Image Segmentation Method Using Thresholds Automatically Determined from Picture Contents.
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| Title: | Image Segmentation Method Using Thresholds Automatically Determined from Picture Contents. |
|---|---|
| Authors: | Yuan Been Chen1,2 ybchen@ctu.edu.tw, Chen, Oscal T.-C.1 |
| Source: | EURASIP Journal on Image & Video Processing. 2009, Vol. 2009, p1-15. 15p. 5 Black and White Photographs, 6 Diagrams, 3 Charts, 5 Graphs. |
| Subjects: | Automation, Selection theorems, Threshold logic, Digital signal processing, Markov random fields |
| Abstract: | Image segmentation has become an indispensable task in many image and video applications. This work develops an image segmentation method based on the modified edge-following scheme where different thresholds are automatically determined according to areas with varied contents in a picture, thus yielding suitable segmentation results in different areas. First, the iterative threshold selection technique is modified to calculate the initial-point threshold of the whole image or a particular block. Second, the quad-tree decomposition that starts from the whole image employs gray-level gradient characteristics of the currently-processed block to decide further decomposition or not. After the quad-tree decomposition, the initial-point threshold in each decomposed block is adopted to determine initial points. Additionally, the contour threshold is determined based on the histogram of gradients in each decomposed block. Particularly, contour thresholds could eliminate inappropriate contours to increase the accuracy of the search and minimize the required searching time. Finally, the edge-following method is modified and then conducted based on initial points and contour thresholds to find contours precisely and rapidly. By using the Berkeley segmentation data set with realistic images, the proposed method is demonstrated to take the least computational time for achieving fairly good segmentation performance in various image types. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 55261184 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Image Segmentation Method Using Thresholds Automatically Determined from Picture Contents. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yuan+Been+Chen%22">Yuan Been Chen</searchLink><relatesTo>1,2</relatesTo><i> ybchen@ctu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Oscal+T%2E-C%2E%22">Chen, Oscal T.-C.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Image+%26+Video+Processing%22">EURASIP Journal on Image & Video Processing</searchLink>. 2009, Vol. 2009, p1-15. 15p. 5 Black and White Photographs, 6 Diagrams, 3 Charts, 5 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Selection+theorems%22">Selection theorems</searchLink><br /><searchLink fieldCode="DE" term="%22Threshold+logic%22">Threshold logic</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+signal+processing%22">Digital signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+random+fields%22">Markov random fields</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Image segmentation has become an indispensable task in many image and video applications. This work develops an image segmentation method based on the modified edge-following scheme where different thresholds are automatically determined according to areas with varied contents in a picture, thus yielding suitable segmentation results in different areas. First, the iterative threshold selection technique is modified to calculate the initial-point threshold of the whole image or a particular block. Second, the quad-tree decomposition that starts from the whole image employs gray-level gradient characteristics of the currently-processed block to decide further decomposition or not. After the quad-tree decomposition, the initial-point threshold in each decomposed block is adopted to determine initial points. Additionally, the contour threshold is determined based on the histogram of gradients in each decomposed block. Particularly, contour thresholds could eliminate inappropriate contours to increase the accuracy of the search and minimize the required searching time. Finally, the edge-following method is modified and then conducted based on initial points and contour thresholds to find contours precisely and rapidly. By using the Berkeley segmentation data set with realistic images, the proposed method is demonstrated to take the least computational time for achieving fairly good segmentation performance in various image types. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/2009/140492 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Automation Type: general – SubjectFull: Selection theorems Type: general – SubjectFull: Threshold logic Type: general – SubjectFull: Digital signal processing Type: general – SubjectFull: Markov random fields Type: general Titles: – TitleFull: Image Segmentation Method Using Thresholds Automatically Determined from Picture Contents. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yuan Been Chen – PersonEntity: Name: NameFull: Chen, Oscal T.-C. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 16875176 Numbering: – Type: volume Value: 2009 Titles: – TitleFull: EURASIP Journal on Image & Video Processing Type: main |
| ResultId | 1 |
