordingly without depending on a priori knowledge of the specific objects. The proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases and on various outdoor natural scene environments, this improves the segmentation quality. By using this clustering technique is to overcome strong reflection and over segmentation. This proposed work shows better performance and improve background identification capability.

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Two State of Art Image Segmentation Approaches for Outdoor Scenes

Anju.J.A
Anju.J.A
Mr. Jenopaul.P
Mr. Jenopaul.P PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu

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